Query: coronavirus public datasets [SEP] What new public datasets are available related to COVID-19? [SEP] Seeking articles that specifically release new data related to SARS-CoV-2 or COVID-19, including genomic data, patient data, public health data, etc. Articles that reference previously existing datasets are not relevant.

title: An outbreak of COVID‐19 caused by a new coronavirus: what we know so far

title: SKP2 attenuates autophagy through Beclin1-ubiquitination and its inhibition reduces MERS-Coronavirus infection
abstract: Autophagy is an essential cellular process affecting virus infections and other diseases and Beclin1 (BECN1) is one of its key regulators. Here, we identified S-phase kinase-associated protein 2 (SKP2) as E3 ligase that executes lysine-48-linked poly-ubiquitination of BECN1, thus promoting its proteasomal degradation. SKP2 activity is regulated by phosphorylation in a hetero-complex involving FKBP51, PHLPP, AKT1, and BECN1. Genetic or pharmacological inhibition of SKP2 decreases BECN1 ubiquitination, decreases BECN1 degradation and enhances autophagic flux. Middle East respiratory syndrome coronavirus (MERS-CoV) multiplication results in reduced BECN1 levels and blocks the fusion of autophagosomes and lysosomes. Inhibitors of SKP2 not only enhance autophagy but also reduce the replication of MERS-CoV up to 28,000-fold. The SKP2-BECN1 link constitutes a promising target for host-directed antiviral drugs and possibly other autophagy-sensitive conditions.

title: COVIDiagnosis-Net: Deep Bayes-SqueezeNet based Diagnostic of the Coronavirus Disease 2019 (COVID-19) from X-Ray Images
abstract: The COVID-19 outbreak has a tremendous impact on global health and the daily life of people still living in more than two hundred countries. The crucial action to gain the force in the fight of COVID-19 is to have powerful monitoring of the site forming infected patients. Most of the initial tests rely on detecting the genetic material of the coronavirus, and they have a poor detection rate with the time-consuming operation. In the ongoing process, radiological imaging is also preferred where chest X-rays are highlighted in the diagnosis. Early studies express the patients with an abnormality in chest X-rays pointing to the presence of the COVID-19. On this motivation, there are several studies cover the deep learning-based solutions to detect the COVID-19 using chest X-rays. A part of the existing studies use non-public datasets, others perform on complicated Artificial Intelligent (AI) structures. In our study, we demonstrate an AI-based structure to outperform the existing studies. The SqueezeNet that comes forward with its light network design is tuned for the COVID-19 diagnosis with Bayesian optimization additive. Fine-tuned hyperparameters and augmented dataset make the proposed network perform much better than existing network designs and to obtain a higher COVID-19 diagnosis accuracy.

title: CovidCounties - an interactive, real-time tracker of the COVID-19 pandemic at the level of US counties
abstract: Management of the COVID-19 pandemic has proven to be a significant challenge to policy makers. This is in large part due to uneven reporting and the absence of open-access visualization tools to present local trends and infer healthcare needs. Here we report the development of CovidCounties.org, an interactive web application that depicts daily disease trends at the level of US counties using time series plots and maps. This application is accompanied by a manually curated dataset that catalogs all major public policy actions made at the state-level, as well as technical validation of the primary data. Finally, the underlying code for the site is also provided as open source, enabling others to validate and learn from this work.

title: MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
abstract: The rapid spread of the new pandemic, coronavirus disease 2019 (COVID-19), has seriously threatened global health. The gold standard for COVID-19 diagnosis is the tried-and-true polymerase chain reaction (PCR), but PCR is a laborious, time-consuming and complicated manual process that is in short supply. Deep learning based computer-aided screening, e.g., infection segmentation, is thus viewed as an alternative due to its great successes in medical imaging. However, the publicly available COVID-19 training data are limited, which would easily cause overfitting of traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also important for quick deployment and development of computer-aided COVID-19 screening systems, but traditional deep learning methods, especially for image segmentation, are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 472K parameters and is thus not easy to overfit; ii) it has high computational efficiency and is thus convenient for practical deployment; iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg with traditional methods. Code and models will be released to promote the research and practical deployment for computer-aided COVID-19 screening.

title: Using X-ray images and deep learning for automated detection of coronavirus disease
abstract: Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201). Communicated by Ramaswamy H. Sarma

title: A Machine Learning Model Reveals Older Age and Delayed Hospitalization as Predictors of Mortality in Patients with COVID-19
abstract: The recent pandemic of novel coronavirus disease 2019 (COVID-19) is increasingly causing severe acute respiratory syndrome (SARS) and significant mortality. We aim here to identify the risk factors associated with mortality of coronavirus infected persons using a supervised machine learning approach.  Clinical data of 1085 cases of COVID-19 from 13th January to 28th February, 2020 was obtained from Kaggle, an online community of Data scientists. 430 cases were selected for the final analysis. Random Forest classification algorithm was implemented on the dataset to identify the important predictors and their effects on mortality.  The Area under the ROC curve obtained during model validation on the test dataset was 0.97. Age was the most important variable in predicting mortality followed by the time gap between symptom onset and hospitalization.  Patients aged beyond 62 years are at higher risk of fatality whereas hospitalization within 2 days of the onset of symptoms could reduce mortality in COVID-19 patients.

title: MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset
abstract: This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia.  https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License.  artificial intelligence, COVID-19, machine learning, dataset, CT, chest, imaging

title: Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients
abstract: Circulating in China and 158 other countries and areas, the ongoing COVID-19 outbreak has caused devastating mortality and posed a great threat to public health. However, efforts to identify effectively supportive therapeutic drugs and treatments has been hampered by our limited understanding of host immune response for this fatal disease. To characterize the transcriptional signatures of host inflammatory response to SARS-CoV-2 (HCoV-19) infection, we carried out transcriptome sequencing of the RNAs isolated from the bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) specimens of COVID-19 patients. Our results reveal distinct host inflammatory cytokine profiles to SARS-CoV-2 infection in patients, and highlight the association between COVID-19 pathogenesis and excessive cytokine release such as CCL2/MCP-1, CXCL10/IP-10, CCL3/MIP-1A, and CCL4/MIP1B. Furthermore, SARS-CoV-2 induced activation of apoptosis and P53 signalling pathway in lymphocytes may be the cause of patients’ lymphopenia. The transcriptome dataset of COVID-19 patients would be a valuable resource for clinical guidance on anti-inflammatory medication and understanding the molecular mechansims of host response.

title: COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms
abstract: The catastrophic outbreak of Severe Acute Respiratory Syndrome - Coronavirus (SARS-CoV-2) also known as COVID-2019 has brought the worldwide threat to the living society. The whole world is putting incredible efforts to fight against the spread of this deadly disease in terms of infrastructure, finance, data sources, protective gears, life-risk treatments and several other resources. The artificial intelligence researchers are focusing their expertise knowledge to develop mathematical models for analyzing this epidemic situation using nationwide shared data. To contribute towards the well-being of living society, this article proposes to utilize the machine learning and deep learning models with the aim for understanding its everyday exponential behaviour along with the prediction of future reachability of the COVID-2019 across the nations by utilizing the real-time information from the Johns Hopkins dashboard.

title: Spatial-Temporal Dataset of COVID-19 Outbreak in China
abstract: We present Coronavirus disease 2019 (COVID-19) statistics in China dataset: daily statistics of the COVID-19 outbreak in China at the city/county level. For each city/country, we include the six most important numbers for epidemic research: daily new infections, accumulated infections, daily new recoveries, accumulated recoveries, daily new deaths, and accumulated deaths. We cross validate the dataset and the estimate error rate is about 0.04%. We then give several examples to show how to trace the spreading in particular cities or provinces, and also contrast the development of COVID-19 in all cities in China at the early, middle and late stages. We hope this dataset can help researchers around the world better understand the spreading dynamics of COVID-19 at a regional level, to inform intervention and mitigation strategies for policymakers.

title: The Challenge of Using Epidemiological Case Count Data: The Example of Confirmed COVID-19 Cases and the Weather
abstract: The publicly available data on COVID-19 cases provides an opportunity to better understand this new disease. However, strong attention needs to be paid to the limitations of the data to avoid making inaccurate conclusions. This article, which focuses on the relationship between the weather and COVID-19, raises the concern that the same factors influencing the spread of the disease might also affect the number of tests performed and who gets tested. For example, weather conditions impact the prevalence of respiratory diseases with symptoms similar to COVID-19, and this will likely influence the number of tests performed. This general limitation could severely undermine any similar analysis using existing COVID-19 data or similar epidemiological data, which could, therefore, mislead decision-makers on questions of great policy relevance.

title: VIGOR, an annotation program for small viral genomes
abstract: The decrease in cost for sequencing and improvement in technologies has made it easier and more common for the re-sequencing of large genomes as well as parallel sequencing of small genomes. It is possible to completely sequence a small genome within days and this increases the number of publicly available genomes. Among the types of genomes being rapidly sequenced are those of microbial and viral genomes responsible for infectious diseases. However, accurate gene prediction is a challenge that persists for decoding a newly sequenced genome. Therefore, accurate and efficient gene prediction programs are highly desired for rapid and cost effective surveillance of RNA viruses through full genome sequencing.  We have developed VIGOR (Viral Genome ORF Reader), a web application tool for gene prediction in influenza virus, rotavirus, rhinovirus and coronavirus subtypes. VIGOR detects protein coding regions based on sequence similarity searches and can accurately detect genome specific features such as frame shifts, overlapping genes, embedded genes, and can predict mature peptides within the context of a single polypeptide open reading frame. Genotyping capability for influenza and rotavirus is built into the program. We compared VIGOR to previously described gene prediction programs, ZCURVE_V, GeneMarkS and FLAN. The specificity and sensitivity of VIGOR are greater than 99% for the RNA viral genomes tested.  VIGOR is a user friendly web-based genome annotation program for five different viral agents, influenza, rotavirus, rhinovirus, coronavirus and SARS coronavirus. This is the first gene prediction program for rotavirus and rhinovirus for public access. VIGOR is able to accurately predict protein coding genes for the above five viral types and has the capability to assign function to the predicted open reading frames and genotype influenza virus. The prediction software was designed for performing high throughput annotation and closure validation in a post-sequencing production pipeline.

title: Role of Chloroquine and Hydroxychloroquine in the Treatment of COVID-19 Infection- A Systematic Literature Review
abstract: Coronavirus pandemic is currently a global public health emergency. At present, no pharmacological treatment is known to treat this condition, and there is a need to review the available treatments.  While there have been studies to describe the role of chloroquine and hydroxychloroquine in various viral conditions, there is limited information about the use of them in COVID-19. This systematic review aims to summarize the available evidence regarding the role of chloroquine in treating coronavirus infection.  The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were used for this review. A literature search was performed using PUBMED & Google Scholar to find articles about the role of CQ in COVID-19 patients.  We included 19 publications (Five published articles, three letters/correspondence, one commentary, five pre-proofs of accepted articles, one abstract of yet to be published article, and four were pre-prints (not yet peer-reviewed) articles) in this systematic review. All the articles mentioned about the role of chloroquine and /or hydroxychloroquine in limiting the infection with SARS-CoV-2 (the virus causing COVID-19).  There is theoretical, experimental, preclinical and clinical evidence of the effectiveness of chloroquine in patients affected with COVID-19. There is adequate evidence of drug safety from the long-time clinical use of chloroquine and hydroxychloroquine in other indications. More data from ongoing and future trials will add more insight into the role of chloroquine and hydroxychloroquine in COVID-19 infection.

title: Evidence from internet search data shows information-seeking responses to news of local COVID-19 cases
abstract: The COVID-19 outbreak is a global pandemic with community circulation in many countries, including the United States, with confirmed cases in all states. The course of this pandemic will be shaped by how governments enact timely policies and disseminate information and by how the public reacts to policies and information. Here, we examine information-seeking responses to the first COVID-19 case public announcement in a state. Using an event study framework for all US states, we show that such news increases collective attention to the crisis right away. However, the elevated level of attention is short-lived, even though the initial announcements are followed by increasingly strong policy measures. Specifically, searches for “coronavirus” increased by about 36% (95% CI: 27 to 44%) on the day immediately after the first case announcement but decreased back to the baseline level in less than a week or two. We find that people respond to the first report of COVID-19 in their state by immediately seeking information about COVID-19, as measured by searches for coronavirus, coronavirus symptoms, and hand sanitizer. On the other hand, searches for information regarding community-level policies (e.g., quarantine, school closures, testing) or personal health strategies (e.g., masks, grocery delivery, over-the-counter medications) do not appear to be immediately triggered by first reports. These results are representative of the study period being relatively early in the epidemic, and more-elaborate policy responses were not yet part of the public discourse. Further analysis should track evolving patterns of responses to subsequent flows of public information.

title: Potential of age distribution profiles for the prediction of COVID-19 infection origin in a patient group
abstract: The COVID-19 pandemic is a serious and global public health concern. It is now well known that COVID-19 cases may result in mild symptoms leading to patient recovery. However, severity of infection, fatality rates, and treatment responses across different countries, age groups, and demographic groups suggest that the nature of infection is diverse, and a timely investigation of the same is needed for evolving sound treatment and preventive strategies. This paper reports an the analysis of age distribution patterns in six groups of Indian COVID-19 patient populations based on their likely geographical origin of infection viz. the United Kingdom, North America, the European Union, the Middle East, and Asian countries. It was observed that patient groups stratified in this way had a distinct age profile and that some of these groups e.g. patient groups from Asia, the European Union, and the United Kingdom formed a different cluster than those from North America, the Middle East, and other regions. Patient age profiles of a population were found to be highly predictive of the group they belong to, and there are indications of their distinct recovery and fatality rates across gender. Altogether this study provides a scalable framework to estimate the source of infection in a new population of COVID-19 patients with unknown origin. It is also concluded that greater public availability of age and other demographic profile details of patients may be helpful in gaining robust insights into COVID-19 infection origins. Datasets and scripts used in this work are shared at http://covid.sciwhylab.org.

title: Genetic diversity and evolution of SARS-CoV-2
abstract: Abstract COVID-19 is a viral respiratory illness caused by a new coronavirus called SARS-CoV-2. The World Health Organization declared the SARS-CoV-2 outbreak a global public health emergency. We performed genetic analyses of eighty-six complete or near-complete genomes of SARS-CoV-2 and revealed many mutations and deletions on coding and non-coding regions. These observations provided evidence of the genetic diversity and rapid evolution of this novel coronavirus.

title: Title: The Emergency Medicine Facing the Challenge of Open Science
abstract: The availability of research datasets can strengthen and facilitate research processes This is specifically relevant in the emergency medicine field due to the importance of providing immediate care in critical situations as the very current Coronavirus (COVID-19) Pandemic is showing to the scientific community This work aims to show which Emergency Medicine journals indexed in Journal Citation Reports (JCR) currently meet data sharing criteria (2) Methods: This study analyzes the editorial policies regarding the data deposit of the journals in the emergency medicine category of the JCR and evaluates the Supplementary material of the articles published in these journals that have been deposited in the PubMed Central repository (3)  It has been observed that 19 out of the 24 journals contained in the emergency medicine category of Journal Citation Reports are also located in PubMed Central (PMC), yielding a total of 5983 articles Out of these, only 9 4% of the articles contain supplemental material Although second quartile journals of JCR emergency medicine category have quantitatively more articles in PMC, the main journals involved in the deposit of supplemental material belong to the first quartile, of which the most used format in the articles is pdf, followed by text documents (4)  This study reveals that data sharing remains an incipient practice in the emergency medicine field, as there are still barriers between researchers to participate in data sharing Therefore, it is necessary to promote dynamics to improve this practice both qualitatively (the quality and format of datasets) and quantitatively (the quantity of datasets in absolute terms) in research

title: Immune and Metabolic Signatures of COVID-19 Revealed by Transcriptomics Data Reuse
abstract: The current pandemic of coronavirus disease 19 (COVID-19) has affected millions of individuals and caused thousands of deaths worldwide. The pathophysiology of the disease is complex and mostly unknown. Therefore, identifying the molecular mechanisms that promote progression of the disease is critical to overcome this pandemic. To address such issues, recent studies have reported transcriptomic profiles of cells, tissues and fluids from COVID-19 patients that mainly demonstrated activation of humoral immunity, dysregulated type I and III interferon expression, intense innate immune responses and inflammatory signaling. Here, we provide novel perspectives on the pathophysiology of COVID-19 using robust functional approaches to analyze public transcriptome datasets. In addition, we compared the transcriptional signature of COVID-19 patients with individuals infected with SARS-CoV-1 and Influenza A (IAV) viruses. We identified a core transcriptional signature induced by the respiratory viruses in peripheral leukocytes, whereas the absence of significant type I interferon/antiviral responses characterized SARS-CoV-2 infection. We also identified the higher expression of genes involved in metabolic pathways including heme biosynthesis, oxidative phosphorylation and tryptophan metabolism. A BTM-driven meta-analysis of bronchoalveolar lavage fluid (BALF) from COVID-19 patients showed significant enrichment for neutrophils and chemokines, which were also significant in data from lung tissue of one deceased COVID-19 patient. Importantly, our results indicate higher expression of genes related to oxidative phosphorylation both in peripheral mononuclear leukocytes and BALF, suggesting a critical role for mitochondrial activity during SARS-CoV-2 infection. Collectively, these data point for immunopathological features and targets that can be therapeutically exploited to control COVID-19.

title: Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron
abstract: Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical, modeling can provide satisfactory models, it can also fail to comprehend the intricacies contained within the data. In this paper, authors use a publicly available dataset, containing information on infected, recovered, and deceased patients in 406 locations over 51 days (22nd January 2020 to 12th March 2020). This dataset, intended to be a time-series dataset, is transformed into a regression dataset and used in training a multilayer perceptron (MLP) artificial neural network (ANN). The aim of training is to achieve a worldwide model of the maximal number of patients across all locations in each time unit. Hyperparameters of the MLP are varied using a grid search algorithm, with a total of 5376 hyperparameter combinations. Using those combinations, a total of 48384 ANNs are trained (16128 for each patient group-deceased, recovered, and infected), and each model is evaluated using the coefficient of determination (R2). Cross-validation is performed using K-fold algorithm with 5-folds. Best models achieved consists of 4 hidden layers with 4 neurons in each of those layers, and use a ReLU activation function, with R2 scores of 0.98599 for confirmed, 0.99429 for deceased, and 0.97941 for recovered patient models. When cross-validation is performed, these scores drop to 0.94 for confirmed, 0.781 for recovered, and 0.986 for deceased patient models, showing high robustness of the deceased patient model, good robustness for confirmed, and low robustness for recovered patient model.

title: Hydroxychloroquine for the management of COVID-19: Hope or Hype? A Systematic review of the current evidence
abstract: The COVID-19 Pandemic has literally left the world breathless in the chase for Pharmacotherapy. With the vaccine approval likely more than a year away and novel drugs in early clinical trials, repurposing of existing drugs takes the center stage. A potential drug discussed both in geopolitical and global scientific community is hydroxychloroquine (HCQ). We intend to systematically weigh and analyze the existing evidence of HCQ in the light of published and pre-print data available so far.  PubMed Ovid MEDLINE, EMBASE, Google scholar databases and official clinical trial Registries of the United States, China, WHO ICTRP were electronically searched for studies for the use of HCQ in patients with COVID-19. Pre-proof article repositories like MedRxiv, BioRxiv, and ChemRxiv were also included in the search. The literature was critically appraised.  Total 71 articles were available as of 15 th April of which articles of relevance (three invitro studies, two open label non-randomized trials, two open label randomized control trials, one follow-up study, three reviews, ten short communications) and 88 clinical trials registered in three clinical trial registries were analyzed. HCQ seems to be efficient in inhibiting of SARS CoV-2 in in-vitro cell lines; there is lack of strong evidence from human studies.  The in-vitro cell culture based data of viral inhibition does not suffice for the use of hydroxychloroquine in the patients with COVID-19. Currently literature shows inadequate, low level evidence in human studies. Scarcity of safety and efficacy data warrants medical communities, health care agencies and governments across the world against the widespread use of HCQ in COVID-19 prophylaxis and treatment, until robust evidence becomes available.  Hydroxychloroquine, SARS-CoV-2, COVID-19, Corona virus, nCov2, systematic review

title: Significant expression of FURIN and ACE2 on oral epithelial cells may facilitate the efficiency of SARS-CoV-2 entry
abstract: Leading to a sustained epidemic spread with >2,000,000 confirmed human infections, including >100,000 deaths, COVID-19 was caused by SARS-CoV-2 and resulted in acute respiratory distress syndrome (ARDS) and sepsis, which brought more challenges to the patient’s treatment. The S-glycoprotein, which recognized as the key factor for the entry of SARS-CoV-2 into the cell, contains two functional domains: an ACE2 receptor binding domain and a second domain necessary for fusion of the coronavirus and cell membranes. FURIN activity, exposes the binding and fusion domains, is essential for the zoonotic transmission of SARS-CoV-2. Moreover, it has been reported that ACE2 is likely to be the receptor for SARS-CoV-2. In addition, FURIN enzyme and ACE2 receptor were expressed in airway epithelia, cardiac tissue, and enteric canals, which considered as the potential target organ of the virus. However, report about the expression of FURIN and ACE2 in oral tissues was limited.  In order to investigate the potential infective channel of new coronavirus in oral cavity, we analyze the expression of ACE2 and FURIN that mediate the new coronavirus entry into host cells in oral mucosa using the public single-cell sequence datasets. Furthermore, immunohistochemical staining experiment was performed to confirm the expression of ACE2 and FURIN in the protein level.  The bioinformatics results indicated the differential expression of ACE2 and FURIN on epithelial cells of different oral mucosal tissues and the proportion of FURIN-positive cells was obviously higher than that of ACE2-positive cells. IHC experiments revealed that both the ACE2-positive and FURIN-positive cells in the target tissues were mainly positioned in the epithelial layers, partly expressed in fibroblasts, which further confirm the bioinformatics results.  Based on these findings, we speculated that SARS-CoV-2 could effectively invade oral mucosal cells though two possible routes: binding to the ACE2 receptor and fusion with cell membrane activated by FURIN protease. Our results indicated that oral mucosa tissues are susceptible to SARS-CoV-2, which provides valuable information for virus-prevention strategy in clinical care as well as daily life.

title: Oral presentations

title: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges
abstract: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; previously provisionally named 2019 novel coronavirus or 2019-nCoV) disease (COVID-19) in China at the end of 2019 has caused a large global outbreak and is a major public health issue. As of 11 February 2020, data from the World Health Organization (WHO) have shown that more than 43 000 confirmed cases have been identified in 28 countries/regions, with >99% of cases being detected in China. On 30 January 2020, the WHO declared COVID-19 as the sixth public health emergency of international concern. SARS-CoV-2 is closely related to two bat-derived severe acute respiratory syndrome-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21. It is spread by human-to-human transmission via droplets or direct contact, and infection has been estimated to have mean incubation period of 6.4 days and a basic reproduction number of 2.24–3.58. Among patients with pneumonia caused by SARS-CoV-2 (novel coronavirus pneumonia or Wuhan pneumonia), fever was the most common symptom, followed by cough. Bilateral lung involvement with ground-glass opacity was the most common finding from computed tomography images of the chest. The one case of SARS-CoV-2 pneumonia in the USA is responding well to remdesivir, which is now undergoing a clinical trial in China. Currently, controlling infection to prevent the spread of SARS-CoV-2 is the primary intervention being used. However, public health authorities should keep monitoring the situation closely, as the more we can learn about this novel virus and its associated outbreak, the better we can respond.

title: Serial interval in determining the estimation of reproduction number of the novel coronavirus disease (COVID-19) during the early outbreak ;Journal of Travel Medicine ;Oxford Academic
abstract: Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China in the end of 2019, and soon spread oversea A comprehensive and timely review summarized the scientific research in estimating the basic reproduction number (R0) released from 1 January to 7 February 2020  During the early outbreak, when the key epidemiological features of COVID-19 were uncovered, the R0 estimation largely relied on the growth rate of the epidemic curve and the estimation of the serial interval (SI) Here, we demonstrated that an overlarge SI would lead to overestimation of R0 We adopted the growing process proposed in  deterministically with a population of 11 million in Wuhan, 1 case initially onset on 5 December 2019 and a fixed step at 1 day We consider two values of the mean serial interval (SI) that are  SI at 4 6 days estimated based on 28 records of transmission chains , which was largely consistent with the SI estimate at 4 4 days based on 71 records [4];and  SI at 8 days, which was closer to the SI of the severe acute respiratory syndrome (SARS, 8 4 days), SI of the Middle East respiratory syndrome (MERS, 7 6 days) As for demonstration that a larger SI could lead to overestimation in R0, we conducted the simulation with two schemes that are  Scheme (I): R0 = 2, and SI = 4 6 days;and  Scheme (II): R0 = 2, 3, 4 and 3 3 as summarized in , and SI = 8 days We also compared the simulation results with the previous estimates of the cumulative number of COVID-19 infection in Wuhan In Fig 1, the simulation results of the scheme (I) had almost the same growing trends as those of scheme (II) with R0 = 3 3 Although a higher R0 could force the epidemic curve increasing rapidly, a shorter SI could increase iteration of transmission generation, i e , transmission may occur shortly post infection According to the simple approximated formula that R0 = exp(γ∙SI), where γ was the exponential growth rate calculated from the incidence data directly, a longer SI would lead to a higher R0 estimate theoretically With a shorter SI at 4 6 days, which was supported by richer datasets in [3, 4], the R0 of COVID-19 could be lower than previous estimates based on longer SI By using the growth rate (γ) at 0 15 per day, the R0 was found at 2 0 with SI at 4 6 days, whereas 3 3 with SI at 8 days Although the effects of public health control were ignored in this analysis, our model could be Downloaded from https://academic oup com/jtm/advance-article-abstract/doi/10 1093/jtm/taaa033/5803291 by guest on 13 March 2020 4 extended by introducing an effective reproduction number accounting for the effectiveness of the control measures, and we remarked this modification would not affect the main conclusion Furthermore, as pointed out in , provided that the SI of COVID-19 might be shorter than its incubation period, pre-symptomatic transmission may occur shortly after being infected  This implies that a fraction of transmissions cannot be prevented solely through isolating the symptomatic cases, since the time when contact tracing is conducted, they may have already been infectious and generated secondary cases Therefore, the effectively quarantine of suspected (and probable) cases, as well as close contacts, and timely contact tracing were crucial in successful outbreak mitigation

title: Insights on early mutational events in SARS-CoV-2 virus reveal founder effects across geographical regions
abstract: Here we aim to describe early mutational events across samples from publicly available SARS-CoV-2 sequences from the sequence read archive and GenBank repositories. Up until 27 March 2020, we downloaded 50 illumina datasets, mostly from China, USA (WA State) and Australia (VIC). A total of 30 datasets (60%) contain at least a single founder mutation and most of the variants are missense (over 63%). Five-point mutations with clonal (founder) effect were found in USA next-generation sequencing samples. Sequencing samples from North America in GenBank (22 April 2020) present this signature with up to 39% allele frequencies among samples (n = 1,359). Australian variant signatures were more diverse than USA samples, but still, clonal events were found in these samples. Mutations in the helicase, encoded by the ORF1ab gene in SARS-CoV-2 were predominant, among others, suggesting that these regions are actively evolving. Finally, we firmly urge that primer sets for diagnosis be carefully designed, since rapidly occurring variants would affect the performance of the reverse transcribed quantitative PCR (RT-qPCR) based viral testing.

title: Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic
abstract: The Coronavirus pandemic has taken the world by storm as also the social media. As the awareness about the ailment increased, so did messages, videos and posts acknowledging its presence. The social networking site, Twitter, demonstrated similar effect with the number of posts related to coronavirus showing an unprecedented growth in a very short span of time. This paper presents a statistical analysis of the twitter messages related to this disease posted since January 2020. Two types of empirical studies have been performed. The first is on word frequency and the second on sentiments of the individual tweet messages. Inspection of the word frequency is useful in characterizing the patterns or trends in the words used on the site. This would also reflect on the psychology of the twitter users at this critical juncture. Unigram, bigram and trigram frequencies have been modeled by power law distribution. The results have been validated by Sum of Square Error (SSE), R2 and Root Mean Square Error (RMSE). High values of R2 and low values of SSE and RMSE lay the grounds for the goodness of fit of this model. Sentiment analysis has been conducted to understand the general attitudes of the twitter users at this time. Both tweets by general public and WHO were part of the corpus. The results showed that the majority of the tweets had a positive polarity and only about 15% were negative.

title: Practical recommendations for the management of diabetes in patients with COVID-19
abstract: Diabetes is one of the most important comorbidities linked to the severity of all three known human pathogenic coronavirus infections, including severe acute respiratory syndrome coronavirus 2. Patients with diabetes have an increased risk of severe complications including Adult Respiratory Distress Syndrome and multi-organ failure. Depending on the global region, 20–50% of patients in the coronavirus disease 2019 (COVID-19) pandemic had diabetes. Given the importance of the link between COVID-19 and diabetes, we have formed an international panel of experts in the field of diabetes and endocrinology to provide some guidance and practical recommendations for the management of diabetes during the pandemic. We aim to briefly provide insight into potential mechanistic links between the novel coronavirus infection and diabetes, present practical management recommendations, and elaborate on the differential needs of several patient groups.

title: Trends and Prediction in Daily New Cases and Deaths of COVID-19 in the United States: An Internet Search-Interest Based Model
abstract: The daily incidence and deaths of coronavirus disease 2019 (COVID-19) in the USA are poorly understood. Internet search interest was found to be correlated with COVID-19 daily incidence in China, but has not yet been applied to the USA. Therefore, we examined the association of internet search-interest with COVID-19 daily incidence and deaths in the USA.  We extracted COVID-19 daily new cases and deaths in the USA from two population-based datasets, namely 1-point-3-acres.com and the Johns Hopkins COVID-19 data repository. The internet search-interest of COVID-19-related terms was obtained using Google Trends. The Pearson correlation test and general linear model were used to examine correlations and predict trends, respectively.  There were 636,282 new cases and,325 deaths of COVID-19 in the USA from March 1 to April 15, 2020, with a crude mortality of 4.45%. The daily new cases peaked at 35,098 cases on April 10, 2020 and the daily deaths peaked at 2,494 on April 15, 2020. The search interest of COVID, “COVID pneumonia” and “COVID heart” were correlated with COVID-19 daily incidence, with 12 or 14 days of delay (Pearson’s r = 0.978, 0.978 and 0.979, respectively) and deaths with 19 days of delay (Pearson’s r = 0.963, 0.958 and 0.970, respectively). The 7-day follow-up with prospectively collected data showed no significant correlations of the observed data with the predicted daily new cases or daily deaths, using search interest of COVID, COVID heart, and COVID pneumonia.  Search terms related to COVID-19 are highly correlated with the COVID-19 daily new cases and deaths in the USA.

title: Emerging Viruses without Borders: The Wuhan Coronavirus
abstract: The recently emerged coronavirus in Wuhan, China has claimed at least six lives as of January 22 and infected hundreds if not thousands of individuals. The situation has drawn international attention, including from the virology community. We applaud the rapid release to the public of the genome sequence of the new virus by Chinese virologists, but we also believe that increased transparency on disease reporting and data sharing with international colleagues are crucial for curbing the spread of this newly emerging virus to other parts of the world.

title: MaskIt: Masking for efficient utilization of incomplete public datasets for training deep learning models
abstract: A major challenge in training deep learning models is the lack of high quality and complete datasets. In the paper, we present a masking approach for training deep learning models from a publicly available but incomplete dataset. For example, city of Hamburg, Germany maintains a list of trees along the roads, but this dataset does not contain any information about trees in private homes and parks. To train a deep learning model on such a dataset, we mask the street trees and aerial images with the road network. Road network used for creating the mask is downloaded from OpenStreetMap, and it marks the area where the training data is available. The mask is passed to the model as one of the inputs and it also coats the output. Our model learns to successfully predict trees only in the masked region with 78.4% accuracy.

title: Increased travel times to United States SARS-CoV-2 testing sites: a spatial modeling study
abstract: Access to testing is key to a successful response to the COVID-19 pandemic.  To determine the geographic accessibility to SARS-CoV-2 testing sites in the United States, as quantified by travel time.  2020 in relation to travel time.  United States COVID-19 pandemic.  The United States, including the 48 contiguous states and the District of Columbia.  Population density, percent minority, percent uninsured, and median income by county from the 2018 American Community Survey demographic data.  SARS-CoV-2 testing sites identified in two national databases (Carbon Health and CodersAgainstCovid), geocoded by address. Median county 1 km2 gridded friction surface of travel times, as a measure of geographic accessibility to SARS-CoV-2 testing sites.  6,236 unique SARS-CoV-2 testing sites in 3,108 United States counties were identified. Thirty percent of the U.S. population live in a county (N = 1,920) with a median travel time over 20 minutes. This was geographically heterogeneous; 86% of the Mountain division population versus 5% of the Middle Atlantic population lived in counties with median travel times over 20 min. Generalized Linear Models showed population density, percent minority, percent uninsured and median income were predictors of median travel time to testing sites. For example, higher percent uninsured was associated with longer travel time (B= 0.41 min/percent, 95% confidence interval 0.3-0.53, p = 1.2x10-12), adjusting for population density.  Geographic accessibility to SARS-Cov-2 testing sites is reduced in counties with lower population density and higher percent of minority and uninsured, which are also risk factors for worse healthcare access and outcomes. Geographic barriers to SARS-Cov-2 testing may exacerbate health inequalities and bias county-specific transmission estimates. Geographic accessibility should be considered when planning the location of future testing sites and interpreting epidemiological data.

title: Novel COronaVirus Disease 2019 (COVID-19) epidemic: What are the risks for systemic sclerosis patients?

title: Preprints: An underutilized mechanism to accelerate outbreak science
abstract: In an Essay, Michael Johansson and colleagues advocate the posting of research studies addressing infectious disease outbreaks as preprints.

title: ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research
abstract: First identified in Wuhan, China, in December 2019, the outbreak of COVID-19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO). Along with this pandemic, we are also experiencing an"infodemic"of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate the studies of combating such information regarding COVID-19. We first broadly search and investigate ~2,000 news publishers, from which 61 are identified with extreme [high or low] levels of credibility. By inheriting the credibility of the media on which they were published, a total of 2,029 news articles on coronavirus, published from January to May 2020, are collected in the repository, along with 140,820 tweets that reveal how these news articles are spread on the social network. The repository provides multimodal information of news articles on coronavirus, including textual, visual, temporal, and network information. The way that news credibility is obtained allows a trade-off between dataset scalability and label accuracy. Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be directly compared. Our repository is available at http://coronavirus-fakenews.com, which will be timely updated.

title: Using ILI surveillance to estimate state-specific case detection rates and forecast SARS-CoV-2 spread in the United States
abstract: Detection of SARS-CoV-2 infections to date has relied on RT-PCR testing. However, a failure to identify early cases imported to a country, bottlenecks in RT-PCR testing, and the existence of infections which are asymptomatic, sub-clinical, or with an alternative presentation than the standard cough and fever have resulted in an under-counting of the true prevalence of SARS-CoV-2. Here, we show how publicly available CDC influenza-like illness (ILI) outpatient surveillance data can be repurposed to estimate the detection rate of symptomatic SARS-CoV-2 infections. We find a surge of non-influenza ILI above the seasonal average and show that this surge is correlated with COVID case counts across states. By quantifying the number of excess ILI patients in March relative to previous years and comparing excess ILI to confirmed COVID case counts, we estimate the syndromic case detection rate of SARS-CoV-2 in the US to be less than 13%. If only 1/3 of patients infected with SARS-CoV-2 sought care, the ILI surge would correspond to more than 8.7 million new SARS-CoV-2 infections across the US during the three week period from March 8 to March 28. Combining excess ILI counts with the date of onset of community transmission in the US, we also show that the early epidemic in the US was unlikely to be doubling slower than every 4 days. Together these results suggest a conceptual model for the COVID epidemic in the US in which rapid spread across the US are combined with a large population of infected patients with presumably mild-to-moderate clinical symptoms. We emphasize the importance of testing these findings with seroprevalence data, and discuss the broader potential to use syndromic time series for early detection and understanding of emerging infectious diseases.

title: COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
abstract: A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We firstly maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively.

title: Tracking the Twitter attention around the research efforts on the COVID-19 pandemic
abstract: The outbreak of the COVID-19 pandemic has been accompanied by a bulk of scientific research and related Twitter discussions. To unravel the public concerns about the COVID-19 crisis reflected in the science-based Twitter conversations, this study tracked the Twitter attention around the COVID-19 research efforts during the first three months of 2020. On the basis of nearly 1.4 million Twitter mentions of 6,162 COVID-19-related scientific publications, we investigated the temporal tweeting dynamic and the Twitter users involved in the online discussions around COVID-19-related research. The results show that the quantity of Twitter mentions of COVID-19-related publications was on rising. Scholarly-oriented Twitter users played an influential role in disseminating research outputs on COVID-19, with their tweets being frequently retweeted. Over time, a change in the focus of the Twitter discussions can be observed, from the initial attention to virological and clinical research to more practical topics, such as the potential treatments, the countermeasures by the governments, the healthcare measures, and the influences on the economy and society, in more recent times.

title: Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo
abstract: With the rapid development of COVID-19, people are asked to maintain"social distance"and"stay at home". In this scenario, more and more social interactions move online, especially on social media like Twitter and Weibo. People post tweets to share information, express opinions and seek help during the pandemic, and these tweets on social media are valuable for studies against COVID19, such as early warning and outbreaks detection. Therefore, in this paper, we release a novel large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 December 2019 to 30 April 2020. Moreover, the field information of the dataset is very rich, including basic tweets information, interactive information, location information and retweet network. We hope this dataset can promote studies of COVID-19 from multiple perspectives and enable better and faster researches to suppress the spread of this disease.

title: Alveolar early progenitors in the aged human lung have increased expression of ACE2 accompanied with genes involved in beta-amyloid clearance: Indication of SARS-CoV-2 also using soluble ACE2 in aged-lungs to enter ACE2-negative cells
abstract: COVID-19 is the current pandemic caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2) that uses ACE2 protein on the cell surface. By analyzing publicly available datasets, I uncovered that alveolar early progenitors (AEP), a subset of the type-2 pneumocytes, showed increased ACE2 expression in the older lungs. AEPs co-express TMPRSS2, CTSL. Aged AEP-gene expression signature suggested an active response to beta-amyloid-induced ACE2 shedding, to limit the intercellular beta-amyloid accumulation in otherwise healthy human lungs. Susceptibility of AEP to SARS-CoV2 and ACE2 secretory capacity of these cells makes aged human lung sensitive for rapid-infection, by a possible in-solution ACE2 binding and entry into ACE2-negative cells, thereby increasing the target cell diversity and numbers. Single-cell analysis of COVID19 patients with moderate and severe infections, clearly showed that severe infections showed SARS-CoV-2 transcript in ACE2-negative TMPRSS-negative but CTSL-positive cell types in their bronchoalveolar lavage fluid, validating in-solution ACE2-binding enabling infection.

title: Distributed data processing for public health surveillance
abstract: Many systems for routine public health surveillance rely on centralized collection of potentially identifiable, individual, identifiable personal health information (PHI) records. Although individual, identifiable patient records are essential for conditions for which there is mandated reporting, such as tuberculosis or sexually transmitted diseases, they are not routinely required for effective syndromic surveillance. Public concern about the routine collection of large quantities of PHI to support non-traditional public health functions may make alternative surveillance methods that do not rely on centralized identifiable PHI databases increasingly desirable.  The National Bioterrorism Syndromic Surveillance Demonstration Program (NDP) is an example of one alternative model. All PHI in this system is initially processed within the secured infrastructure of the health care provider that collects and holds the data, using uniform software distributed and supported by the NDP. Only highly aggregated count data is transferred to the datacenter for statistical processing and display.  Detailed, patient level information is readily available to the health care provider to elucidate signals observed in the aggregated data, or for ad hoc queries. We briefly describe the benefits and disadvantages associated with this distributed processing model for routine automated syndromic surveillance.  For well-defined surveillance requirements, the model can be successfully deployed with very low risk of inadvertent disclosure of PHI – a feature that may make participation in surveillance systems more feasible for organizations and more appealing to the individuals whose PHI they hold. It is possible to design and implement distributed systems to support non-routine public health needs if required.

title: Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies
abstract: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method.  We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies.  We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses.  Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

title: Cancer patients and research during COVID-19 pandemic: A systematic review of current evidence
abstract: The novel coronavirus, also known as SARS-Cov-2 or COVID-19 has become a worldwide threat and the major healthcare concern of the year 2020. Cancer research was directly affected by the emerging of this disease. According to some Chinese studies, cancer patients are more vulnerable to COVID-19 complications. This observation led many oncologists to change their daily practice in cancer care, without solid evidence and recommendations. Moreover, the COVID-19 manifestations as well as its diagnosis are particular in this special population. In this review paper we expose the challenges of cancer management in the era of SARS-CoV-2, the epidemiological, clinical, pathological and radiological characteristics of the disease in cancer patients and its outcomes on this population. Finally, we focus on strategies that are followed in cancer management with review of national and international guidelines.

title: A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration.
abstract: As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated in the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique world-wide event into biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. This open dataset will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time.

title: COVID-19 and gender-specific difference: Analysis of public surveillance data in Hong Kong and Shenzhen, China, from January 10 to February 15, 2020

title: COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses
abstract: The coronavirus disease 19 (COVID-19) is a highly transmittable and pathogenic viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in Wuhan, China and spread around the world. Genomic analysis revealed that SARS-CoV-2 is phylogenetically related to severe acute respiratory syndrome-like (SARS-like) bat viruses, therefore bats could be the possible primary reservoir. The intermediate source of origin and transfer to humans is not known, however, the rapid human to human transfer has been confirmed widely. There is no clinically approved antiviral drug or vaccine available to be used against COVID-19. However, few broad-spectrum antiviral drugs have been evaluated against COVID-19 in clinical trials, resulted in clinical recovery. In the current review, we summarize and comparatively analyze the emergence and pathogenicity of COVID-19 infection and previous human coronaviruses severe acute respiratory syndrome coronavirus (SARS-CoV) and middle east respiratory syndrome coronavirus (MERS-CoV). We also discuss the approaches for developing effective vaccines and therapeutic combinations to cope with this viral outbreak.

title: Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants †
abstract: The recent outbreak of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 in December 2019 raised global health concerns. The viral 3-chymotrypsin-like cysteine protease (3CLpro) enzyme controls coronavirus replication and is essential for its life cycle. 3CLpro is a proven drug discovery target in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and middle east respiratory syndrome coronavirus (MERS-CoV). Recent studies revealed that the genome sequence of SARS-CoV-2 is very similar to that of SARS-CoV. Therefore, herein, we analysed the 3CLpro sequence, constructed its 3D homology model, and screened it against a medicinal plant library containing 32,297 potential anti-viral phytochemicals/traditional Chinese medicinal compounds. Our analyses revealed that the top nine hits might serve as potential anti- SARS-CoV-2 lead molecules for further optimisation and drug development process to combat COVID-19.

title: Current epidemiological and clinical features of COVID-19; a global perspective from China
abstract: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and represents a potentially fatal disease of great global public health importance. As of March 26, 2020, the outbreak of COVID-19 has resulted in 462,801 confirmed cases and 20,839 deaths globally, which is more than those caused by SARS and Middle East respiratory syndrome (MERS) in 2003 and 2013, respectively. The epidemic has posed considerable challenges worldwide. Under a strict mechanism of massive prevention and control, China has seen a rapid decrease in new cases of coronavirus; however, the global situation remains serious. Additionally, the origin of COVID-19 has not been determined and no specific antiviral treatment or vaccine is currently available. Based on the published data, this review systematically discusses the etiology, epidemiology, clinical characteristics, and current intervention measures related to COVID-19 in the hope that it may provide a reference for future studies and aid in the prevention and control of the COVID-19 epidemic.

title: Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review
abstract: Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

title: Database and Analytical Resources for Viral Research Community
abstract: Many public databases and analytical resources are available to facilitate virology research. The Virus Pathogen Database and Analysis Resource (ViPR, see “Relevant Websites section”) and Influenza Research Database (IRD, see “Relevant Websites section”) are comprehensive and highly curated repositories of genome and protein sequence records and annotations, protein structures, immune epitopes, and epidemiological and surveillance data about human and related viral pathogens. These data are acquired from public repositories, direct submissions and in-house bioinformatics analyses. The resources offer seamless integration of data, analytics and visualization, and are freely available without cost or restriction to facilitate diagnostics and therapeutics development for priority pathogens.

title: Vaginal delivery report of a healthy neonate born to a convalescent mother with COVID--19
abstract: The outbreak of the infection of 2019 novel coronavirus disease (COVID--19) has become a challenging public health threat worldwide. Limited data are available for pregnant women with COVID-19 pneumonia. We report a case of a convalescing pregnant woman diagnosed with COVID-19 infection 37 days before delivery in the third trimester. A live birth without severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was performed successfully via the vagina. The findings from our case indicate that there is no intrauterine transmission in this woman who developed COVID-19 pneumonia in late pregnancy.

title: Machine intelligence design of 2019-nCoV drugs
abstract: Wuhan coronavirus, called 2019-nCoV, is a newly emerged virus that infected more than 9692 people and leads to more than 213 fatalities by January 30, 2020. Currently, there is no effective treatment for this epidemic. However, the viral protease of a coronavirus is well-known to be essential for its replication and thus is an effective drug target. Fortunately, the sequence identity of the 2019-nCoV protease and that of severe-acute respiratory syndrome virus (SARS-CoV) is as high as 96.1%. We show that the protease inhibitor binding sites of 2019-nCoV and SARS-CoV are almost identical, which means all potential anti-SARS-CoV chemotherapies are also potential 2019-nCoV drugs. Here, we report a family of potential 2019-nCoV drugs generated by a machine intelligence-based generative network complex (GNC). The potential effectiveness of treating 2019-nCoV by using some existing HIV drugs is also analyzed.

title: The assessment of data sources for influenza virologic surveillance in New York State
abstract: Following the 2013 USA release of the Influenza Virologic Surveillance Right Size Roadmap, the New York State Department of Health (NYSDOH) embarked on an evaluation of data sources for influenza virologic surveillance.  To assess NYS data sources, additional to data generated by the state public health laboratory (PHL), which could enhance influenza surveillance at the state and national level.  Potential sources of laboratory test data for influenza were analyzed for quantity and quality. Computer models, designed to assess sample sizes and the confidence of data for statistical representation of influenza activity, were used to compare PHL test data to results from clinical and commercial laboratories, reported between June 8, 2013 and May 31, 2014.  Sample sizes tested for influenza at the state PHL were sufficient for situational awareness surveillance with optimal confidence levels, only during peak weeks of the influenza season. Influenza data pooled from NYS PHLs and clinical laboratories generated optimal confidence levels for situational awareness throughout the influenza season. For novel influenza virus detection in NYS, combined real‐time (rt) RT‐PCR data from state and regional PHLs achieved ≥85% confidence during peak influenza activity, and ≥95% confidence for most of low season and all of off‐season.  In NYS, combined data from clinical, commercial, and public health laboratories generated optimal influenza surveillance for situational awareness throughout the season. Statistical confidence for novel virus detection, which is reliant on only PHL data, was achieved for most of the year.

title: Response to Commentary on "The neuroinvasive potential of SARS-CoV-2 may play a role in the respiratory failure of COVID-19 patients"
abstract: In a recent review, we have suggested a neuroinvasive potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its possible role in the causation of acute respiratory failure of coronavirus disease 2019 (COVID-19) patients (J Med Viol doi: 10.1002/jmv.25728), based upon the clinical and experimental data available on the past SARS-CoV-1 and the recent SARS-CoV-2 pandemic. In this article, we provide new evidence recently reported regarding the neurotropic potential of SARS-CoV-2 and respond to several comments on our previously published article. In addition, we also discuss the peculiar manifestations of respiratory failure in COVID-19 patients and the possible involvement of nervous system.

title: A New Resource for Genomics and Precision Health Information and Publications on the Investigation and Control of COVID-19 and other Coronaviruses
abstract: We developed a new online database that contains the most updated published scientific literature, online news and reports, CDC and National Institutes of Health (NIH) resources. The tool captures emerging discoveries and applications of genomics, molecular, and other precision medicine and precision public health tools in the investigation and control of coronavirus diseases, including COVID-19, MERS-CoV, and SARS. Availability Coronavirus Disease Portal (CDP) can be freely accessed via https://phgkb.cdc.gov/PHGKB/coVInfoStartPage.action.

title: Composition and divergence of coronavirus spike proteins and host ACE2 receptors predict potential intermediate hosts of SARS‐CoV‐2
abstract: From the beginning of 2002 and 2012, severe respiratory syndrome coronavirus (SARS‐CoV) and Middle East respiratory syndrome coronavirus (MERS‐CoV) crossed the species barriers to infect humans, causing thousands of infections and hundreds of deaths, respectively. Currently, a novel coronavirus (SARS‐CoV‐2), which has become the cause of the outbreak of Coronavirus Disease 2019 (COVID‐19), was discovered. Until 18 February 2020, there were 72 533 confirmed COVID‐19 cases (including 10 644 severe cases) and 1872 deaths in China. SARS‐CoV‐2 is spreading among the public and causing substantial burden due to its human‐to‐human transmission. However, the intermediate host of SARS‐CoV‐2 is still unclear. Finding the possible intermediate host of SARS‐CoV‐2 is imperative to prevent further spread of the epidemic. In this study, we used systematic comparison and analysis to predict the interaction between the receptor‐binding domain (RBD) of coronavirus spike protein and the host receptor, angiotensin‐converting enzyme 2 (ACE2). The interaction between the key amino acids of S protein RBD and ACE2 indicated that, other than pangolins and snakes, as previously suggested, turtles (Chrysemys picta bellii, Chelonia mydas, and Pelodiscus sinensis) may act as the potential intermediate hosts transmitting SARS‐CoV‐2 to humans.

title: Characteristics of Endodontic Emergencies during COVID-19 Outbreak in Wuhan
abstract: In late 2019 an outbreak of a new coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was detected in Wuhan, China. A great percentage of patients with this disease developed symptoms of dry cough, malaise and high fever. During this time several patients requiring assessment and treatment of endodontic emergencies were directed to the School and Hospital of Stomatology at Wuhan University (WHUSS). Here, we examined the characteristics of these patients.  A total of 96 patients with mean age of 42.24 ± 18.32 years visited General and Emergency Department of WHUSS due to endodontic emergencies during the peak period of February 22 to March 2, 2020. Patient information was collected and organized by date of visit, gender, age, systemic diseases history. Body temperature was measured and acquired for each patient, a coronavirus disease 2019 (COVID-19) epidemiological investigation questionnaire was given to each patient, endodontic diagnosis determined for the offending tooth and verbal numerical rating scale (VNRS) was used to record pain levels.  Of total patient visits during this period, 50.26% of visits were for endodontic treatment. No patients had fever (>37.2°C). One patient with a confirmed COVID-19 history was admitted after recovery. Three admitted patients had been exposed to confirmed or suspected COVID-19 patients. Twelve admitted patients (12.5%) with mean age of 62.42 ± 13.77 years had a history of systemic diseases. The most common age group for endodontic emergencies was 45-64 years (30.21%) and patients of this group showed significantly higher mean VNRS score compared with that of the 6-19 years group and 20-34 years group (p<0.05). The majority of endodontic emergency diagnosis was diseases of symptomatic irreversible pulpitis (53.10%). Patients who were diagnosed with symptomatic irreversible pulpitis, symptomatic apical periodontitis and acute apical abscess showed significantly higher mean VNRS Score than that of other groups (p<0.05).  Endodontic emergencies, with symptomatic irreversible pulpitis being the most common, consist of a much higher proportion of dental emergencies in a COVID-19 high risk area than normally. Vital pulp therapy can advantageously reduce treatment time resulting in reduced risk of infection for vital pulp cases. Rubber dam, personal protective equipment and patient screening are of great importance during the COVID-19 outbreak in protecting clinicians.

title: Piveau: A Large-Scale Open Data Management Platform Based on Semantic Web Technologies
abstract: The publication and (re)utilization of Open Data is still facing multiple barriers on technical, organizational and legal levels. This includes limitations in interfaces, search capabilities, provision of quality information and the lack of definite standards and implementation guidelines. Many Semantic Web specifications and technologies are specifically designed to address the publication of data on the web. In addition, many official publication bodies encourage and foster the development of Open Data standards based on Semantic Web principles. However, no existing solution for managing Open Data takes full advantage of these possibilities and benefits. In this paper, we present our solution “Piveau”, a fully-fledged Open Data management solution, based on Semantic Web technologies. It harnesses a variety of standards, like RDF, DCAT, DQV, and SKOS, to overcome the barriers in Open Data publication. The solution puts a strong focus on assuring data quality and scalability. We give a detailed description of the underlying, highly scalable, service-oriented architecture, how we integrated the aforementioned standards, and used a triplestore as our primary database. We have evaluated our work in a comprehensive feature comparison to established solutions and through a practical application in a production environment, the European Data Portal. Our solution is available as Open Source.  The online version of this chapter (10.1007/978-3-030-49461-2_38) contains supplementary material, which is available to authorized users.

title: Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
abstract: In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task. Variants of GCN are used in multiple state-of-the-art approaches and therefore it is important to understand the specifics and limitations of GCN-based models. Despite serious efforts, we were not able to fully reproduce the results from the original paper and after a thorough audit of the code provided by authors, we concluded, that their implementation is different from the architecture described in the paper. In addition, several tricks are required to make the model work and some of them are not very intuitive.We provide an extensive ablation study to quantify the effects these tricks and changes of architecture have on final performance. Furthermore, we examine current evaluation approaches and systematize available benchmark datasets.We believe that people interested in KG matching might profit from our work, as well as novices entering the field.

title: Bayesian monitoring of emerging infectious diseases
abstract: We define data analyses to monitor a change in R, the average number of secondary cases caused by a typical infected individual. The input dataset consists of incident cases partitioned into outbreaks, each initiated from a single index case. We split of the input dataset into two successive subsets, to evaluate two successive R values, according to the Bayesian paradigm. We used the Bayes factor between the model with two different R values and that with a single R value to justify that the change in R is statistically significant. We validated our approach using simulated data, generated using known R. In particular, we found that claiming two distinct R values may depend significantly on the number of outbreaks. We then reanalyzed data previously studied by Jansen et al. [Jansen et al. Science 301 (5634), 804], concerning the effective reproduction number for measles in the UK, during 1995-2002. Our analyses showed that the 1995-2002 dataset should be divided into two separate subsets for the periods 1995-1998 and 1999-2002. In contrast, Jansen et al. take this splitting point as input of their analysis. Our estimated effective reproduction numbers R are in good agreement with those found by Jansen et al. In conclusion, our methodology for detecting temporal changes in R using outbreak-size data worked satisfactorily with both simulated and real-world data. The methodology may be used for updating R in real time, as surveillance outbreak data become available.

title: The MERS-CoV receptor DPP4 as a candidate binding target of the SARS-CoV-2 spike
abstract: The ongoing outbreak of the novel coronavirus pneumonia COVID-19 has caused great number of cases and deaths, but our understanding about the pathogen SARS-CoV-2 remains largely unclear. The attachment of the virus with the cell-surface receptor and a co-factor is the first step for the infection. Here, bioinformatics approaches combining human-virus protein interaction prediction and protein docking based on crystal structures have revealed the high affinity between human dipeptidyl peptidase 4 (DPP4) and the spike (S) receptor-binding domain of SARS-CoV-2. Intriguingly, the crucial binding residues of DPP4 are identical to those as bound to the MERS-CoV-S. Moreover, E484 insertion and adjacent substitutions should be most essential for this DPP4-binding ability acquirement of SARS-CoV-2-S compared with SARS-CoV-S. This potential utilization of DPP4 as a binding target for SARS-CoV-2 may offer novel insight into the viral pathogenesis, and help the surveillance and therapeutics strategy for meeting the challenge of COVID-19.

title: First Mildly Ill, Non-Hospitalized Case of Coronavirus Disease 2019 (COVID-19) Without Viral Transmission in the United States — Maricopa County, Arizona, 2020
abstract: Coronavirus disease 2019 (COVID-19) causes a range of illness severity. Mild illness has been reported, but whether illness severity correlates with infectivity is unknown. We describe the public health investigation of a mildly ill, non-hospitalized COVID-19 case who traveled to China.  The case was a Maricopa County resident with multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive specimens collected on January 22, 2020. Contacts were persons exposed to the case on or after the day before case diagnostic specimen collection. Contacts were monitored for 14 days after last known exposure. High-risk contacts had close, prolonged case contact (≥10 minutes within 2 meters). Medium-risk contacts wore all U.S. Centers for Disease Control and Prevention (CDC)-recommended personal protective equipment during interactions. Nasopharyngeal and oropharyngeal (NP/OP) specimens were collected from the case and high-risk contacts and tested for SARS-CoV-2.  Paired case NP/OP specimens were collected for SARS-CoV-2 testing at 11 time points. In 8 pairs (73%), ≥1 specimen tested positive or indeterminate, and in 3 pairs (27%) both tested negative. Specimens collected 18 days after diagnosis tested positive. Sixteen contacts were identified; 11 (69%) had high-risk exposure, including 1 intimate contact, and 5 (31%) had medium-risk exposure. In total, 35 high-risk contact NP/OP specimens were collected for SARS-CoV-2 testing; all 35 pairs (100%) tested negative.  This report demonstrates that SARS-CoV-2 infection can cause mild illness and result in positive tests for up to 18 days after diagnosis, without evidence of transmission to close contacts. These data might inform public health strategies to manage individuals with asymptomatic infection or mild illness.

title: The American Heart Association COVID-19 CVD Registry powered by Get With The Guidelines®.
abstract: In response to the public health emergency created by the COVID-19 pandemic, American Heart Association volunteers and staff aimed to rapidly develop and launch a resource for the medical and research community to expedite scientific advancement through shared learning, quality improvement, and research. In less than 4 weeks after it was first announced on April 3, 2020, AHA's COVID-19 CVD Registry powered by Get With The Guidelines® (GWTG) received its first clinical records.  Participating hospitals are enrolling consecutive hospitalized patients with active COVID-19 disease, regardless of CVD status. This hospital quality improvement program will allow participating hospitals and health systems to evaluate patient-level data including mortality rates, intensive care unit (ICU) bed days, and ventilator days from individual review of electronic medical records of sequential adult patients with active COVID-19 infection. Participating sites can leverage these data for onsite, rapid quality improvement and benchmarking versus other institutions. After 9 weeks, more than 130 sites have enrolled in the program and more than 4,000 records have been abstracted in the national dataset. Additionally, the aggregate dataset will be a valuable data resource for the medical research community.  The AHA COVID-19 CVD Registry will support greater understanding of the impact of COVID-19 on cardiovascular disease and will inform best practices for evaluation and management of patients with COVID-19.

title: Purely data-driven exploration of COVID-19 pandemic after three months of the outbreak
abstract: It has been three months since the novel coronavirus (COVID-19) pandemic outbreak. Many research studies were carried to understand its epidemiological characteristics in the early phase of the disease outbreak. The current study is yet another contribution to better understand the disease properties by parameter estimation of mathematical SIR epidemic modeling. The authors use Johns Hopkins University dataset to estimate the basic reproduction number of COVID-19 for representative countries (Japan, Germany, Italy, France, and Netherlands) selected using cluster analysis. As a by-product, the authors estimate transmission, recovery, and death rates for each selected country and carry statistical tests to see if there are any significant differences.

title: A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration
abstract: As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated in the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique world-wide event into biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. This open dataset will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time.

title: Clinical research networks and assessing pandemic severity

title: Data Protection and Research: A vital challenge in the era of Covid-19 Pandemic

title: Using Supervised Machine Learning and Empirical Bayesian Kriging to reveal Correlates and Patterns of COVID-19 Disease outbreak in sub-Saharan Africa: Exploratory Data Analysis
abstract: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan, China, and has subsequently spread worldwide. Knowledge of coronavirus-related risk factors can help countries build more systematic and successful responses to COVID-19 disease outbreak. Here we used Supervised Machine Learning and Empirical Bayesian Kriging (EBK) techniques to reveal correlates and patterns of COVID-19 Disease outbreak in sub-Saharan Africa (SSA).  We analyzed time series aggregate data compiled by Johns Hopkins University on the outbreak of COVID-19 disease across SSA. COVID-19 data was merged with additional data on socio-demographic and health indicator survey data for 39 of SSA 48 countries that reported confirmed cases and deaths from coronavirus between February 28, 2020 through March 26, 2020. We used supervised machine learning algorithm, Lasso for variable selection and statistical inference. EBK was used to also create a raster estimating the spatial distribution of COVID-19 disease outbreak.  The lasso Cross-fit partialing out predictive model ascertained seven variables significantly associated with the risk of coronavirus infection (i.e. new HIV infections among pediatric, adolescent, and middle-aged adult PLHIV, time (days), pneumococcal conjugate-based vaccine, incidence of malaria and diarrhea treatment). Our study indicates, the doubling time in new coronavirus cases was 3 days. The steady three-day decrease in coronavirus outbreak rate of change (ROC) from 37% on March 23, 2020 to 23% on March 26, 2020 indicates the positive impact of countries' steps to stymie the outbreak. The interpolated maps show that coronavirus is rising every day and appears to be severely confined in South Africa. In the West African region (i.e. Burkina Faso, Ghana, Senegal, CotedIviore, Cameroon, and Nigeria), we predict that new cases and deaths from the virus are most likely to increase.  Integrated and efficiently delivered interventions to reduce HIV, pneumonia, malaria and diarrhea, are essential to accelerating global health efforts. Scaling up screening and increasing COVID-19 testing capacity across SSA countries can help provide better understanding on how the pandemic is progressing and possibly ensure a sustained decline in the ROC of coronavirus outbreak.

title: Chaos game representation dataset of SARS-CoV-2 genome
abstract: As of April 16, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 142,000 deaths and more than 2,000,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream, digital signal processing, and machine learning techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical values representation. Thus, the dataset provides a chaos game representation (CGR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the CGR of 100 instances of SARS-CoV-2 virus, 11540 instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).

title: Fast and accurate detection of Covid-19-related pneumonia from chest X-ray images with novel deep learning model
abstract: Novel coronavirus disease has spread rapidly worldwide. As recent radiological literatures on Covid-19 related pneumonia is primarily focused on CT findings, the American College of Radiology (ACR) recommends using portable chest X-radiograph (CXR). A tool to assist for detection and monitoring of Covid-19 cases from CXR is highly required.  To develop a fully automatic framework to detect Covid-19 related pneumonia using CXR images and evaluate its performance.  In this study, a novel deep learning model, named CovIDNet (Covid-19 Indonesia Neural-Network), was developed to extract visual features from chest x-ray images for the detection of Covid-19 related pneumonia. The model was trained and validated by chest x-rays datasets collected from several open source provided by GitHub and Kaggle.  In the validation stage using open-source data, the accuracy to recognize Covid-19 and others classes reaches 98.44%, that is, 100% Covid-19 precision and 97% others precision.  The use of the model to classify Covid-19 and other pathologies might slightly decrease the accuracy. Although SoftMax was used to handle classification bias, this indicates the benefit of additional training upon the introduction of new set of data.  The model has been tested and get 98.4% accuracy for open source datasets, the sensitivity and specificity are 100% and 96.97%, respectively.

title: Containing COVID-19 Among 627,386 Persons in Contact With the Diamond Princess Cruise Ship Passengers Who Disembarked in Taiwan: Big Data Analytics
abstract: Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation.  We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on February 5, 2020, after an index case was identified on January 20, 2020.  The smart contact tracing–based mobile sensor data, cross-validated by other big sensor surveillance data, were analyzed by the mobile geopositioning method and rapid analysis to identify 627,386 potential contact-persons. Information on self-monitoring and self-quarantine was provided via SMS, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were offered for symptomatic contacts. National Health Insurance claims big data were linked, to follow-up on the outcome related to COVID-19 among those who were hospitalized due to pneumonia and advised to undergo screening for SARS-CoV-2.  As of February 29, a total of 67 contacts who were tested by reverse transcription–polymerase chain reaction were all negative and no confirmed COVID-19 cases were found. Less cases of respiratory syndrome and pneumonia were found after the follow-up of the contact population compared with the general population until March 10, 2020.  Big data analytics with smart contact tracing, automated alert messaging for self-restriction, and follow-up of the outcome related to COVID-19 using health insurance data could curtail the resources required for conventional epidemiological contact tracing.

title: Psychological responses, behavioral changes and public perceptions during the early phase of the COVID-19 outbreak in China: a population based cross-sectional survey
abstract: To investigate psychological and behavioral responses to the threat of SARS-CoV-2 infections and their associations with public perceptions in China Design: Cross sectional population-based telephone survey via random digital dialing between 1 and 10 February, 2020  Wuhan (the epicentre and quarantined city), and Shanghai (a typical major city with close transportation link with Wuhan) Participants: Random sample of 510 residents in Wuhan and 501 residents in Shanghai aged above 18  Anxiety (measured by the 7-item generalized anxiety disorder [GAD-7] scale), recommended and avoidance behaviors (engaged in all six behaviors such as increasing surface cleaning and reducing going out).  The prevalence rates of moderate or severe anxiety (score ≥10 on GAD-7) were 32.7% (n=167) among Wuhan participants and 20.4% (n=102) among Shanghai participants. 78.6% (n=401) of Wuhan participants and 63.9% (n=320) of Shanghai participants had carried out all six precautionary behaviors. For both measures, Wuhan participants were more responsive to the outbreak (p<0.001). Controlling for personal characteristics, logistic regression results suggested that risks of moderate or severe anxiety were positively associated with perceived susceptibility (odds ratio 1.6, 95% confidence interval 1.3-1.8) and severity of the disease (1.6, 1.4-1.9) and confusion about information reliability (1.6, 1.5-1.9). Having confidence in taking measures to protect oneself against the disease was associated with a lower risk (0.6, 0.5-0.7). The strongest predictor of behavioral change was perceived severity (1.2, 1.1-1.4), followed by confusion about information reliability (1.1, 1.0-1.3).  Psychological and behavioral responses to COVID-19 have been dramatic during the rising phase of the outbreak. Our results support efforts for timely dissemination of accurate and reliable information to address the high anxiety level.

title: Benefits of random‐priming: Exhaustive survey of a cDNA library from lung tissue of a SARS patient
abstract: The severe acute respiratory syndrome (SARS) leads to severe injury in the lungs with multiple factors, though the pathogenesis is still largely unclear. This paper describes the particular analyses of the transcriptome of human lung tissue that was infected by SARS‐associated coronavirus (SARS‐CoV). Random primers were used to produce ESTs from total RNA samples of the lung tissue. The result showed a high diversity of the transcripts, covering much of the human genome, including loci which do not contain protein coding sequences. 10,801 ESTs were generated and assembled into 267 contigs plus 7,659 singletons. Sequences matching to SARS‐CoV RNAs and other pneumonia‐related microbes were found. The transcripts were well classified by functional annotation. Among the 7,872 assembled sequences that were identified as from human genome, 578 non‐coding genes were revealed by BLAST search. The transcripts were mapped to the human genome with the restriction of identity = 100%, which found a candidate pool of 448 novel transcriptional loci where EST transcriptional signal was never found before. Among these, 13 loci were never reported to be transcriptional by other detection methods such as gene chips, tiling arrays, and paired‐end ditags (PETs). The result showed that random‐priming cDNA library is valid for the investigation of transcript diversity in the virus‐infected tissue. The EST data could be a useful supplemental source for SARS pathology researches.  2011.

title: Features of anosmia in COVID-19
abstract: Medical publications about anosmia with COVID-19 are scarce. We aimed to describe the prevalence and features of anosmia in COVID-19 patients.  We retrospectively included COVID-19 patients with anosmia between March 1 and March 17, 2020. We used SARS-CoV-2 real time PCR in respiratory samples to confirm the cases.  Fifty-four of 114 patients (47%) with confirmed COVID-19 reported anosmia. Mean age of the 54 patients was 47 (±16) years; 67% were females and 37% were hospitalized. The median Charlson comorbidity index was 0.70 (±1.6 [0-7]). Forty-six patients (85%) had dysgeusia and 28% presented with pneumonia. Anosmia began 4.4 (±1.9 [1-8]) days after infection onset. The mean duration of anosmia was 8.9 (±6.3 [1-21]) days and 98% of patients recovered within 28 days.  Anosmia was present in half of our European COVID-19 patients and was often associated with dysgeusia.

title: COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images
abstract: Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To further improve diagnosis capabilities of the COVID-CAPS, pre-training based on a new dataset constructed from an external dataset of X-ray images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%.

title: A comprehensive assessment of N-terminal signal peptides prediction methods
abstract: Amino-terminal signal peptides (SPs) are short regions that guide the targeting of secretory proteins to the correct subcellular compartments in the cell. They are cleaved off upon the passenger protein reaching its destination. The explosive growth in sequencing technologies has led to the deposition of vast numbers of protein sequences necessitating rapid functional annotation techniques, with subcellular localization being a key feature. Of the myriad software prediction tools developed to automate the task of assigning the SP cleavage site of these new sequences, we review here, the performance and reliability of commonly used SP prediction tools.  The available signal peptide data has been manually curated and organized into three datasets representing eukaryotes, Gram-positive and Gram-negative bacteria. These datasets are used to evaluate thirteen prediction tools that are publicly available. SignalP (both the HMM and ANN versions) maintains consistency and achieves the best overall accuracy in all three benchmarking experiments, ranging from 0.872 to 0.914 although other prediction tools are narrowing the performance gap.  The majority of the tools evaluated in this study encounter no difficulty in discriminating between secretory and non-secretory proteins. The challenge clearly remains with pinpointing the correct SP cleavage site. The composite scoring schemes employed by SignalP may help to explain its accuracy. Prediction task is divided into a number of separate steps, thus allowing each score to tackle a particular aspect of the prediction.

title: “Yes, but will it work for my patients?” Driving clinically relevant research with benchmark datasets
abstract: Benchmark datasets have a powerful normative influence: by determining how the real world is represented in data, they define which problems will first be solved by algorithms built using the datasets and, by extension, who these algorithms will work for. It is desirable for these datasets to serve four functions: (1) enabling the creation of clinically relevant algorithms; (2) facilitating like-for-like comparison of algorithmic performance; (3) ensuring reproducibility of algorithms; (4) asserting a normative influence on the clinical domains and diversity of patients that will potentially benefit from technological advances. Without benchmark datasets that satisfy these functions, it is impossible to address two perennial concerns of clinicians experienced in computational research: “the data scientists just go where the data is rather than where the needs are,” and, “yes, but will this work for my patients?” If algorithms are to be developed and applied for the care of patients, then it is prudent for the research community to create benchmark datasets proactively, across specialties. As yet, best practice in this area has not been defined. Broadly speaking, efforts will include design of the dataset; compliance and contracting issues relating to the sharing of sensitive data; enabling access and reuse; and planning for translation of algorithms to the clinical environment. If a deliberate and systematic approach is not followed, not only will the considerable benefits of clinical algorithms fail to be realized, but the potential harms may be regressively incurred across existing gradients of social inequity.

title: Quantifying the social distancing privilege gap: a longitudinal study of smartphone movement
abstract: In response to the coronavirus pandemic, social distancing became a widely deployed countermeasure in March 2020. We examined whether healthier and wealthier places more successfully implemented social distancing.  Mobile device location data were used to quantify declines in movement by county (n=2,633) in the United States of America, comparing April 15-17 (n=65,544,268 traces) to baseline of February 17 - March 7. Negative binomial regression was used to estimate gradients of privilege across eleven healthcare and economic indicators, adjusting for rurality and stay-at-home mandates. External validation used separate venue-specific data from Google Location Services.  Counties without stay-at-home orders showed a mobility decline of -52.3% (95% CI: -50.3%, -54.3%), slightly less than the decline in mandated areas (-60.8%; 95% CI: -60.0%, -61.6%). Strong linear gradients in privilege were observed. After adjusting for rurality and stay-at-home orders, counties in the highest quintile of social distancing mobility restriction had: 52% less uninsured, 47% more primary care providers, 29% more exercise space, 27% less food insecurity, 26% less child poverty, 17% higher incomes, 14% less overcrowding, 9.6% more racial segregation, 8.2% less youth, 7.4% more elderly, and 6.2% less influenza vaccination, compared to least social distancing areas.  Healthier and wealthier counties displayed a social distancing privilege gap, measured via smartphone mobility change. Structural inequities in this key countermeasure will influence immunity, and disease incidence and mortality.

title: Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model
abstract: The outbreak of novel coronavirus pneumonia (COVID-19) caused thousands of deaths worldwide, and the number of total infections is still rising. However, the development of effective vaccine for this novel virus would take a few months. Thus it is urgent to identify some potentially effective old drugs that can be used immediately. Fortunately, some compounds that can inhibit coronavirus in vitro have been reported. In this study, the coronavirus-specific dataset was used to fine-tune our pre-trained multi-task deep model. Next we used the re-trained model to select available commercial drugs against targeted proteins of SARS-CoV-2. The results show that abacavir, a powerful nucleoside analog reverse transcriptase inhibitor used to treat HIV, is predicted to have high binding affinity with several proteins of SARS-CoV-2. Almitrine mesylate and roflumilast which are used for respiratory diseases such as chronic obstructive pulmonary disease are also predicted to have inhibitory effect. Overall, ten drugs are listed as potential inhibitors and the important sites for these binding by our model are exhibited. We hope these results would be useful in the fight against SARS-CoV-2.

title: Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study
abstract: Concerns regarding potential neurological complications of COVID-19 are being increasingly reported, primarily in small series. Larger studies have been limited by both geography and specialty. Comprehensive characterisation of clinical syndromes is crucial to allow rational selection and evaluation of potential therapies. The aim of this study was to investigate the breadth of complications of COVID-19 across the UK that affected the brain.  During the exponential phase of the pandemic, we developed an online network of secure rapid-response case report notification portals across the spectrum of major UK neuroscience bodies, comprising the Association of British Neurologists (ABN), the British Association of Stroke Physicians (BASP), and the Royal College of Psychiatrists (RCPsych), and representing neurology, stroke, psychiatry, and intensive care. Broad clinical syndromes associated with COVID-19 were classified as a cerebrovascular event (defined as an acute ischaemic, haemorrhagic, or thrombotic vascular event involving the brain parenchyma or subarachnoid space), altered mental status (defined as an acute alteration in personality, behaviour, cognition, or consciousness), peripheral neurology (defined as involving nerve roots, peripheral nerves, neuromuscular junction, or muscle), or other (with free text boxes for those not meeting these syndromic presentations). Physicians were encouraged to report cases prospectively and we permitted recent cases to be notified retrospectively when assigned a confirmed date of admission or initial clinical assessment, allowing identification of cases that occurred before notification portals were available. Data collected were compared with the geographical, demographic, and temporal presentation of overall cases of COVID-19 as reported by UK Government public health bodies.  The ABN portal was launched on April 2, 2020, the BASP portal on April 3, 2020, and the RCPsych portal on April 21, 2020. Data lock for this report was on April 26, 2020. During this period, the platforms received notification of 153 unique cases that met the clinical case definitions by clinicians in the UK, with an exponential growth in reported cases that was similar to overall COVID-19 data from UK Government public health bodies. Median patient age was 71 years (range 23-94; IQR 58-79). Complete clinical datasets were available for 125 (82%) of 153 patients. 77 (62%) of 125 patients presented with a cerebrovascular event, of whom 57 (74%) had an ischaemic stroke, nine (12%) an intracerebral haemorrhage, and one (1%) CNS vasculitis. 39 (31%) of 125 patients presented with altered mental status, comprising nine (23%) patients with unspecified encephalopathy and seven (18%) patients with encephalitis. The remaining 23 (59%) patients with altered mental status fulfilled the clinical case definitions for psychiatric diagnoses as classified by the notifying psychiatrist or neuropsychiatrist, and 21 (92%) of these were new diagnoses. Ten (43%) of 23 patients with neuropsychiatric disorders had new-onset psychosis, six (26%) had a neurocognitive (dementia-like) syndrome, and four (17%) had an affective disorder. 18 (49%) of 37 patients with altered mental status were younger than 60 years and 19 (51%) were older than 60 years, whereas 13 (18%) of 74 patients with cerebrovascular events were younger than 60 years versus 61 (82%) patients older than 60 years.  To our knowledge, this is the first nationwide, cross-specialty surveillance study of acute neurological and psychiatric complications of COVID-19. Altered mental status was the second most common presentation, comprising encephalopathy or encephalitis and primary psychiatric diagnoses, often occurring in younger patients. This study provides valuable and timely data that are urgently needed by clinicians, researchers, and funders to inform immediate steps in COVID-19 neuroscience research and health policy.

title: Identification of spatial variations in COVID-19 epidemiological data using K-Means clustering algorithm: a global perspective
abstract: Discerning spatial variations of COVID-19 through quantitative analysis operating on the geographically designated datasets relating to socio-demographics and epidemiological data facilitate strategy planning in curtailing the transmission of the disease and focus on articulation of necessary interventions in an informed manner.  K-means clustering was employed on the available country-specific COVID-19 epidemiological data and the influential background characteristics. Country-specific case fatality rates and the average number of people tested positive for COVID-19 per every 10,000 population in each country were derived from the WHO COVID-19 situation report 107, and were used for clustering along with the background characteristics of proportion of countrys population aged >65 years and percentage GDP spent as public health expenditure.  The algorithm grouped the 89 countries into cluster 1 and Cluster 2 of sizes 54 and 35, respectively. It is apparent that Americas, European countries, and Australia formed a major part of cluster 2 with high COVID-19 case fatality rate, higher proportion of countrys population tested COVID-19 positive, higher percentage of GDP spent as public health expenditure, and greater percentage of population being more than 65 years of age.  In spite of the positive correlation between high public health expenditure (%GDP) and COVID-19 incidence, case fatality rate, the immediate task ahead of most of the low and middle income countries is to strengthen their public health systems realizing that the correlation found in this study could be spurious in light of the underreported number of cases and poor death registration.

title: Classification of Coronavirus Images using Shrunken Features
abstract: Necessary screenings must be performed to control the spread of the Corona Virus (COVID-19) in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the wrong test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions with COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it was aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray images. In this study, images belonging to 6 situations, including coronavirus images, are classified. Since the number of images in the dataset is deficient and unbalanced, it is more convenient to analyze these images with hand-crafted feature extraction methods. For this purpose, firstly, all the images in the dataset are extracted with the help of four feature extraction algorithms. These extracted features are combined in raw form. The unbalanced data problem is eliminated by producing feature vectors with the SMOTE algorithm. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially in order to make the diagnosis of COVID-19 in a short time and effectively.

title: Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
abstract: We created this CORD-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020-03-13). This CORD-NER dataset covers 75 fine-grained entity types: In addition to the common biomedical entity types (e.g., genes, chemicals and diseases), it covers many new entity types related explicitly to the COVID-19 studies (e.g., coronaviruses, viral proteins, evolution, materials, substrates and immune responses), which may benefit research on COVID-19 related virus, spreading mechanisms, and potential vaccines. CORD-NER annotation is a combination of four sources with different NER methods. The quality of CORD-NER annotation surpasses SciSpacy (over 10% higher on the F1 score based on a sample set of documents), a fully supervised BioNER tool. Moreover, CORD-NER supports incrementally adding new documents as well as adding new entity types when needed by adding dozens of seeds as the input examples. We will constantly update CORD-NER based on the incremental updates of the CORD-19 corpus and the improvement of our system.

title: Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study
abstract: The recent coronavirus disease (COVID-19) pandemic is taking a toll on the world’s health care infrastructure as well as the social, economic, and psychological well-being of humanity. Individuals, organizations, and governments are using social media to communicate with each other on a number of issues relating to the COVID-19 pandemic. Not much is known about the topics being shared on social media platforms relating to COVID-19. Analyzing such information can help policy makers and health care organizations assess the needs of their stakeholders and address them appropriately.  This study aims to identify the main topics posted by Twitter users related to the COVID-19 pandemic.  Leveraging a set of tools (Twitter’s search application programming interface (API), Tweepy Python library, and PostgreSQL database) and using a set of predefined search terms (“corona,” “2019-nCov,” and “COVID-19”), we extracted the text and metadata (number of likes and retweets, and user profile information including the number of followers) of public English language tweets from February 2, 2020, to March 15, 2020. We analyzed the collected tweets using word frequencies of single (unigrams) and double words (bigrams). We leveraged latent Dirichlet allocation for topic modeling to identify topics discussed in the tweets. We also performed sentiment analysis and extracted the mean number of retweets, likes, and followers for each topic and calculated the interaction rate per topic.  Out of approximately 2.8 million tweets included, 167,073 unique tweets from 160,829 unique users met the inclusion criteria. Our analysis identified 12 topics, which were grouped into four main themes: origin of the virus; its sources; its impact on people, countries, and the economy; and ways of mitigating the risk of infection. The mean sentiment was positive for 10 topics and negative for 2 topics (deaths caused by COVID-19 and increased racism). The mean for tweet topics of account followers ranged from 2722 (increased racism) to 13,413 (economic losses). The highest mean of likes for the tweets was 15.4 (economic loss), while the lowest was 3.94 (travel bans and warnings).  Public health crisis response activities on the ground and online are becoming increasingly simultaneous and intertwined. Social media provides an opportunity to directly communicate health information to the public. Health systems should work on building national and international disease detection and surveillance systems through monitoring social media. There is also a need for a more proactive and agile public health presence on social media to combat the spread of fake news.

title: Dataset for country profile and mobility analysis in the assessment of COVID-19 pandemic
abstract: Understanding the COVID-19 pandemic is a multidisciplinary effort that requires a significant number of variables. This dataset comprises (i) sociodemographic characteristics, compiled from 35 datasets obtained at UN Data; (ii) mobility metrics that can assist the analysis of social distancing, from Google Community Mobility Reports and; (iii) daily counts of cases and deaths by COVID-19, from the European Centre for Disease Prevention and Control and the Johns Hopkins University Center for Systems Science and Engineering. This unified dataset ranges from February 15, 2020 to April 26, 2020, a total of 72 days, and is provided as a collection of time series for 131 countries with 192 variables. The pipeline to preprocess and generate the dataset, along with the dataset itself, are versioned with the Data Version Control tool (DVC) and are thus easily reproducible.

title: Tracing open data in emergencies: the case of the COVID-19 pandemic
abstract: The COVID-19 pandemic constitutes an ongoing, burning Public Health Emergency of International Concern (PHEIC). In 2015, the World Health Organization (WHO) adopted an open-data policy recommendation in such situations.  The present cross-sectional meta-research study aimed to assess the availability of open data and metrics and of articles pertaining to the COVID-19 outbreak in five high-impact journals.  All articles regarding the SARS-CoV-2, published in five high impact journals (Ann Intern Med, BMJ, JAMA, NEJM and Lancet) until March 14, 2020 were retrieved. Meta-data (namely the type of article, number of authors, number of patients, citations, errata, news and social media mentions) were extracted for each article in each journal in a systematic way. Google Scholar and Scopus were used for citations and author metrics respectively, and Altmetrics and PlumX were used for news and social media mentions retrieval. The degree of adherence to the PHEIC open data call was also evaluated.  A total of 140 articles were published until March 14, 2020, mostly opinion papers. Sixteen errata followed these publications. The number of authors in each article ranged from 1 to 63, whereas the number of patients with a laboratory-confirmed SARS-CoV-2 infection reached 2,645. The impact of these publications reached a total of 4,210 cumulative crude citations and 342,790 news and social media mentions. Only one publication (0.7%) provided complete open data, while 32 (22.9%) included patient data.  Even though a large number of manuscripts was produced since the pandemic, availability of open data remains restricted.

title: Noninvasive ventilation in acute respiratory failure due to H1N1 influenza
abstract: We present a case of severe H1N1 influenza with hypoxemic acute respiratory failure necessitating mechanical ventilation benefited from noninvasive positive pressure ventilation (NIPPV). The NIPPV may be of great use in treating patients with H1N1-related acute respiratory distress syndrome in a resource poor setting or when invasive ventilator is unavailable.

title: Potent MERS-CoV Fusion Inhibitory Peptides Identified from HR2 Domain in Spike Protein of Bat Coronavirus HKU4
abstract: The Middle East respiratory syndrome coronavirus (MERS-CoV) emerged in 2012 and caused continual outbreaks worldwide with high mortality. However, no effective anti-MERS-CoV drug is currently available. Recently, numerous evolutionary studies have suggested that MERS-CoV originated from bat coronavirus (BatCoV). We herein reported that three peptides derived from the HR2 region in spike protein of BatCoV HKU4, including HKU4-HR2P1, HKU4-HR2P2 and HKU4-HR2P3, could bind the MERS-CoV HR1-derived peptide to form a six-helix bundle (6-HB) with high stability. Moreover, these peptides, particularly HKU4-HR2P2 and HKU4-HR2P3, exhibited potent inhibitory activity against MERS-CoV S-mediated cell–cell fusion and viral infection, suggesting that these HKU4 HR2-derived peptides could be candidates for futher development as antiviral agents against MERS-CoV infection.

title: Coronavirus Perceptions And Economic Anxiety
abstract: We provide one of the first systematic assessments of the development and determinants of economic anxiety at the onset of the coronavirus pandemic. Using a global dataset on internet searches and two representative surveys from the US, we document a substantial increase in economic anxiety during and after the arrival of the coronavirus. We also document a large dispersion in beliefs about the pandemic risk factors of the coronavirus, and demonstrate that these beliefs causally affect individuals' economic anxieties. Finally, we show that individuals' mental models of infectious disease spread understate non-linear growth and shape the extent of economic anxiety.

title: Review on Machine and Deep Learning Models for the Detection and Prediction of Coronavirus
abstract: The novel Coronavirus disease has increased rapidly in the Wuhan city of China in December 2019. This fatal virus has spread across the whole world like a fire in different stages and affecting millions of population and thousands of deaths worldwide. Therefore, it is essential to classify the infected people, so that they can take the precaution in the earlier stages. Also, due to the increasing cases spread of Coronavirus, there are only limited numbers of polymerase change reaction kits available in the hospitals for testing Coronavirus patients. That why it is extremely important to develop artificial intelligence-based automatic diagnostic tools to classify the Coronavirus outbreak. The objective of this paper is to know the novel disease epidemiology, major prevention from spreading of Coronavirus Severe Acute Respiratory Syndrome, and to assess the machine and deep learning-based architectures performance that is proposed in the present year for classification of Coronavirus images such as, X-Ray and computed tomography. Specifically, advanced deep learning-based algorithms known as the Convolutional neural network, which plays a great effect on extracting highly essential features, mostly in terms of medical images. This technique, with using CT and X-Ray image scans, has been adopted in most of the recently published articles on the Coronavirus with remarkable results. Furthermore, according to this paper, this can be noted and said that deep learning technology has potential clinical applications.

title: Data for understanding the risk perception of COVID-19 from Vietnamese sample
abstract: This data article describes the risk perception of COVID-190 from 391 Vietnamese respondents aged from 15 to 47 years. These data have been used in Huynh (2020). These data include the socioeconomics, media attention, and risk perception of COVID-19 in Vietnam through a survey conducted on February 1, 2020. In addition, our data might serve as a reference source for further in-depth surveys to understand the risk perception and social media communication across countries.

title: The SARS-CoV-2 epidemic: how the Italian public is being informed

title: Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study
abstract: To develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection.  A total of 52 patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images were enrolled from 5 designated hospitals in Ankang, Lishui, Zhenjiang, Lanzhou, and Linxia between January 23, 2020 and February 8, 2020. As of February 20, patients remained in hospital or with non-findings in CT were excluded. Therefore, 31 patients with 72 lesion segments were included in the final analysis.  CT radiomics models based on logistic regression (LR) and random forest (RF) were developed on features extracted from pneumonia lesions in training and inter-validation datasets. The predictive performance was further evaluated in test dataset on lung lobe- and patients-level. Main outcomes Short-term hospital stay (≤10 days) and long-term hospital stay (>10 days).  The CT radiomics models based on 6 second-order features were effective in discriminating short- and long-term hospital stay in patients with pneumonia associated with SARS-CoV-2 infection, with areas under the curves of 0.97 (95%CI 0.83-1.0) and 0.92 (95%CI 0.67-1.0) by LR and RF, respectively, in the test dataset. The LR model showed a sensitivity and specificity of 1.0 and 0.89, and the RF model showed similar performance with sensitivity and specificity of 0.75 and 1.0 in test dataset.  The machine learning-based CT radiomics models showed feasibility and accuracy for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection.

title: Developing WHO rapid advice guidelines in the setting of a public health emergency
abstract: We describe newly established guidance for guideline developers at the World Health Organization (WHO) on the process and procedures for developing a rapid advice guideline in the context of a public health emergency (e.g., the 2014 Ebola epidemic).  We based our approach on established rapid review methods, which were incorporated into existing WHO guideline development processes. Guidance was further informed by in-depth discussions of issues related to rapid guideline development with WHO staff (n = 6), who oversee the Organization's response to emergencies.  We discuss criteria for considering if a rapid advice guideline is appropriate and feasible and outline the roles of various contributors across the phases of development. Further, we describe the methods and steps involved in performing rapid reviews, which are more fluid and iterative than for a standard guideline process. In general, rapid advice guidelines involve a shorter timeline, narrower scope, and the use of abbreviated methods for the evidence review.  Important differences exist between developing a standard guideline and a rapid advice guideline. However, the core principles for WHO guidelines apply to rapid advice guidelines including minimizing bias, applying transparent processes and the use of explicit methods.

title: Social media as a tool for scientific updating at the time of COVID pandemic
abstract: In the face of the rapid evolution of the COVID-19 pandemic, healthcare professionals on the frontline are in urgent need of frequent updates in the accomplishment of their practice. Hence, clinicians started to search for prompt, valid information on sources parallel to academic journals publications. Aim of this work is to investigate the extent of this phenomenon. We administered an anonymous online cross-sectional survey to 645 Italian clinicians. 369 questionnaires were returned. 19,5% (n=72) of respondents were younger than 30 years-old; 49,3% (n=182) worked in Infectious Diseases, Internal Medicine or Respiratory Medicine departments, 11.5% (n=42) in Intensive Care Unit and 7.4% (n=27) were general practitioner. 70% (n=261) of respondents reported that their use of social media to seek medical information increased during the pandemic. 39.3% (n = 145) consistently consulted Facebook groups and 53.1% (n = 196) Whatsapp chats. 47% (n = 174) of respondents reported that information shared on social media had a consistent impact on their daily practice. In the present study, we found no difference in social media usage between age groups or medical specialties. Given the urgent need for scientific update in face of the present health emergency, these findings may help understanding how clinicians access new evidences and implement them in their daily practice.

title: Canadian Geriatrics Society COVID-19 Recommendations for Older Adults. What Do Older Adults Need To Know?

title: The Role of Data Registries in the Time of COVID-19.
abstract: On April 1, 2020, the confirmed global burden of COVID-19 was more than 900,000 with 46,413 deaths. Despite worldwide calls for social distancing and containment, the incidence of the disease continues to increase. COVID-19 is a respiratory tract infection caused by the novel coronavirus (SARS-CoV2). Preliminary analyses from the Chinese Center for Disease Control and Prevention indicated an overall case fatality rate (CFR) of 2.3%; however the CFR was higher in older adults (14.8% in those 80± years) and 49% of all critical cases. Those with preexisting conditions (cardiovascular disease (CVD), diabetes, chronic respiratory disease, hypertension, and cancer) also had higher CRFs. In the United States, 116 million adults have hypertension, 26 million US adults have diabetes mellitus, 9% have preexisting CVD and may therefore be at greater risk of complications or adverse health outcomes from COVID-19 infection.

title: Clinical Characteristics of Two Human to Human Transmitted Coronaviruses: Corona Virus Disease 2019 versus Middle East Respiratory Syndrome Coronavirus.
abstract: After the outbreak of the middle east respiratory syndrome (MERS) worldwide in 2012. Currently, a novel human coronavirus has caused a major disease outbreak, and named corona virus disease 2019 (COVID-19). The emergency of MRES-COV and COVID-19 has caused global panic and threatened health security. Unfortunately, the similarities and differences between the two coronavirus diseases remain to be unknown. The aim of this study, therefore, is to perform a systematic review to compare epidemiological, clinical and laboratory features of COVID-19 and MERS-COV population. We searched PubMed, EMBASE and Cochrane Register of Controlled Trials database to identify potential studies reported COVID-19 or MERS-COV. Epidemiological, clinical and laboratory outcomes, the admission rate of intensive cure unit (ICU), discharge rate and fatality rate were evaluated using GraphPad Prism software. Thirty-two studies involving 3770 patients (COVID-19 = 1062, MERS-COV = 2708) were included in this study. The present study revealed that compared with COVID-19 population, MERS-COV population had a higher rate of ICU admission, discharge and fatality and longer incubation time. It pointed out that fever, cough and generalised weakness and myalgia were main clinical manifestations of both COVID-19 and MERS-COV, whereas ARDS was main complication. The most effective drug for MERS-COV is ribavirin and interferon.

title: Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies
abstract: The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally-determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the 120 available SARS-CoV-2 sequences (as of 21 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.

title: Black, Asian and Minority Ethnic groups in England are at increased risk of death from COVID-19: indirect standardisation of NHS mortality data
abstract: International and UK data suggest that Black, Asian and Minority Ethnic (BAME) groups are at increased risk of infection and death from COVID-19. We aimed to explore the risk of death in minority ethnic groups in England using data reported by NHS England.  We used NHS data on patients with a positive COVID-19 test who died in hospitals in England published on 28th April, with deaths by ethnicity available from 1st March 2020 up to 5pm on 21 April 2020. We undertook indirect standardisation of these data (using the whole population of England as the reference) to produce ethnic specific standardised mortality ratios (SMRs) adjusted for age and geographical region.  The largest total number of deaths in minority ethnic groups were Indian (492 deaths) and Black Caribbean (460 deaths) groups. Adjusting for region we found a lower risk of death for White Irish (SMR 0.52; 95%CIs 0.45-0.60) and White British ethnic groups (0.88; 95%CIs 0.86-0.0.89), but increased risk of death for Black African (3.24; 95%CIs 2.90-3.62), Black Caribbean (2.21; 95%CIs 2.02-2.41), Pakistani (3.29; 95%CIs 2.96-3.64), Bangladeshi (2.41; 95%CIs 1.98-2.91) and Indian (1.70; 95%CIs 1.56-1.85) minority ethnic groups.  Our analysis adds to the evidence that BAME people are at increased risk of death from COVID-19 even after adjusting for geographical region. We believe there is an urgent need to take action to reduce the risk of death for BAME groups and better understand why some ethnic groups experience greater risk. Actions that are likely to reduce these inequities include ensuring adequate income protection (so that low paid and zero-hours contract workers can afford to follow social distancing recommendations), reducing occupational risks (such as ensuring adequate personal protective equipment), reducing barriers in accessing healthcare and providing culturally and linguistically appropriate public health communications.

title: The association between ischaemia-modified albumin levels in umbilical vein and intrauterine growth restriction.
abstract: Intrauterine growth restriction (IUGR) is one of the most common problems in obstetrics. Ischaemia-modified albumin (IMA), a product deriving from albumin as a result of the modification by oxidative free radicals in response to hypoxia, was previously used as a marker of ischaemia in acute coronary syndrome. We performed this study to determine whether umbilical venous IMA levels are associated with IUGR. A total of 40 pregnancies with IUGR were compared with 40 of normal fetal development. Blood samples were obtained from the umbilical vein after delivery. IMA levels in the IUGR group were higher than in the control group (78.74 ± 6.87 vs 74.43 ± 7.84 U/ml, respectively, p = 0.011). An elevated IMA level was associated with IUGR (OR: 1.079, 95% CI: 1.000-1.163, p = 0.049). We suggest that IMA, which was formerly proved to arise in ischaemic conditions, may also be a valuable marker in perinatal hypoxia and IUGR detection.

title: Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks
abstract: The 2019 novel coronavirus (COVID-19), with a starting point in China, has spread rapidly among people living in other countries, and is approaching approximately 12,245,417 cases worldwide according to the statistics of European Centre for Disease Prevention and Control. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, three different convolutional neural network based models (ResNet50, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia infected patient using chest X-ray radiographs. ROC analyses and confusion matrices by these three models are given and analyzed using 5-fold cross validation. Considering the performance results obtained, it is seen that the pre-trained ResNet50 model provides the highest classification performance with 98% accuracy among other two proposed models (97% accuracy for InceptionV3 and 87% accuracy for Inception-ResNetV2).

title: Extracting COVID-19 Events from Twitter
abstract: We present a corpus of 7,500 tweets annotated with COVID-19 events, including positive test results, denied access to testing, and more. We show that our corpus enables automatic identification of COVID-19 events mentioned in Twitter with text spans that fill a set of pre-defined slots for each event. We also present analyses on the self-reporting cases and user's demographic information. We will make our annotated corpus and extraction tools available for the research community to use upon publication at https://github.com/viczong/extract_COVID19_events_from_Twitter

title: The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application
abstract: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 , which has important implications for surveillance and control activities.  To estimate the length of the incubation period of COVID-19 and describe its public health implications.  Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020.  News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China.  Persons with confirmed SARS-CoV-2 infection outside Hubei province, China.  Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization.  There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.  Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases.  This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases.  U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

title: Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
abstract: "Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", the novel coronavirus, is responsible for the ongoing worldwide pandemic. "World Health Organization (WHO)" assigned an "International Classification of Diseases (ICD)" code-"COVID-19"-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with "Middle East Respiratory Syndrome (MERS-CoV)", "Severe Acute Respiratory Syndrome (SARS-CoV)", and now with this new virus, namely "SARS-CoV-2", or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a "spread factor (f)", and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in "ourworldindata.org (Oxford University database)", and newly simulated datasets, following the analysis of different univariate "Long Short Term Memory (LSTM)" models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months - January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.

title: TICO-19: the Translation Initiative for Covid-19
abstract: The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations the means by which they can protect themselves. To this end, the collaborators forming the Translation Initiative for COvid-19 (TICO-19) have made test and development data available to AI and MT researchers in 35 different languages in order to foster the development of tools and resources for improving access to information about COVID-19 in these languages. In addition to 9 high-resourced,"pivot"languages, the team is targeting 26 lesser resourced languages, in particular languages of Africa, South Asia and South-East Asia, whose populations may be the most vulnerable to the spread of the virus. The same data is translated into all of the languages represented, meaning that testing or development can be done for any pairing of languages in the set. Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.

title: The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review
abstract: Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance.  This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods.  A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria.  Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias.  IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population’s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

title: High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa
abstract: It has been reported that ACE2 is the main host cell receptor of 2019-nCoV and plays a crucial role in the entry of virus into the cell to cause the final infection. To investigate the potential route of 2019-nCov infection on the mucosa of oral cavity, bulk RNA-seq profiles from two public databases including The Cancer Genome Atlas (TCGA) and Functional Annotation of The Mammalian Genome Cap Analysis of Gene Expression (FANTOM5 CAGE) dataset were collected. RNA-seq profiling data of 13 organ types with para-carcinoma normal tissues from TCGA and 14 organ types with normal tissues from FANTOM5 CAGE were analyzed in order to explore and validate the expression of ACE2 on the mucosa of oral cavity. Further, single-cell transcriptomes from an independent data generated in-house were used to identify and confirm the ACE2-expressing cell composition and proportion in oral cavity. The results demonstrated that the ACE2 expressed on the mucosa of oral cavity. Interestingly, this receptor was highly enriched in epithelial cells of tongue. Preliminarily, those findings have explained the basic mechanism that the oral cavity is a potentially high risk for 2019-nCoV infectious susceptibility and provided a piece of evidence for the future prevention strategy in dental clinical practice as well as daily life.

title: Epidemiological characteristics of novel coronavirus infection: A statistical analysis of publicly available case data
abstract: Following the first report of coronavirus disease 2019 (COVID-19) in Sapporo City, Hokkaido Prefecture, Japan on 14 February 2020, a surge of cases was observed in Hokkaido during February and March. As of 6 March, 90 cases were diagnosed in Hokkaido. Unfortunately, many infected persons may not have been recognized as cases due to having mild or no symptoms. We therefore estimated the actual number of COVID-19 cases in (i) Hokkaido Prefecture and (ii) Sapporo City using data on cases diagnosed outside these areas. The estimated cumulative incidence in Hokkaido as of 27 February was 2297 cases (95% confidence interval [CI]: 382, 7091) based on data on travelers outbound from Hokkaido. The cumulative incidence in Sapporo City as of 28 February was estimated at 2233 cases (95% CI: 0, 4893) based on the count of confirmed cases within Hokkaido. Both approaches resulted in similar estimates, indicating higher incidence of infections in Hokkaido than were detected by the surveillance system. This quantification of the gap between detected and estimated cases can help inform public health response as it provides insight into the possible scope of undetected transmission.

title: Differential Diagnosis of Common Acute Toxicologic Versus Nontoxicologic Illness
abstract: This table outlines common toxicologic versus nontoxicologic rule outs based on clinical abnormalities seen in an acutely ill animal. The purpose is to provide an initial guideline for considering toxicologic versus nontoxicologic rule outs when a patient is presented to a practicing veterinarian. Major clinical abnormalities followed by common toxicologic rule outs and non-toxicologic rule outs have been listed so that practicing veterinarians can narrow down an etiology quickly. Based on history, physical examination findings, and blood work changes, once a reasonable etiology has been narrowed down or established, the reader is encouraged to review a more detailed discussion on management of the particular poisoning or disease listed in this or other references.

title: Large Arabic Twitter Dataset on COVID-19
abstract: The 2019 coronavirus disease (COVID-19), emerged late December 2019 in China, is now rapidly spreading across the globe. At the time of writing this paper, the number of global confirmed cases has passed two millions and half with over 180,000 fatalities. Many countries have enforced strict social distancing policies to contain the spread of the virus. This have changed the daily life of tens of millions of people, and urged people to turn their discussions online, e.g., via online social media sites like Twitter. In this work, we describe the first Arabic tweets dataset on COVID-19 that we have been collecting since January 1st, 2020. The dataset would help researchers and policy makers in studying different societal issues related to the pandemic. Many other tasks related to behavioral change, information sharing, misinformation and rumors spreading can also be analyzed.

title: A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic
abstract: Proactive management of an Infodemic that grows faster than the underlying epidemic is a modern-day challenge. This requires raising awareness and sensitization with the correct information in order to prevent and contain outbreaks such as the ongoing COVID-19 pandemic. Therefore, there is a fine balance between continuous awareness-raising by providing new information and the risk of misinformation. In this work, we address this gap by creating a life-long learning application that delivers authentic information to users in Hindi and English, the most widely used languages in India. It does this by matching sources of verified and authentic information such as the WHO reports against daily news by using machine learning and natural language processing. It delivers the narrated content in Hindi by using state-of-the-art text to speech engines. Finally, the approach allows user input for continuous improvement of news feed relevance daily. We demonstrate this approach for Water, Sanitation, Hygiene information for containment of the COVID-19 pandemic. Thirteen combinations of pre-processing strategies, word-embeddings, and similarity metrics were evaluated by eight human users via calculation of agreement statistics. The best performing combination achieved a Cohen's Kappa of 0.54 and was deployed as On AIr, WashKaro's AI-powered back-end. We introduced a novel way of contact tracing, deploying the Bluetooth sensors of an individual's smartphone and automatic recording of physical interactions with other users. Additionally, the application also features a symptom self-assessment tool based on WHO-approved guidelines, human-curated and vetted information to reach out to the community as audio-visual content in local languages.

title: Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence
abstract: The current form of severe acute respiratory syndrome called coronavirus disease 2019 (COVID19) caused by a coronavirus (SARSCoV2) is a major global health problem. The aim of our study was to use the official data and predict the possible outcomes of the COVID 19 pandemic using artificial intelligence (AI) based RNNs (Recurrent Neural Networks), then compare and validate the predicted and observed data.  We used the publicly available datasets of World Health Organization and Johns Hopkins University to create the training dataset, then have used recurrent neural networks (RNNs) with gated recurring units (Long Short Term Memory: LSTM units) to create 2 Prediction Models. Information collected in the first t time steps were aggregated with a fully connected (dense) neural network layer and a consequent regression output layer to determine the next predicted value. We used root mean squared logarithmic errors (RMSLE) to compare the predicted and observed data, then recalculated the predictions again.  The result of our study underscores that the COVID19 pandemic is probably a propagated source epidemic, therefore repeated peaks on the epidemic curve (rise of the daily number of the newly diagnosed infections) are to be anticipated. The errors between the predicted and validated data and trends seems to be low.  The influence of this pandemic is great worldwide, impact our everyday lifes. Especially decision makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI based predictions might be useful tools for predictions and the models can be recalculated according to the new observed data, to get more precise forecast of the pandemic.

title: TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
abstract: TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic. One of the key characteristics of pandemic search is the accelerated rate of change: the topics of interest evolve as the pandemic progresses and the scientific literature in the area explodes. The COVID-19 pandemic provides an opportunity to capture this progression as it happens. COVID, in creating a test collection around COVID-19 literature, is building infrastructure to support new research and technologies in pandemic search.

title: Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study
abstract: A range of public health measures have been implemented to suppress local transmission of coronavirus disease 2019 (COVID-19) in Hong Kong. We examined the effect of these interventions and behavioural changes of the public on the incidence of COVID-19, as well as on influenza virus infections, which might share some aspects of transmission dynamics with COVID-19.  We analysed data on laboratory-confirmed COVID-19 cases, influenza surveillance data in outpatients of all ages, and influenza hospitalisations in children. We estimated the daily effective reproduction number (R(t)) for COVID-19 and influenza A H1N1 to estimate changes in transmissibility over time. Attitudes towards COVID-19 and changes in population behaviours were reviewed through three telephone surveys done on Jan 20–23, Feb 11–14, and March 10–13, 2020.  COVID-19 transmissibility measured by R(t) has remained at approximately 1 for 8 weeks in Hong Kong. Influenza transmission declined substantially after the implementation of social distancing measures and changes in population behaviours in late January, with a 44% (95% CI 34–53%) reduction in transmissibility in the community, from an estimated R(t) of 1·28 (95% CI 1·26–1·30) before the start of the school closures to 0·72 (0·70–0·74) during the closure weeks. Similarly, a 33% (24–43%) reduction in transmissibility was seen based on paediatric hospitalisation rates, from an R(t) of 1·10 (1·06–1·12) before the start of the school closures to 0·73 (0·68–0·77) after school closures. Among respondents to the surveys, 74·5%, 97·5%, and 98·8% reported wearing masks when going out, and 61·3%, 90·2%, and 85·1% reported avoiding crowded places in surveys 1 (n=1008), 2 (n=1000), and 3 (n=1005), respectively.  Our study shows that non-pharmaceutical interventions (including border restrictions, quarantine and isolation, distancing, and changes in population behaviour) were associated with reduced transmission of COVID-19 in Hong Kong, and are also likely to have substantially reduced influenza transmission in early February, 2020.  Health and Medical Research Fund, Hong Kong.

title: Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms
abstract: India has taken strong and early public health measures for arresting the spread of the COVID-19 epidemic. With only 536 COVID-19 cases and 11 fatalities, India - a democracy of 1.34 billion people - took the historic decision of a 21-day national lockdown on March 25. The lockdown was further extended to May 3, soon after the analysis of this paper was completed.  To study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 cases in India compared to other less severe non-pharmaceutical interventions using epidemiological forecasting models and Bayesian estimation algorithms; to compare effects of hypothetical durations of lockdown from an epidemiological perspective; to study alternative explanations for slower growth rate of the virus outbreak in India, including exploring the association of the number of cases and average monthly temperature; and finally, to outline the pivotal role of reliable and transparent data, reproducible data science methods, tools and products as we reopen the country and prepare for a post lock-down phase of the pandemic.  We use the daily data on the number of COVID-19 cases, of recovered and of deaths from March 1 until April 7, 2020 from the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Additionally, we use COVID-19 incidence counts data from Kaggle and the monthly average temperature of major cities across the world from Wikipedia.  The current time-series data on daily proportions of cases and removed (recovered and death combined) from India are analyzed using an extended version of the standard SIR (susceptible, infected, and removed) model. The eSIR model incorporates time-varying transmission rates that help us predict the effect of lockdown compared to other hypothetical interventions on the number of cases at future time points. A Markov Chain Monte Carlo implementation of this model provided predicted proportions of the cases at future time points along with credible intervals (CI).  Our predicted cumulative number of COVID-19 cases in India on April 30 assuming a 1-week delay in people's adherence to a 21-day lockdown (March 25 - April 14) and a gradual, moderate resumption of daily activities after April 14 is 9,181 with upper 95% CI of 72,245. In comparison, the predicted cumulative number of cases under "no intervention" and "social distancing and travel bans without lockdown" are 358 thousand and 46 thousand (upper 95% CI of nearly 2.3 million and 0.3 million) respectively. An effective lockdown can prevent roughly 343 thousand (upper 95% CI 1.8 million) and 2.4 million (upper 95% CI 38.4 million) COVID-19 cases nationwide compared to social distancing alone by May 15 and June 15, respectively. When comparing a 21-day lockdown with a hypothetical lockdown of longer duration, we find that 28-, 42-, and 56-day lockdowns can approximately prevent 238 thousand (upper 95% CI 2.3 million), 622 thousand (upper 95% CI 4.3 million), 781 thousand (upper 95% CI 4.6 million) cases by June 15, respectively. We find some suggestive evidence that the COVID-19 incidence rates worldwide are negatively associated with temperature in a crude unadjusted analysis with Pearson correlation estimates [95% confidence interval] between average monthly temperature and total monthly incidence around the world being -0.185 [-0.548, 0.236] for January, -0.110 [-0.362, 0.157] for February, and -0.173 [-0.314, -0.026] for March.  The lockdown, if implemented correctly in the end, has a high chance of reducing the total number of COVID-19 cases in the short term, and buy India invaluable time to prepare its healthcare and disease monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for the best outcome. We cannot heavily rely on the hypothetical prevention governed by meteorological factors such as temperature based on current evidence. From an epidemiological perspective, a longer lockdown between 42-56 days is preferable. However, the lockdown comes at a tremendous price to social and economic health through a contagion process not dissimilar to that of the coronavirus itself. Data can play a defining role as we design post-lockdown testing, reopening and resource allocation strategies.  Our contribution to data science includes an interactive and dynamic app (covind19.org) with short- and long-term projections updated daily that can help inform policy and practice related to COVID-19 in India. Anyone can visualize the observed data for India and create predictions under hypothetical scenarios with quantification of uncertainties. We make our prediction codes freely available (https://github.com/umich-cphds/cov-ind-19) for reproducible science and for other COVID-19 affected countries to use them for their prediction and data visualization work.

title: Stability of the COVID-19 virus under wet, dry and acidic conditions
abstract: COVID-19 has become a pandemic and is spreading fast worldwide. The COVID-19 virus is transmitted mainly through respiratory droplets and close contact. However, the fecal-oral transmission of the virus has not been ruled out and it is important to ascertain how acidic condition in the stomach affects the infectivity of the virus. Besides, it is unclear how stable the COVID-19 virus is under dry and wet conditions. In the present study, we have shown that the COVID-19 virus is extremely infectious as manifested by the infection of Vero-E6 cells by one PFU (Plaque Forming Unit) of the virus. We then investigated the stability of the COVID-19 virus in wet, dry and acidic (pH2.2) environments at room temperature. Results showed that the COVID-19 virus could survive for three days in wet and dry environments, but the dry condition is less favorable for the survival of the virus. Our study also demonstrated that the COVID-19 virus at a relative high titer (1.2 x 103 PFU) exhibits a certain degree of tolerance to acidic environment at least for 60 minutes. When the virus titer was ≤1.0 x 103 PFU, acid treatment (pH2.2) for 30 or 60 minute resulted in virus inactivation. It suggests that the virus at a high concentration may survive in the acidic environment of the stomach. The finding of the present study will contribute to the control of the spread of the COVID-19 virus.

title: Social big data: Recent achievements and new challenges
abstract: Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media and big data paradigms.

title: Retinal pathology in multiple sclerosis: insight into the mechanisms of neuronal pathology

title: Case- fatality rate in COVID- 19 patients: A meta-analysis of publicly accessible database
abstract: A novel coronavirus was reported in Wuhan, China in December 2019 to cause severe acute respiratory symptoms (COVID- 19). In this meta-analysis, we estimated case fatality rate from COVID- 19 infection by random effect meta-analysis model with country level data. Publicly accessible web database WorldOMeter (https://www.worldometers.info/coronavirus/) was accessed on 24th March 2020 GMT and reported total number of cases, total death, active cases and seriously ill/ critically ill patients were retrieved. Primary outcome of this meta-analysis was case fatality rate defined by total number of deaths divided by total number of diagnosed cases. Pooled case fatality rate (95% CI) was 1.78 (1.34- 2.22) %. Between country heterogeneity was 0.018 (p<0.0001). Pooled estimate of composite poor outcome (95% CI) was 4.06 (3.24- 4.88) % at that point of time after exclusion of countries reported small number of cases. Pooled mortality rate (95% CI) was 33.97 (27.44- 40.49) % amongst closed cases (where patients have recovered or died) with. Meta regression analysis identified statistically significant association between health expenditure and mortality amongst closed cases (p=0.037).

title: India nudges to contain COVID-19 pandemic: a reactive public policy analysis using machine-learning based topic modelling
abstract: India locked down 1.3 billion people on March 25, 2020 in the wake of COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion, while the social costs are still unknown. This study investigated how government formed reactive policies to fight coronavirus across its policy sectors. Primary data was collected from the Press Information Bureau (PIB) in the form press releases of government plans, policies, programme initiatives and achievements. A text corpus of 260,852 words was created from 396 documents from the PIB. An unsupervised machine-based topic modelling using Latent Dirichlet Allocation (LDA) algorithm was performed on the text corpus. It was done to extract high probability topics in the policy sectors. The interpretation of the extracted topics was made through a nudge theoretic lens to derive the critical policy heuristics of the government. Results showed that most interventions were targeted to generate endogenous nudge by using external triggers. Notably, the nudges from the Prime Minister of India was critical in creating herd effect on lockdown and social distancing norms across the nation. A similar effect was also observed around the public health (e.g., masks in public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks), science and technology sector (e.g., diagnostic kits, robots and nano-technology), home affairs (e.g., surveillance and lockdown), urban (e.g. drones, GIS-tools) and education (e.g., online learning). A conclusion was drawn on leveraging these heuristics are crucial for lockdown easement planning.

title: Virus Adaptation by Manipulation of Host's Gene Expression
abstract: Viruses adapt to their hosts by evading defense mechanisms and taking over cellular metabolism for their own benefit. Alterations in cell metabolism as well as side-effects of antiviral responses contribute to symptoms development and virulence. Sometimes, a virus may spill over from its usual host species into a novel one, where usually will fail to successfully infect and further transmit to new host. However, in some cases, the virus transmits and persists after fixing beneficial mutations that allow for a better exploitation of the new host. This situation would represent a case for a new emerging virus. Here we report results from an evolution experiment in which a plant virus was allowed to infect and evolve on a naïve host. After 17 serial passages, the viral genome has accumulated only five changes, three of which were non-synonymous. An amino acid substitution in the viral VPg protein was responsible for the appearance of symptoms, whereas one substitution in the viral P3 protein the epistatically contributed to exacerbate severity. DNA microarray analyses show that the evolved and ancestral viruses affect the global patterns of host gene expression in radically different ways. A major difference is that genes involved in stress and pathogen response are not activated upon infection with the evolved virus, suggesting that selection has favored viral strategies to escape from host defenses.

title: Using informatics to guide public health policy during the COVID-19 pandemic in the USA.
abstract: Current and future pandemics will require informatics solutions to assess the risks, resources and policies to guide better public health decision-making.  2020) by applying biomedical informatics and data visualization tools to several public and federal government datasets, including analysis of the impact of statewide stay-at-home orders.  There were 2753.2 cases and 158.0 deaths per million residents, respectively, in the USA with variable distributions throughout divisions, regions and states. Forty-two states and Washington, DC, (84.3%) had statewide stay-at-home orders, with the remaining states having population-adjusted characteristics in the highest risk quartile.  Effective national preparedness requires clearly understanding states' ability to predict, manage and balance public health needs through all stages of a pandemic. This will require leveraging data quickly, correctly and responsibly into sound public health policies.

title: Machine Learning Maps Research Needs in COVID-19 Literature
abstract: Manually assessing the scope of the thousands of publications on the COVID-19 (coronavirus disease 2019) pandemic is an overwhelming task. Shortcuts through metadata analysis (e.g., keywords) assume that studies are properly tagged. However, machine learning approaches can rapidly survey the actual text of coronavirus abstracts to identify research overlap between COVID-19 and other coronavirus diseases, research hotspots, and areas warranting exploration. We propose a fast, scalable, and reusable framework to parse novel disease literature. When applied to the COVID-19 Open Research Dataset (CORD-19), dimensionality reduction suggested that COVID-19 studies to date are primarily clinical-, modeling- or field-based, in contrast to the vast quantity of laboratory-driven research for other (non-COVID-19) coronavirus diseases. Topic modeling also indicated that COVID-19 publications have thus far focused primarily on public health, outbreak reporting, clinical care, and testing for coronaviruses, as opposed to the more limited number focused on basic microbiology, including pathogenesis and transmission.

title: Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space Model
abstract: The outbreak of Coronavirus Disease 2019 (COVID-19) is an ongoing pandemic affecting over 200 countries and regions. Inference about the transmission dynamics of COVID-19 can provide important insights into the speed of disease spread and the effects of mitigation policies. We develop a novel Bayesian approach to such inference based on a probabilistic compartmental model using data of daily confirmed COVID-19 cases. In particular, we consider a probabilistic extension of the classical susceptible-infectious-recovered model, which takes into account undocumented infections and allows the epidemiological parameters to vary over time. We estimate the disease transmission rate via a Gaussian process prior, which captures nonlinear changes over time without the need of specific parametric assumptions. We utilize a parallel-tempering Markov chain Monte Carlo algorithm to efficiently sample from the highly correlated posterior space. Predictions for future observations are done by sampling from their posterior predictive distributions. Performance of the proposed approach is assessed using simulated datasets. Finally, our approach is applied to COVID-19 data from four states of the United States: Washington, New York, California, and Illinois. An R package BaySIR is made available at https://github.com/tianjianzhou/BaySIR for the public to conduct independent analysis or reproduce the results in this paper.

title: What we know so far: COVID-19 current clinical knowledge and research
abstract: In December 2019, health authorities in Wuhan, China, identified a cluster of pneumonia cases of unknown aetiology linked to the city's South China Seafood Market. Subsequent investigations revealed a novel coronavirus, SARS-CoV-2, as the causative agent now at the heart of a major outbreak. The rising case numbers have been accompanied by unprecedented public health action, including the wholesale isolation of Wuhan. Alongside this has been a robust scientific response, including early publication of the pathogen genome, and rapid development of highly specific diagnostics. This article will review the new knowledge of SARS-CoV-2 COVID-19 acute respiratory disease, and summarise its clinical features.

title: A Survey on Deep Transfer Learning and Edge Computing for Mitigating the COVID-19 Pandemic
abstract: Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The spreading and infection factors of this disease are very high. A huge number of people from most of the countries are infected within four months from its first report of appearance and it keeps spreading. The required systems are not ready up to some stages for any pandemic; therefore, mitigation with existing capacity becomes necessary. On the other hand, modern-era largely depends on Artificial Intelligence(AI) including Data Science, and Deep Learning(DL) is one of the current flag-bearer of these techniques. It could use to mitigate COVID-19 like pandemics in terms of stop spread, diagnosis of the disease, drug & vaccine discovery, treatment, and many more. But this DL requires large datasets as well as powerful computing resources. A shortage of reliable datasets of a running pandemic is a common phenomenon. So, Deep Transfer Learning(DTL) would be effective as it learns from one task and could work on another task. In addition, Edge Devices(ED) such as IoT, Webcam, Drone, Intelligent Medical Equipment, Robot, etc. are very useful in a pandemic situation. These types of equipment make the infrastructures sophisticated and automated which helps to cope with an outbreak. But these are equipped with low computing resources, so, applying DL is also a bit challenging; therefore, DTL also would be effective there. This article scholarly studies the potentiality and challenges of these issues. It has described relevant technical backgrounds and reviews of the related recent state-of-the-art. This article also draws a pipeline of DTL over Edge Computing as a future scope to assist the mitigation of any pandemic.

title: Pathology of infectious diseases: new agents, opportunistic, neglectable, emergent, reemergent diseases and why not super resistant nosocomial bacteria?

title: Caring for persons in detention suffering with mental illness during the Covid-19 outbreak

title: Paucity and disparity of publicly available sex-disaggregated data for the COVID-19 epidemic hamper evidence-based decision-making
abstract: COVID-19 has joined the long list of human disorders with sexually dimorphic expression. Increased lethality in men was evident in the first large reports from ChinaCDC and WHO-China, and the gender gap appeared even wider in the early Italian outbreak. Newspapers and scientific journals alike have commented on this finding and the preexisting conditions, biological processes, and gender role behavior differences that may underlie it. However, as for other diseases, and in spite of years of advocating for the collection of raw epidemiological data and the analysis of clinical trial data sets by sex, very little appeared to be released about sex differences in characteristics of the epidemics beyond infection and death rates, such as severity of disease, comorbidities, rate of recovery, length of hospital stay, or number of tests for the SARS-CoV-2 coronavirus. These data are critical not only for scientists to understand the pathophysiology of disease, but also to inform decision-making by countries and healthcare systems on how to prioritize testing and best allocate scarce resources and relief funds. Systematic analysis of official websites for the 20 countries and 6 US states reporting the highest number of cases on March 21, 2020, revealed a wide disparity in sex-disaggregated data made available to the public and scholars. Only a handful of the countries reported cases by sex separately. None of the other characteristics, including fatality rates, were stratified by sex at the time. Beyond suboptimal sex disaggregation, our analysis found a paucity of usable raw data sets and a generalized lack of standardization of captured data, making comparisons difficult. A second round of data capture in April found more complete, but even more disparate, information. Our analysis revealed a wide range of sex ratios among confirmed cases, which changed over time. In countries where a male-biased sex ratio was initially reported, the reported proportion of women among cases dramatically increased in under 3 weeks. In contrast, men were consistently over-represented in severe cases, intensive care admissions, and deaths. We also show that the sex ratio varies with age, with a complex pattern, reproduced across the 6 countries for which data were found. Accurate, peer-reviewed, statistical analysis of harmonized, sex-disaggregated data for other characteristics of epidemics, such as availability of testing, suspected source of infection, or comorbidities will be critical to understand where the observed disparities come from and to generate evidence-based recommendations for decision-making by institutions and governments around the world.

title: Profiling ACE2 expression in colon tissue of healthy adults and colorectal cancer patients by single-cell transcriptome analysis
abstract: A newly identified novel coronavirus (2019-nCoV) has caused numerous acute respiratory syndrome cases in Wuhan China from December 2019 to Feb 2020. Its fast spreading to other provinces in China and overseas is very likely causing a pandemic. Since the novel coronavirus has been reported to be capable of endangering thousands of lives, it is extremely important to find out how the coronavirus is transmitted in human organs. Apart from fever and respiratory complications, gastrointestinal symptoms are observed in some patients with 2019-nCoV but the significance remains undetermined. The cell receptor angiotensin covering enzyme II (ACE2), which is the major receptor of SARS-nCoV, has been reported to be a cellular entry receptor of 2019-nCoV as well. Here, to more precisely explore the potential pathogen transmission route of the 2019-nCoV infections in the gastrointestinal tract, we analyzed the ACE2 RNA expression profile in the colon tissue of healthy adults and colorectal cancer patients of our cohort and other databases. The data indicates that ACE2 is mainly expressed in epithelial cells of the colon. The expression of ACE2 is gradually increased from healthy control, adenoma to colorectal cancer patients in our cohort as well as in the external Asian datasets. According to the expression profile of ACE2 in colon epithelial cells, we speculate adenoma and colorectal cancer patients are more likely to be infected with 2019-nCoV than healthy people. Our data may provide a theoretical basis for the classification and management of future 2019-nCoV susceptibility people in clinical application.

title: Persistent infections support maintenance of a coronavirus in a population of Australian bats (Myotis macropus)
abstract: Understanding viral transmission dynamics within populations of reservoir hosts can facilitate greater knowledge of the spillover of emerging infectious diseases. While bat-borne viruses are of concern to public health, investigations into their dynamics have been limited by a lack of longitudinal data from individual bats. Here, we examine capture–mark–recapture (CMR) data from a species of Australian bat (Myotis macropus) infected with a putative novel Alphacoronavirus within a Bayesian framework. Then, we developed epidemic models to estimate the effect of persistently infectious individuals (which shed viruses for extensive periods) on the probability of viral maintenance within the study population. We found that the CMR data analysis supported grouping of infectious bats into persistently and transiently infectious bats. Maintenance of coronavirus within the study population was more likely in an epidemic model that included both persistently and transiently infectious bats, compared with the epidemic model with non-grouping of bats. These findings, using rare CMR data from longitudinal samples of individual bats, increase our understanding of transmission dynamics of bat viral infectious diseases.

title: Modelling the COVID-19 epidemics in Brasil: Parametric identification and public health measures influence
abstract: A SIRU-type epidemic model is proposed for the prediction of COVID-19 spreading within Brasil, and analyse the influence of public health measures on simulating the control of this infectious disease. Since the reported cases are typically only a fraction of the total number of the symptomatic infectious individuals, the model accounts for both reported and unreported cases. Also, the model allows for the time variation of both the transmission rate and the fraction of asymptomatic infectious that become reported symptomatic individuals, so as to reflect public health interventions, towards its control, along the course of the epidemic evolution. An analytical exponential behaviour for the accumulated reported cases evolution is assumed at the onset of the epidemy, for explicitly estimating initial conditions, while a Bayesian inference approach is adopted for parametric estimations employing the present direct problem model with the data from the known portion of the epidemics evolution, represented by the time series for the reported cases of infected individuals. The direct-inverse problem analysis is then employed with the actual data from China, with the first half been employed for the parametric estimation and the second half for validation of the predictive capability of the proposed approach. The full dataset for China is then employed in another parameter identification, including the average times that asymptomatic infectious individuals and that symptomatic individuals are infectious. Following this validation, the available data on reported cases in Brasil from February 15th till March 29th, 2020, is used for estimating parameters and then predict the epidemy evolution under these conditions. Finally, public health interventions are simulated, aimed at diminishing the effects of the disease spreading, by acting on both the transmission rate and the fraction of the total number of the symptomatic infectious individuals, considering time variable exponential behaviours for these two parameters, usually assumed constant in epidemic evolutions without intervention. It is demonstrated that a combination of actions to affect both parameters can have a much faster and effective result in the control of the epidemy dynamics.

title: A Tale of Two Communities: Characterizing Reddit Response to COVID-19 through /r/China_Flu and /r/Coronavirus
abstract: The COVID-19 pandemic has deeply impacted people's lives around the globe. During the extended lockdowns caused by the pandemic, online communities are crucial for people to access information and share experiences. In particular, two"new"communities have emerged on Reddit: /r/China_flu and /r/Coronavirus. By studying activities and users in these two communities, we provide a characterization of people's responses to COVID-19 on Reddit. First, we find that user activity peaks around March 17, when the World Health Organization (WHO) announced COVID-19 as a pandemic. Shortly after that, the activity levels of both communities have been declining week by week. We further illustrate the central role of these two communities in the emergence of COVID-related communities. Second, we study the differences between these two communities. /r/Coronavirus is recommended as the official community for COVID-19 on Reddit, while /r/China_flu adopts a loose moderation practice. As a result, we observe that these two communities are gradually growing apart and more extremism is being found in /r/China_flu. Finally, we examine the spillover effect of the COVID-19 pandemic on user activity across the entire Reddit platform. Our results show significant changes in user activities outside COVID-related communities. In subreddits related to finance, food, and countries/cities, user activity is recovering to the pre-pandemic level in late April and May as countries reopen, but subreddits related to travel and sports remain highly impacted and show lower activity levels than the pre-pandemic period. Our work highlights the strength of Reddit as a source for understanding public reactions to COVID-19 and the importance of content moderation on the Internet during a pandemic.

title: Roles of meteorological conditions in COVID-19 transmission on a worldwide scale
abstract: The novel coronavirus (SARS-CoV-2/ 2019-nCoV) identified in Wuhan, China, in December 2019 has caused great damage to public health and economy worldwide with over 140,000 infected cases up to date. Previous research has suggested an involvement of meteorological conditions in the spread of droplet-mediated viral diseases, such as influenza. However, as for the recent novel coronavirus, few studies have discussed systematically about the role of daily weather in the epidemic transmission of the virus. Here, we examine the relationships of meteorological variables with the severity of the outbreak on a worldwide scale. The confirmed case counts, which indicates the severity of COVID-19 spread, and four meteorological variables, i.e., air temperature, relative humidity, wind speed, and visibility, were collected daily between January 20 and March 11 (52 days) for 430 cities and districts all over China, 21 cities/ provinces in Italy, 21 cities/ provinces in Japan, and 51 other countries around the world. Four different time delays of weather (on the day, 3 days ago, 7 days ago, and 14 days ago) as to the epidemic situation were taken for modeling and we finally chose the weather two weeks ago to model against the daily epidemic situation as its correlated with the outbreak best. Taken Chinese cities as a discovery dataset, it was suggested that temperature, wind speed, and relative humidity combined together could best predict the epidemic situation. The meteorological model could well predict the outbreak around the world with a high correlation (r2>0.6) with the real data. Using this model, we further predicted the possible epidemic situation in the future 12 days in several high-latitude cities with potential outbreak. This model could provide more information for government's future decisions on COVID-19 outbreak control.

title: Assessing the Effects of Metropolitan-Wide Quarantine on the Spread of COVID-19 in Public Space and Households
abstract: Hubei province in China has completed cycle of quarantine-resumption in 23rd Janauary and 8th April 2020, providing a unique opportunity as for now to assess its intervention impact and the pattern of SARS-COV-2 transmission during the quarantine period. In this study, we evaluate the impact of the metropolitan-wide quarantine on the trend and transmission route of the COVID-19 epidemic in Hubei, China. The intervention reduces more than 70% of new infections in both households and the public space, as well as the deaths caused by COVID-19 pneumonia. Household transmission is the dominant route of disease spread regardless of quarantine. This will provide important evidence and scientific insights to other worldwide countries that are currently under quarantine.

title: Estimating pre-symptomatic transmission of COVID-19: a secondary analysis using published data
abstract: Understanding the extent of virus transmission that can occur before symptom onset is vital for targeting control measures against the global pandemic of COVID-19.  Estimation of (1) the proportion of pre-symptomatic transmission of COVID-19 that can occur and (2) timing of transmission relative to symptom onset.  Secondary analysis of published data  Meta-analysis of COVID-19 incubation period and a rapid systematic review of serial interval and generation time, which are published separately.  Simulations were generated of incubation period and of serial interval or generation time. From these, transmission times relative to symptom onset were calculated and the proportion of pre-symptomatic transmission was estimated.  A total of 23 estimates of serial interval and five estimates of generation time from 17 publications were included. These came from nine different data source categories (presented here in descending order of the proportion of pre-symptomatic transmission): Hong Kong, Tianjin, pooled data from Hong Kong and Shenzhen, Singapore, Mainland China excluding Hubei, mixed sources, Shenzhen, northern Italy and Wuhan. Transmission time relative to symptom onset ranged from a mean of 2.05 days before symptom onset for Hong Kong to 1.72 days after symptom onset for Wuhan. Proportion of pre-symptomatic transmission ranged from 33.7% in Wuhan to 72.7% in Hong Kong. Based on individual estimates, transmission time relative to symptom onset ranged from mean of 2.95 days before symptom onset to 1.72 days after symptom onset and proportion of pre-symptomatic transmission ranged from 33.7% to 79.9%. Simple unweighted pooling of estimates based on serial intervals resulted in a mean time of transmission of 0.67 days before symptoms, and an estimated 56.1% of transmission occurring in the pre-symptomatic period.  Contact rates between symptomatic infectious and susceptible people are likely to influence the proportion of pre-symptomatic transmission. There is substantial potential for pre-symptomatic transmission of COVID-19 in a range of different contexts. Our work suggests that transmission of SARS-CoV-2 is most likely in the day before symptom onset whereas estimates suggesting most pre-symptomatic transmission highlighted a mean transmission times almost 3 days before symptom onset. These findings highlight the urgent need for extremely rapid and effective case detection, contact tracing and quarantine measures if strict social distancing measures are to be eased.

title: A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19
abstract: In the past few months, several works were published in regards to the dynamics and early detection of COVID-19 via mathematical modeling and Artificial intelligence (AI). The aim of this work is to provide the research community with comprehensive overview of the methods used in these studies as well as a compendium of available open source datasets in regards to COVID-19. In all, 61 journal articles, reports, fact sheets, and websites dealing with COVID-19 were studied and reviewed. It was found that most mathematical modeling done were based on the Susceptible-Exposed-Infected-Removed (SEIR) and Susceptible-infected-recovered (SIR) models while most of the AI implementations were Convolutional Neural Network (CNN) on X-ray and CT images. In terms of available datasets, they include aggregated case reports, medical images, management strategies, healthcare workforce, demography, and mobility during the outbreak. Both Mathematical modeling and AI have both shown to be reliable tools in the fight against this pandemic. Several datasets concerning the COVID-19 have also been collected and shared open source. However, much work is needed to be done in the diversification of the datasets. Other AI and modeling applications in healthcare should be explored in regards to this COVID-19.

title: Understand Research Hotspots Surrounding COVID-19 and Other Coronavirus Infections Using Topic Modeling
abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that causes severe respiratory illness in humans, which eventually results in the current outbreak of novel coronavirus disease (COVID-19) around the world. The research community is interested to know what are the hotspots in coronavirus (CoV) research and how much is known about COVID-19. This study aimed to evaluate the characteristics of publications involving coronaviruses as well as COVID-19 by using a topic modeling analysis.  We extracted all abstracts and retained the most informative words from the COVID-19 Open Research Dataset, which contains all the 35,092 pieces of coronavirus related literature published up to March 20, 2020. Using Latent Dirichlet Allocation modeling, we trained an eight-topic model from the corpus. We then analyzed the semantic relationships between topics and compared the topic distribution between COVID-19 and other CoV infections.  Eight topics emerged overall: clinical characterization, pathogenesis research, therapeutics research, epidemiological study, virus transmission, vaccines research, virus diagnostics, and viral genomics. It was observed that COVID-19 research puts more emphasis on clinical characterization, epidemiological study, and virus transmission at present. In contrast, topics about diagnostics, therapeutics, vaccines, genomics and pathogenesis only accounted for less than 10% or even 4% of all the COVID-19 publications, much lower than those of other CoV infections.  These results identified knowledge gaps in the area of COVID-19 and offered directions for future research.  COVID-19, coronavirus, topic modeling, hotspots, text mining

title: Survival analysis of hospital length of stay of novel coronavirus (COVID-19) pneumonia patients in Sichuan, China
abstract: Allocation of medical resource is essential to a strong public health system in response to COVID-19. Analysis of confirmed COVID-19 patients' hospital length of stay in Sichuan can be informative to decision-making in other regions of the world.  A retrospective cross-sectional study.  Data from confirmed COVID-19 cases in Sichuan Province were obtained from the National Notifiable Diseases Reporting System (NNDRS) and field survey. We collected information on demographic, epidemiological, clinical characteristics, and the length of hospital stay for confirmed patients. We conducted an exploratory analysis using adjusted multivariate cox-proportional models.  A total of 538 confirmed patients of COVID-19 infection in Sichuan Province from January to March 2020. Outcome measure The length of hospital stay after admissions for confirmed patients.  From January 16, 2020 to March 4, 2020, 538 human cases of COVID-19 infection were laboratory-confirmed, and were hospitalized for treatment. Among these, 271 (50%) were 45 years of age or above, 285 (53%) were male, 450 (84%) were considered as having mild symptoms. The median hospital length of stay was 19 days (interquartile range (IQR): 14-23, Range: 3-41). Adjusted multivariate analysis showed that longer hospital length of stay was associated with factors aged 45 and over (HR: 0.74, 95% CI: 0.60-0.91), those admitted to provincial hospital (HR: 0.73, 95% CI: 0.54-0.99), and those with serious illness (HR: 0.66, 95% CI: 0.48-0.90); living in areas with more than 5.5 healthcare workers per 1000 population (HR: 1.32, 95% CI: 1.05-1.65) was associated with shorter hospital length of stay. There was no gender difference.  Preparation control measures of COVID-19 should involve the allocation of sufficient medical resources, especially in areas with older vulnerable populations and in areas that lack basic medical resources.

title: Curation of viral genomes: challenges, applications and the way forward
abstract: Whole genome sequence data is a step towards generating the 'parts list' of life to understand the underlying principles of Biocomplexity. Genome sequencing initiatives of human and model organisms are targeted efforts towards understanding principles of evolution with an application envisaged to improve human health. These efforts culminated in the development of dedicated resources. Whereas a large number of viral genomes have been sequenced by groups or individuals with an interest to study antigenic variation amongst strains and species. These independent efforts enabled viruses to attain the status of 'best-represented taxa' with the highest number of genomes. However, due to lack of concerted efforts, viral genomic sequences merely remained as entries in the public repositories until recently.  VirGen is a curated resource of viral genomes and their analyses. Since its first release, it has grown both in terms of coverage of viral families and development of new modules for annotation and analysis. The current release (2.0) includes data for twenty-five families with broad host range as against eight in the first release. The taxonomic description of viruses in VirGen is in accordance with the ICTV nomenclature. A well-characterised strain is identified as a 'representative entry' for every viral species. This non-redundant dataset is used for subsequent annotation and analyses using sequenced-based Bioinformatics approaches. VirGen archives precomputed data on genome and proteome comparisons. A new data module that provides structures of viral proteins available in PDB has been incorporated recently. One of the unique features of VirGen is predicted conformational and sequential epitopes of known antigenic proteins using in-house developed algorithms, a step towards reverse vaccinology.  Structured organization of genomic data facilitates use of data mining tools, which provides opportunities for knowledge discovery. One of the approaches to achieve this goal is to carry out functional annotations using comparative genomics. VirGen, a comprehensive viral genome resource that serves as an annotation and analysis pipeline has been developed for the curation of public domain viral genome data . Various steps in the curation and annotation of the genomic data and applications of the value-added derived data are substantiated with case studies.

title: Testing the association between blood type and COVID-19 infection, intubation, and death
abstract: The rapid global spread of the novel coronavirus SARS-CoV-2 has strained existing healthcare and testing resources, making the identification and prioritization of individuals most at-risk a critical challenge. A recent study of patients in China discovered an association between ABO blood type and SARS-CoV-2 infection status by comparing COVID-19 patients with the general population. Whether blood type is associated with increased COVID-19 morbidity or mortality remains unknown. We used observational healthcare data on 1559 individuals tested for SARS-CoV-2 (682 COV+) with known blood type in the New York Presbyterian (NYP) hospital system to assess the association between ABO+Rh blood type and SARS-CoV-2 infection status, intubation, and death. We found a higher proportion of blood group A and a lower proportion of blood group O among COV+ patients compared to COV−, though in both cases the result is significant only in Rh positive blood types. We show that the effect of blood type is not explained by risk factors we considered (age, sex, hypertension, diabetes mellitus, overweight status, and chronic cardiovascular and lung disorders). In a meta-analysis of NYP data with previously-reported data from China, we find enrichment for A and B and depletion of O blood groups among COVID-19 patients compared to the general population. Our data do not provide strong evidence of associations between blood group and intubation or death among COVID-19 patients. In this preliminary observational study of data currently being collected during the outbreak, we find new evidence of associations between B, AB, and Rh blood groups and COVID-19 and further evidence of recently-discovered associations between A and O blood groups and COVID-19.

title: Getting Insights from a Large Corpus of Scientific Papers on Specialisted Comprehensive Topics -- the Case of COVID-19
abstract: COVID-19 is one of the most important topic these days, specifically on search engines and news. While fake news are easily shared, scientific papers are reliable sources where information can be extracted. With about 24,000 scientific publications on COVID-19 and related research on PUBMED, automatic computer-assisted analysis is required. In this paper, we develop two methodologies to get insights on specific sub-topics of interest and latest research sub-topics. They rely on natural language processing and graph-based visualizations. We run these methodologies on two cases: the virus origin and the uses of existing drugs.

title: Endovascular treatment of middle cerebral artery aneurysms with flow modification with the use of the pipeline embolization device.
abstract: The Pipeline Embolization Device was reported to be safe and effective in the treatment of sidewall aneurysms, preserving the patency of the vessels covered by the construct. However, to date, the safety and efficacy of this device in treating bifurcation aneurysms remains unknown. We report our preliminary experience with the use of the Pipeline Embolization Device in the management of MCA aneurysms located at the bifurcations, including mid- and long-term follow-up data.  Wide-neck MCA aneurysms, which give rise to a bifurcating or distal branch in which other endovascular techniques are thought to be unfeasible or more risky, were included. Data including demographics, aneurysm features, antiplatelet therapy, complications, and angiographic follow-up results for up to 30 months were recorded.  Twenty-five aneurysms located at the MCA bifurcation (n = 21) or distal (n = 4) were treated. Of these, 22 were small and 3 were large. A single device was used in all but 2. No deaths occurred in the series. All patients had at least 1 control angiographic study, 21 of which were DSA (3-30 months), which showed that 12 of the rising branches were patent whereas 6 were filling in reduced caliber and 3 were occluded asymptomatically. According to the last angiographic follow-up, complete occlusion was revealed in 21 of 25 aneurysms (84%).  The Pipeline Embolization Device provides a safe and effective treatment alternative for wide-neck MCA aneurysms that give rise to a bifurcating or distal branch when other endovascular techniques are thought to be unfeasible or more risky.

title: A comprehensive Chinese experience against SARS-CoV-2 in ophthalmology
abstract: The 2019 novel coronavirus disease (COVID-19) has now swept through the continents and poses a global threat to public health. Several investigations have been conducted to identify whether COVID-19 can be transmitted through the ocular route, and the conclusion is that it is a potential route but remains uncertain. Due to the face-to-face communication with patients, frequent exposure to tears and ocular discharge, and the unavoidable use of equipment which requires close proximity, ophthalmologists carry a high risk of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Based on 33 articles published by Chinese scholars, guidelines and clinical practice experience in domestic hospitals, we have summarized the Chinese experience through the lens of ophthalmology, hoping to make a contribution to protecting ophthalmologists and patients around the world.

title: SARS-CoV-2 RNA detection of hospital isolation wards hygiene monitoring during the Coronavirus Disease 2019 outbreak in a Chinese hospital
abstract: The aim of this paper was to monitor the presence of SARS-Cov-2 among hospital environment surfaces, sewage, and personal protective equipment (PPE) of staffs in isolation wards in the First Affiliated Hospital of Zhejiang University, China.  Surfaces of objects were routinely wiped with 1000mg/L chlorine containing disinfectant. Air and sewage disinfection was proceeded routinely and strictly. Hospital environmental surfaces and PPE of staffs in isolation wards were sampled using swabs. The sewage from various inlet and outlets were sampled. The respiratory and stool specimens of patients were collected. The respiratory specimens of staffs in the isolation wards were also sampled once a week. Quantitative real-time reverse transcription PCR (qRT-PCR) methods were used to confirm the existence of SARS-Cov-2 RNA. Viral culture was done for the samples positive for SARS-Cov-2 RNA.  During the study period, 33 laboratory-confirmed patients were hospitalized in isolation wards in the hospital. None of SARS-Cov-2 RNA was detected among the 36 objects surface samples and 9 staffs PPE samples in isolation wards. Though the 3 sewage samples from the inlet of preprocessing disinfection pool were positive for SARS-CoV-2 RNA and the sample from the outlet of preprocessing disinfection pool was weakly positive, the sewage sample from the outlet of the last disinfection pool was negative. All of the 5 sewage samples from various points were negative by viral culture of SARS-Cov-2. None of the respiratory specimens of staffs in the isolation wards were positive.  Though SARS-Cov-2 RNA of the sewage samples were positive from inlets of the sewage disinfection pool and negative from the outlet of the last sewage disinfection pool, no viable virus was detected by culture. The monitoring data in this study suggested that the strict disinfection and hand hygiene could decrease the hospital-associated COVID-19 infection risk of the staffs in isolation wards.

title: The establishment of reference sequence for SARS‐CoV‐2 and variation analysis
abstract: Starting around December 2019, an epidemic of pneumonia, which was named COVID‐19 by the World Health Organization, broke out in Wuhan, China, and is spreading throughout the world. A new coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) by the Coronavirus Study Group of the International Committee on Taxonomy of Viruses was soon found to be the cause. At present, the sensitivity of clinical nucleic acid detection is limited, and it is still unclear whether it is related to genetic variation. In this study, we retrieved 95 full‐length genomic sequences of SARAS‐CoV‐2 strains from the National Center for Biotechnology Information and GISAID databases, established the reference sequence by conducting multiple sequence alignment and phylogenetic analyses, and analyzed sequence variations along the SARS‐CoV‐2 genome. The homology among all viral strains was generally high, among them, 99.99% (99.91%‐100%) at the nucleotide level and 99.99% (99.79%‐100%) at the amino acid level. Although overall variation in open‐reading frame (ORF) regions is low, 13 variation sites in 1a, 1b, S, 3a, M, 8, and N regions were identified, among which positions nt28144 in ORF 8 and nt8782 in ORF 1a showed mutation rate of 30.53% (29/95) and 29.47% (28/95), respectively. These findings suggested that there may be selective mutations in SARS‐COV‐2, and it is necessary to avoid certain regions when designing primers and probes. Establishment of the reference sequence for SARS‐CoV‐2 could benefit not only biological study of this virus but also diagnosis, clinical monitoring and intervention of SARS‐CoV‐2 infection in the future.

title: The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application
abstract: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 , which has important implications for surveillance and control activities.  To estimate the length of the incubation period of COVID-19 and describe its public health implications.  Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020.  News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China.  Persons with confirmed SARS-CoV-2 infection outside Hubei province, China.  Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization.  There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.  Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases.  This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases.  U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

title: The Evaluation of Deep Neural Networks and X-Ray as a Practical Alternative for Diagnosis and Management of COVID-19
abstract: High-resolution computed tomography radiology is a critical tool in the diagnosis and management of COVID-19 infection; however, in smaller clinics around the world, there is a shortage of radiologists available to analyze these images. In this paper, we compare the performance of 16 available deep learning algorithms to help identify COVID19. We utilize an already existing diagnostic technology (X-ray) and an already existing neural network (ResNet-50) to diagnose COVID-19. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithm, thus aiding the front-line in the race against the COVID-19 pandemic. Results show that ResNet-50 is the optimal pretrained neural network for the detection of COVID-19, using three different cross-validation ratios, based on training time, accuracy, and network size. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression.

title: Potential of large “first generation” human‐to‐human transmission of 2019‐nCoV
abstract: To investigate the genetic diversity, time origin, and evolutionary history of the 2019‐nCoV outbreak in China and Thailand, a total of 12 genome sequences of the virus with known sampling date (24 December 2019 and 13 January 2020) and geographic location (primarily Wuhan city, Hubei Province, China, but also Bangkok, Thailand) were analyzed. Phylogenetic and likelihood‐mapping analyses of these genome sequences were performed. On the basis of our results, the star‐like signal and topology of 2019‐nCoV may be indicative of potentially large “first generation” human‐to‐human virus transmission. We estimated that 2019‐nCoV likely originated in Wuhan on 9 November 2019 (95% credible interval: 25 September 2019 and 19 December 2019), and that Wuhan is the major hub for the spread of the 2019‐nCoV outbreak in China and elsewhere. Our results could be useful for designing effective prevention strategies for 2019‐nCoV in China and beyond.

title: Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine
abstract: We propose the use of a machine learning algorithm to improve possible COVID-19 case identification more quickly using a mobile phone–based web survey. This method could reduce the spread of the virus in susceptible populations under quarantine.

title: Community perspectives on the benefits and risks of technologically enhanced communicable disease surveillance systems: a report on four community juries
abstract: Outbreaks of infectious disease cause serious and costly health and social problems. Two new technologies – pathogen whole genome sequencing (WGS) and Big Data analytics – promise to improve our capacity to detect and control outbreaks earlier, saving lives and resources. However, routinely using these technologies to capture more detailed and specific personal information could be perceived as intrusive and a threat to privacy.  making pathogen WGS and linked administrative data available for public health research; using this information in concert with data linkage and machine learning to enhance communicable disease surveillance systems. Fifty participants of diverse backgrounds, mixed genders and ages were recruited by random-digit-dialling and topic-blinded social-media advertising. Each jury was presented with balanced factual evidence supporting different expert perspectives on the potential benefits and costs of technologically enhanced public health research and communicable disease surveillance and given the opportunity to question experts.  Almost all jurors supported data linkage and WGS on routinely collected patient isolates for the purposes of public health research, provided standard de-identification practices were applied. However, allowing this information to be operationalised as a syndromic surveillance system was highly contentious with three juries voting in favour, and one against by narrow margins. For those in favour, support depended on several conditions related to system oversight and security being met. Those against were concerned about loss of privacy and did not trust Australian governments to run secure and effective systems.  Participants across all four events strongly supported the introduction of data linkage and pathogenomics to public health research under current research governance structures. Combining pathogen WGS with event-based data surveillance systems, however, is likely to be controversial because of a lack of public trust, even when the potential public health benefits are clear. Any suggestion of private sector involvement or commercialisation of WGS or surveillance data was unanimously rejected.

title: Open access epidemiological data from the COVID-19 outbreak

title: Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020
abstract: Data assimilation is used to optimally fit a classical epidemiology model to the Johns Hopkins data of the Covid-19 pandemic. The optimisation is based on the confirmed cases and confirmed deaths. This is the only data available with reasonable accuracy. Infection and recovery rates can be infered from the model as well as the model parameters. The parameters can be linked with government actions or events like the end of the holiday season. Based on this numbers predictions for the future can be made and control targets specified. With other words: We look for a solution to a given model which fits the given data in an optimal sense. Having that solution, we have all parameters.

title: Preliminary Estimates of the Prevalence of Selected Underlying Health Conditions Among Patients with Coronavirus Disease 2019 — United States, February 12–March 28, 2020
abstract: On March 11, 2020, the World Health Organization declared Coronavirus Disease 2019 (COVID-19) a pandemic (1). As of March 28, 2020, a total of 571,678 confirmed COVID-19 cases and 26,494 deaths have been reported worldwide (2). Reports from China and Italy suggest that risk factors for severe disease include older age and the presence of at least one of several underlying health conditions (3,4). U.S. older adults, including those aged ≥65 years and particularly those aged ≥85 years, also appear to be at higher risk for severe COVID-19-associated outcomes; however, data describing underlying health conditions among U.S. COVID-19 patients have not yet been reported (5). As of March 28, 2020, U.S. states and territories have reported 122,653 U.S. COVID-19 cases to CDC, including 7,162 (5.8%) for whom data on underlying health conditions and other known risk factors for severe outcomes from respiratory infections were reported. Among these 7,162 cases, 2,692 (37.6%) patients had one or more underlying health condition or risk factor, and 4,470 (62.4%) had none of these conditions reported. The percentage of COVID-19 patients with at least one underlying health condition or risk factor was higher among those requiring intensive care unit (ICU) admission (358 of 457, 78%) and those requiring hospitalization without ICU admission (732 of 1,037, 71%) than that among those who were not hospitalized (1,388 of 5,143, 27%). The most commonly reported conditions were diabetes mellitus, chronic lung disease, and cardiovascular disease. These preliminary findings suggest that in the United States, persons with underlying health conditions or other recognized risk factors for severe outcomes from respiratory infections appear to be at a higher risk for severe disease from COVID-19 than are persons without these conditions.

title: COVID-19 Government Response Event Dataset (CoronaNet v.1.0).
abstract: Governments worldwide have implemented countless policies in response to the COVID-19 pandemic. We present an initial public release of a large hand-coded dataset of over 13,000 such policy announcements across more than 195 countries. The dataset is updated daily, with a 5-day lag for validity checking. We document policies across numerous dimensions, including the type of policy, national versus subnational enforcement, the specific human group and geographical region targeted by the policy, and the time frame within which each policy is implemented. We further analyse the dataset using a Bayesian measurement model, which shows the quick acceleration of the adoption of costly policies across countries beginning in mid-March 2020 through 24 May 2020. We believe that these data will be instrumental for helping policymakers and researchers assess, among other objectives, how effective different policies are in addressing the spread and health outcomes of COVID-19.

title: Dataset of seismic ambient vibrations from the quaternary Norcia basin (central Italy)
abstract: Central Italy was affected by a long seismic sequence in 2016 and 2017, characterized by five main-shocks with Mw>5.0. The Mw 6.5 mainshock occurred on 30 October 2016 close to the town of Norcia, located in the intra-Apennine Norcia basin. Different degrees of damages were observed during this seismic crisis, caused by a variable seismic shaking. This was also due to important 1D and 2D variation of Quaternary fluvio-lacustrine sediments infilling the basin. Following such considerations, a new geophysical dataset of seismic vibration measurements was acquired in the study area during the period April 2017–November 2019. We collected mainly single-seismic station noise data, to infer the distribution of resonance frequency (f(0)) of the basin. A total of 60 sites were measured to cover the entire extension in the basin. We deployed seismometers along three transects of a total length of 21 km, mostly along the main structural directions of the basin (i.e. NNW-SSE and NE-SW). Two 2D arrays of seismic stations with a elicoidal-shaped geometry, and a set of MASW active data were also acquired in the northern sector of the basin, in order to better constrain the seismic velocity of the sedimentary infilling. These new records have been integrated with available geological information in order to reconstruct the deep structure of the basin, as discussed in the research paper by . The entire dataset used in  is here provided, together with 7 additional records recovered for the basin (i.e. N54-N60) and ancillary open-source geospatial data. The dataset can be used for different purposes: specific research on the Norcia basin, comparative studies on similar areas around the world, development of new data modeling and testing of new analysis software, and as a training dataset for machine learning applications.

title: Origin of Novel Coronavirus (COVID-19): A Computational Biology Study using Artificial Intelligence
abstract: Origin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences. This paper investigates the COVID-19 virus origin by using artificial intelligence (AI) and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus. At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.

title: Development of a Novel, Genome Subtraction-Derived, SARS-CoV-2-Specific COVID-19-nsp2 Real-Time RT-PCR Assay and Its Evaluation Using Clinical Specimens
abstract: The pandemic novel coronavirus infection, Coronavirus Disease 2019 (COVID-19), has affected at least 190 countries or territories, with 465,915 confirmed cases and 21,031 deaths. In a containment-based strategy, rapid, sensitive and specific testing is important in epidemiological control and clinical management. Using 96 SARS-CoV-2 and 104 non-SARS-CoV-2 coronavirus genomes and our in-house program, GolayMetaMiner, four specific regions longer than 50 nucleotides in the SARS-CoV-2 genome were identified. Primers were designed to target the longest and previously untargeted nsp2 region and optimized as a probe-free real-time reverse transcription-polymerase chain reaction (RT-PCR) assay. The new COVID-19-nsp2 assay had a limit of detection (LOD) of 1.8 TCID(50)/mL and did not amplify other human-pathogenic coronaviruses and respiratory viruses. Assay reproducibility in terms of cycle threshold (Cp) values was satisfactory, with the total imprecision (% CV) values well below 5%. Evaluation of the new assay using 59 clinical specimens from 14 confirmed cases showed 100% concordance with our previously developed COVID-19-RdRp/Hel reference assay. A rapid, sensitive, SARS-CoV-2-specific real-time RT-PCR assay, COVID-19-nsp2, was developed.

title: Mixed and blended emotional reactions to 2014 Ebola outbreak

title: Tracing open data in emergencies: the case of the COVID‐19 pandemic
abstract: The COVID‐19 pandemic constitutes an ongoing, burning Public Health Emergency of International Concern (PHEIC). In 2015, the World Health Organization (WHO) adopted an open‐data policy recommendation in such situations.  The present cross‐sectional meta‐research study aimed to assess the availability of open data and metrics and of articles pertaining to the COVID‐19 outbreak in five high‐impact journals.  All articles regarding the SARS‐CoV‐2, published in five high impact journals (Ann Intern Med, BMJ, JAMA, NEJM and Lancet) until March 14, 2020 were retrieved. Meta‐data (namely the type of article, number of authors, number of patients, citations, errata, news and social media mentions) were extracted for each article in each journal in a systematic way. Google Scholar and Scopus were used for citations and author metrics respectively, and Altmetrics and PlumX were used for news and social media mentions retrieval. The degree of adherence to the PHEIC open data call was also evaluated.  A total of 140 articles were published until March 14, 2020, mostly opinion papers. Sixteen errata followed these publications. The number of authors in each article ranged from 1 to 63, whereas the number of patients with a laboratory‐confirmed SARS‐CoV‐2 infection reached 2,645. The impact of these publications reached a total of 4,210 cumulative crude citations and 342,790 news and social media mentions. Only one publication (0.7%) provided complete open data, while 32 (22.9%) included patient data.  Even though a large number of manuscripts was produced since the pandemic, availability of open data remains restricted.

title: Estimating the Early Outbreak Cumulative Incidence of COVID-19 in the United States: Three Complementary Approaches
abstract: Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the weekly incidence of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the reported positive COVID-19 case counts. We introduce three complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 during the early outbreak in each state in the US as well as in New York City, using a combination of excess influenza-like illness reports, COVID-19 test statistics, and COVID-19 mortality reports. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 symptomatic case counts from March 1 to April 4, 2020 varied from 5 to 50 times greater than the official positive test counts. Nationally, our estimates of COVID-19 symptomatic cases in the US as of April 4 have a likely range of 2.2 to 5.1 million cases, with possibly as high as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 12.2 million, which compares with 1.5 million positive test counts. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems during the flu season for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 during upcoming flu seasons in the US and other countries with comparable influenza surveillance systems.

title: Single cell resolution regulatory landscape of the mouse kidney highlights cellular differentiation programs and renal disease targets
abstract: Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profiled open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We define key cell type-specific transcription factors and major gene-regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrated that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells followed a more complex differentiation, where Hfn4a was associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease identified critical cell types, developmental stages, genes, and regulatory mechanisms. We provide a global single cell resolution view of chromatin accessibility of kidney development. The dataset is available via interactive public websites.

title: Coronavirus Disease COVID-19: A New Threat to Public Health.

title: Development of Monoclonal Antibody and Diagnostic Test for Middle East Respiratory Syndrome Coronavirus Using Cell-Free Synthesized Nucleocapsid Antigen
abstract: Protein nativity is one of the most critical factors for the quality of antigens used as immunogens and the reactivities of the resultant antibodies. The preparation and purification of native viral antigens in conventional cell-based protein expression systems are often accompanied by technical hardships. These challenges are attributable mainly to protein aggregation and insolubility during expression and purification, as well as to very low expression levels associated with the toxicity of some viral proteins. Here, we describe a novel approach for the production of monoclonal antibodies (mAbs) against nucleocapsid protein (NP) of the Middle East respiratory syndrome coronavirus (MERS-CoV). Using a wheat germ cell-free protein synthesis system, we successfully prepared large amounts of MERS-CoV NP antigen in a state that was highly soluble and intact for immunization. Following mouse immunization and hybridoma generation, we selected seven hybridoma clones that produced mAbs with exclusive reactivity against MERS-CoV NP. Epitope mapping and subsequent bioinformatic analysis revealed that these mAbs recognized epitopes located within relatively highly conserved regions of the MERS-CoV amino-acid sequence. Consistently, the mAbs exhibited no obvious cross-reactivity with NPs derived from other related viruses, including SARS coronavirus. After determining the optimal combinations of these mAbs, we developed an enzyme-linked immunosorbent assay and a rapid immunochromatographic antigen detection test that can be reliably used for laboratory diagnosis of MERS-CoV. Thus, this study provides strong evidence that the wheat germ cell-free system is useful for the production of diagnostic mAbs against emerging pathogens.

title: CORD-19: The COVID-19 Open Research Dataset
abstract: The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19.

title: Informational needs assessment of non‐Hodgkin lymphoma survivors and their physicians

title: CoroNet: A Deep Neural Network for Detection and Diagnosis of COVID-19 from Chest X-ray Images
abstract: The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays.  In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases.  CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available.  CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases.

title: Single-cell RNA expression profiling of ACE2,thereceptor of SARS-CoV-2
abstract: A novel coronavirus SARS-CoV-2 was identified in Wuhan, Hubei Province, China in December of 2019. According to WHO report, this new coronavirus has resulted in 76,392 confirmed infections and 2,348 deaths in China by 22 February, 2020, with additional patients being identified in a rapidly growing number internationally. SARS-CoV-2 was reported to share the same receptor, Angiotensin-converting enzyme 2 (ACE2), with SARS-CoV. Here based on the public database and the state-of-the-art single-cell RNA-Seq technique, we analyzed the ACE2 RNA expression profile in the normal human lungs. The result indicates that the ACE2 virus receptor expression is concentrated in a small population of type II alveolar cells (AT2). Surprisingly, we found that this population of ACE2-expressing AT2 also highly expressed many other genes that positively regulating viral entry, reproduction and transmission. This study provides a biological background for the epidemic investigation of the COVID-19, and could be informative for future anti-ACE2 therapeutic strategy development.

title: Estimating unobserved SARS-CoV-2 infections in the United States
abstract: By March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved SARS-CoV-2 infections during its initial invasion of the US remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the US through March 12, we estimated that 22,876 (95% posterior predictive interval: 7,451 - 53,044) infections occurred in the US by this date. By comparing the model's predictions of symptomatic infections to local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as exponential growth of infections outpaced increases in testing. Between February 21 and March 12, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.97, 95% PPI: 0.85 - 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the US.

title: A Multi-Dimensional Big Data Storing System for Generated COVID-19 Large-Scale Data using Apache Spark
abstract: The ongoing outbreak of coronavirus disease (COVID-19) had burst out in Wuhan China, specifically in December 2019. COVID-19 has caused by a new virus that had not been identified in human previously. This was followed by a widespread and rapid spread of this epidemic throughout the world. Daily, the number of the confirmed cases are increasing rapidly, number of the suspect increases, based on the symptoms that accompany this disease, and unfortunately number of the deaths also increase. Therefore, with these increases in number of cases around the world, it becomes hard to manage all these cases information with different situations; if the patient either injured or suspect with which symptoms that appeared on the patient. Therefore, there is a critical need to construct a multi-dimensional system to store and analyze the generated large-scale data. In this paper, a Comprehensive Storing System for COVID-19 data using Apache Spark (CSS-COVID) is proposed, to handle and manage the problem caused by increasing the number of COVID-19 daily. CSS-COVID helps in decreasing the processing time for querying and storing COVID-19 daily data. CSS-COVID consists of three stages, namely, inserting and indexing, storing, and querying stage. In the inserting stage, data is divided into subsets and then index each subset separately. The storing stage uses set of storing-nodes to store data, while querying stage is responsible for handling the querying processes. Using Apache Spark in CSS-COVID leverages the performance of dealing with large-scale data of the coronavirus disease injured whom increase daily. A set of experiments are applied, using real COVID-19 Datasets, to prove the efficiency of CSS-COVID in indexing large-scale data.

title: Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries
abstract: Public health measures to prevent, detect, and respond to events are essential to control public health risks, including infectious disease outbreaks, as highlighted in the International Health Regulations (IHR). In light of the outbreak of 2019 novel coronavirus disease (COVID-19), we aimed to review existing health security capacities against public health risks and events.  We used 18 indicators from the IHR State Party Annual Reporting (SPAR) tool and associated data from national SPAR reports to develop five indices: (1) prevent, (2) detect, (3) respond, (4) enabling function, and (5) operational readiness. We used SPAR 2018 data for all of the indicators and categorised countries into five levels across the indices, in which level 1 indicated the lowest level of national capacity and level 5 the highest. We also analysed data at the regional level (using the six geographical WHO regions).  Of 182 countries, 52 (28%) had prevent capacities at levels 1 or 2, and 60 (33%) had response capacities at levels 1 or 2. 81 (45%) countries had prevent capacities and 78 (43%) had response capacities at levels 4 or 5, indicating that these countries were operationally ready. 138 (76%) countries scored more highly in the detect index than in the other indices. 44 (24%) countries did not have an effective enabling function for public health risks and events, including infectious disease outbreaks (7 [4%] at level 1 and 37 [20%] at level 2). 102 (56%) countries had level 4 or level 5 enabling function capacities in place. 32 (18%) countries had low readiness (2 [1%] at level 1 and 30 [17%] at level 2), and 104 (57%) countries were operationally ready to prevent, detect, and control an outbreak of a novel infectious disease (66 [36%] at level 4 and 38 [21%] at level 5).  Countries vary widely in terms of their capacity to prevent, detect, and respond to outbreaks. Half of all countries analysed have strong operational readiness capacities in place, which suggests that an effective response to potential health emergencies could be enabled, including to COVID-19. Findings from local risk assessments are needed to fully understand national readiness capacities in relation to COVID-19. Capacity building and collaboration between countries are needed to strengthen global readiness for outbreak control.

title: Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data
abstract: The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2–14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3–4 days without truncation and at 5–9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.

title: Survey data of COVID-19-related Knowledge, Risk Perceptions and Precautionary Behavior among Nigerians
abstract: In response to the global call for strategic information to understand the novel coronavirus, the dataset presented in this paper is an examination of COVID-19-related knowledge, risk perceptions and precautionary health behavior among Nigerians. The data were generated during the COVID-19 lockdown in the country through a survey distributed via an online questionnaire, assessing socio-demographic information (7 items), knowledge (5 items), information sources (1 item), risk perception (9 items), expected end of lockdown (1 item), and COVID-19 precautionary health behavior (10 items), from 28th March to 4th April, 2020, gathering a total of 1,357 responses. A combination of purposive and snowball techniques helped to select the respondents via Whatsapp and Facebook from 180 cities/towns in the 6 geopolitical zones of Nigeria. The survey data were analyzed using descriptive and inferential statistics. The entire dataset is stored in a Microsoft Excel Worksheet (xlsx) and the questionnaire is attached as a supplementary file. The data will assist in curbing the Coronavirus pandemic by offering evidence for strategic and targeted interventions as well as health policy formulations and implementation.

title: International Electronic Health Record-Derived COVID-19 Clinical Course Profiles: The 4CE Consortium
abstract: We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across 5 countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on comorbidities and temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

title: Host-viral infection maps reveal signatures of severe COVID-19 patients
abstract: Viruses are a constant threat to global health as highlighted by the current COVID-19 pandemic. Currently, lack of data underlying how the human host interacts with viruses, including the SARS-CoV-2 virus, limits effective therapeutic intervention. We introduce Viral-Track, a computational method that globally scans unmapped scRNA-seq data for the presence of viral RNA, enabling transcriptional cell sorting of infected versus bystander cells. We demonstrate the sensitivity and specificity of Viral-Track to systematically detect viruses from multiple models of infection, including hepatitis B virus in an unsupervised manner. Applying Viral-Track to Bronchoalveloar-Lavage samples from severe and mild COVID-19 patients reveals a dramatic impact of the virus on the immune system of severe patients compared to mild cases. Viral-Track detects an unexpected co-infection of the human MetaPneumoVirus, present mainly in monocytes perturbed in type-I IFN-signaling. Viral-Track provides a robust technology for dissecting the mechanisms of viral-infection and pathology.

title: SARS-CoV-2 in the ocular surface of COVID-19 patients
abstract: The 2019 novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally, while the routes of transmission of this virus are still controversial. We enrolled 33 patients, without any ocular manifestation, with their ocular surface swabs collected for virus detection. RNA was detected strong positive in samples of both eyes from two patients. Therefore, SARS-CoV-2 may exist in the normal ocular surface of COVID-19 patients, suggesting that this virus might be spread through conjunctival contact.

title: Weather Conditions and COVID-19 Transmission: Estimates and Projections
abstract: Understanding and projecting the spread of COVID-19 requires reliable estimates of how weather components are associated with the transmission of the virus. Prior research on this topic has been inconclusive. Identifying key challenges to reliable estimation of weather impact on transmission we study this question using one of the largest assembled databases of COVID-19 infections and weather.  We assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020. Using simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study. Using this method and controlling for location-specific response trends we estimate how different weather variables are associated with the reproduction number for COVID-19. We then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities.  We show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to-date. Correcting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number ([R]), with each degree Celsius associated with a 3.1% (95% CI, 1.5% to 4.8%) reduction in [R]. Higher levels of relative humidity strengthen the negative effect of temperature above 25 degrees. Moreover, one millibar of additional pressure increases [R] by approximately 0.8 percent (95% CI, 0.6% to 1%) at the median pressure (1016 millibars) in our sample. We also find significant positive effects for wind speed, precipitation, and diurnal temperature on [R]. Sensitivity analysis and simulations show that results are robust to multiple assumptions. Despite conservative estimates, weather effects are associated with a 43% change in [R] between the 5th and 95th percentile of weather conditions in our sample.  These results provide evidence for the relationship between several weather variables and the spread of COVID-19. However, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations.

title: COVID-19 Image Data Collection
abstract: This paper describes the initial COVID-19 open image data collection. It was created by assembling medical images from websites and publications and currently contains 123 frontal view X-rays.

title: [An analysis of global research on SARS-CoV-2].
abstract: The SARS-CoV-2 has been spread to 26 countries around the world since its outbreak. By February 16, 2020, more than 68 000 people had been diagnosed with COVID-19. Researchers from all over the world have carried out timely studies on this public health emergency and produced a number of scientific publications. This review aims to re-analyze and summarize the current research findings in a timely manner to guide scholars in relevant fields to further SARS-CoV-2 research and assist healthcare professionals in their work and decision-making. The SARS-CoV-2 related terms were selected in both English and Chinese and were searched in several major databases, including Pubmed, Web of Science, CNKI, Wanfang, and VIP databases. The reference list of each search result was screened for relevance, which was further supplemented to the search results. The included studies were categorized by topics with key characteristics extracted, re-analyzed, and summarized. A total of 301 articles were finally included with 136 in Chinese and 165 in English. The number of publications has rapidly increased since mid-January, 2020, and a peak day was 6th February on which 50 articles were published. The top three countries publishing articles were China, the United States and the United Kingdom. The Lancet and its specialty journals have published the most articles, with contribution also from journals such as New England Journal of Medicine ( NEJM), The Journal of the American Medical Association ( JAMA), and Nature. All articles were categorized into epidemiology, clinical diagnosis and treatment, basic research, pregnant women and children, mental health, epidemic prevention & control, and others. The literatures related to SARS-CoV-2 are emerging rapidly. It is necessary to sort out and summarize the research topic in time, which has a good reference value for staff in different positions. At the same time, it is necessary to strengthen the judgment of the quality of literatures.

title: From plague to coronavirus: On the value of ship traffic data for epidemic modeling
abstract: In addition to moving people and goods, ships can spread disease. Ship traffic may complement air traffic as a source of import risk, and cruise ships - with large passenger volumes and multiple stops - are potential hotspots, in particular for diseases with long incubation periods. Vessel trajectory data from ship Automatic Identification Systems (AIS) is available online and it is possible to extract and analyze this data. We illustrate this in the case of the current coronavirus epidemic, in which hundreds of infected individuals have traveled in ships captured in the AIS dataset. This real time and historical data should be included in epidemiological models of disease to inform the corresponding operational response.

title: Are Online Searches for the Novel Coronavirus (COVID-19) Related to Media or Epidemiology? A Cross-sectional Study
abstract: Previous studies on the novel Coronavirus (COVID-19) have found strong correlations between online searches and the epidemiology of the disease.  Our aim was to determine if online searches for COVID-19 related to international media announcements or national epidemiology.  Searches for “coronavirus” were made on Google Trends from December 31, 2019 to April 13, 2020 for 40 European countries. The online COVID-19 searches for all countries were correlated with each other. COVID-10 epidemiology (i.e. incidence and mortality) was correlated with the national online searches. Major announcements by the World Health Organization (WHO) were taken into consideration with peaks in online searches. Correlations were made using Spearman's rank correlation coefficient.  Overall, the online searches for COVID-19 were not correlated with the actual incidence and mortality of COVID-19. The mean Spearman correlation for incidence was 0.20 (range -0.66 to 0.76) and for mortality was 0.35 (range -0.75 to 0.85). Online searches in Europe were all strongly synchronized with each other; a mean Spearman correlation of 0.93 (range 0.62 to 0.99).  Online searches for COVID-19 in Europe are not correlated with epidemiology but strongly correlated with international WHO announcements. Our study challenges previous Google Trends studies and emphasizes the role of the WHO in raising awareness of a new disease.

title: Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project
abstract: John Brownstein and colleagues discuss HealthMap, an automated real-time system that monitors and disseminates online information about emerging infectious diseases.

title: Big data, privacy and COVID-19 – learning from humanitarian expertise in data protection
abstract: The COVID-19 pandemic leads governments around the world to resort to tracking technology and other data-driven tools in order to monitor and curb the spread of SARS-CoV-2. Such large-scale incursion into privacy and data protection is unthinkable during times of normalcy. However, in times of a pandemic the use of location data provided by telecom operators and/or technology companies becomes a viable option. Importantly, legal regulations hardly protect people’s privacy against governmental and corporate misuse. Established privacy regimes are focused on individual consent, and most human rights treaties know derogations from privacy and data protection norms for states of emergency. This leaves little safeguards nor remedies to guarantee individual and collective autonomy. However, the challenge of responsible data use during a crisis is not novel. The humanitarian sector has more than a decade of experience to offer. International organisations and humanitarian actors have developed detailed guidelines on how to use data responsibly under extreme circumstances. This article briefly addresses the legal gap of data protection and privacy during this global crisis. Then it outlines the state of the art in humanitarian practice and academia on data protection and data responsibility during crisis.

title: A modular framework for the development of targeted Covid-19 blood transcript profiling panels
abstract: Covid-19 morbidity and mortality are associated with a dysregulated immune response. Tools are needed to enhance existing immune profiling capabilities in affected patients. Here we aimed to develop an approach to support the design of focused blood transcriptome panels for profiling the immune response to SARS-CoV-2 infection. We designed a pool of candidates based on a pre-existing and well-characterized repertoire of blood transcriptional modules. Available Covid-19 blood transcriptome data was also used to guide this process. Further selection steps relied on expert curation. Additionally, we developed several custom web applications to support the evaluation of candidates. As a proof of principle, we designed three targeted blood transcript panels, each with a different translational connotation: therapeutic development relevance, SARS biology relevance and immunological relevance. Altogether the work presented here may contribute to the future expansion of immune profiling capabilities via targeted profiling of blood transcript abundance in Covid-19 patients.

title: COVID-CT-Dataset: A CT Scan Dataset about COVID-19
abstract: During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-powered diagnosis methods of COVID-19 based on CTs. To address this issue, we build an open-sourced dataset -- COVID-CT, which contains 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 CTs. The utility of this dataset is confirmed by a senior radiologist who has been diagnosing and treating COVID-19 patients since the outbreak of this pandemic. We also perform experimental studies which further demonstrate that this dataset is useful for developing AI-based diagnosis models of COVID-19. Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0.90, an AUC of 0.98, and an accuracy of 0.89. According to the senior radiologist, models with such performance are good enough for clinical usage. The data and code are available at https://github.com/UCSD-AI4H/COVID-CT

title: Epidemiological parameters of coronavirus disease 2019: a pooled analysis of publicly reported individual data of 1155 cases from seven countries
abstract: The outbreak of coronavirus disease 2019 (COVID-19) has been declared a pandemic by the World Health Organization, while several key epidemiological parameters of the disease remain to be clarified. This study aimed to obtain robust estimates of the incubation period, upper limit of latent period (interval between exposure of infector and infectee), serial interval, time point of exposure and basic reproduction number (R0) of COVID-19.  Between late February and early March of 2020, the individual data of laboratory confirmed cases of COVID-19 were retrieved from 10728 publicly available reports released by the health authorities of and outside China and from 1790 publications identified in PubMed and CNKI. To be eligible, a report had to contain the data that allowed for estimation of at least one parameter. As relevant data mainly came from clustering cases, the clusters for which no evidence was available to establish transmission order were all excluded to ensure accuracy of estimates. Additionally, only the cases with an exposure period spanning 3 days or less were included in the estimation of parameters involving exposure date, and a simple method for determining exposure date was adopted to ensure the error of estimates be small (< 0.3 day). Depending on specific parameters, three or four of normal, lognormal, Weibull, and gamma distributions were fitted to the datasets and the results from appropriate models were presented.  In total, 1155 cases from China, Japan, Singapore, South Korea, Vietnam, Germany and Malaysia were included for the final analysis. The mean and standard deviation were 7.44 days and 4.39 days for incubation period, 2.52 days and 3.95 days for the upper limit of latent period, 6.70 days and 5.20 days for serial interval, and -0.19 day (i.e., 0.19 day before symptom onset of infector) and 3.32 days for time point of exposure. R0 was estimated to be 1.70 and 1.78 based on two different formulas. For 39 (6.64%) cases, the incubation periods were longer than 14 days. In 102 (43.78%) infector-infectee pairs, transmission occurred before the symptom onsets of infectors. In 27 (3.92%) infector-infectee pairs, symptom onsets of infectees occurred before those of infectors. Stratified analysis showed that incubation period and serial interval were consistently longer for those with less severe disease and for those whose primary cases had less severe disease. Asymptomatic transmission was also observed.  This study obtained robust estimates of several key epidemiological parameters of COVID-19. The findings support current practice of 14-day quarantine of persons with potential exposure, but also suggest that longer monitoring periods might be needed for selected groups. The estimates of serial interval, time point of exposure and latent period provide consistent evidence on pre-symptomatic transmission. This together with asymptomatic transmission and the generally longer incubation and serial interval of less severe cases suggests a high risk of long-term epidemic in the absence of appropriate control measures.

title: Putative Inhibitors of SARS-CoV-2 Main Protease from A Library of Marine Natural Products: A Virtual Screening and Molecular Modeling Study
abstract: The current emergency due to the worldwide spread of the COVID-19 caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a great concern for global public health. Already in the past, the outbreak of severe acute respiratory syndrome (SARS) in 2003 and Middle Eastern respiratory syndrome (MERS) in 2012 demonstrates the potential of coronaviruses to cross-species borders and further underlines the importance of identifying new-targeted drugs. An ideal antiviral agent should target essential proteins involved in the lifecycle of SARS-CoV. Currently, some HIV protease inhibitors (i.e., Lopinavir) are proposed for the treatment of COVID-19, although their effectiveness has not yet been assessed. The main protease (M(pro)) provides a highly validated pharmacological target for the discovery and design of inhibitors. We identified potent M(pro) inhibitors employing computational techniques that entail the screening of a Marine Natural Product (MNP) library. MNP library was screened by a hyphenated pharmacophore model, and molecular docking approaches. Molecular dynamics and re-docking further confirmed the results obtained by structure-based techniques and allowed this study to highlight some crucial aspects. Seventeen potential SARS-CoV-2 M(pro) inhibitors have been identified among the natural substances of marine origin. As these compounds were extensively validated by a consensus approach and by molecular dynamics, the likelihood that at least one of these compounds could be bioactive is excellent.

title: Racial demographics and COVID-19 confirmed cases and deaths: a correlational analysis of 2886 US counties
abstract: Recent news reports state that racial minority groups, such as African–Americans, are experiencing a greater COVID-19 burden, as measured by confirmed cases and deaths. Limited racial data is available on a national level.  We conducted the first nationwide analysis to examine COVID-19 and race on a county level. We obtained datasets on COVID-19 cases and deaths, and racial population totals, by US county. We examined if correlations exist between the racial percentages and percentages of confirmed COVID-19 cases and deaths by county.  A positive correlation existed between percentages of African–Americans living in a county and who have COVID-19 (r = 0.254, P < 0.0001), who have died from COVID-19 (r = 0.268, P < 0.0001), and case mortality (r = 0.055, P = 0.003). Positive correlations also existed between percentages of Asian–Americans living in counties and these factors. Negative correlations existed between percentages of Whites living in counties and these factors.  A weak, albeit very significant, positive relationship exists between the percentage of African–Americans living in a county and the percentage of COVID-19 confirmed cases, confirmed deaths and case mortality in the county. This is in support of many city and statewide analyses, and we urge for targeted resources towards work that further examine these racial associations.

title: The dark cloud with a silver lining: Assessing the impact of the SARS COVID-19 pandemic on the global environment
abstract: The Severe Acute Respiratory Syndrome-Coronavirus Disease 2019 (COVID-19) pandemic caused by a novel coronavirus known as SARS-CoV-2 has caused tremendous suffering and huge economic losses. We hypothesized that extreme measures of partial-to-total shutdown might have influenced the quality of the global environment because of decreased emissions of atmospheric pollutants. We tested this hypothesis using satellite imagery, climatic datasets (temperature, and absolute humidity), and COVID-19 cases available in the public domain. While the majority of the cases were recorded from Western countries, where mortality rates were strongly positively correlated with age, the number of cases in tropical regions was relatively lower than European and North American regions, possibly attributed to faster human-to-human transmission. There was a substantial reduction in the level of nitrogen dioxide (NO2: 0.00002 mol m−2), a low reduction in CO (<0.03 mol m−2), and a low-to-moderate reduction in Aerosol Optical Depth (AOD: ~0.1–0.2) in the major hotspots of COVID-19 outbreak during February–March 2020, which may be attributed to the mass lockdowns. Our study projects an increasing coverage of high COVID-19 hazard at absolute humidity levels ranging from 4 to 9 g m−3 across a large part of the globe during April–July 2020 due to a high prospective meteorological suitability for COVID-19 spread. Our findings suggest that there is ample scope for restoring the global environment from the ill-effects of anthropogenic activities through temporary shutdown measures.

title: Central nervous system manifestations of COVID-19: A systematic review
abstract: In this systematic review, we will discuss the evidence on the occurrence of central nervous system (CNS) involvement and neurological manifestations in patients with COVID-19.  MEDLINE (accessed from PubMed) and Scopus from December 01, 2019 to March 26, 2020 were systematically searched for related published articles. In both electronic databases, the following search strategy was implemented and these key words (in the title/abstract) were used: “COVID 19” OR “coronavirus” AND “brain” OR “CNS” OR “neurologic”.  Through the search strategy, we could identify two articles about neurological involvement by COVID-19. One of these publications was a narrative review and the other one was a viewpoint. However, the authors scanned the reference lists of the included studies and could identify multiple references. One study, specifically investigated the neurological manifestations of COVID-19 and could document CNS manifestations in 25% of the patients. Most of the studies investigated the manifestations of COVID-19 in general.  While neurological manifestations of COVID-19 have not been studied appropriately, it is highly likely that some of these patients, particularly those who suffer from a severe illness, have CNS involvement and neurological manifestations. Precise and targeted documentation of neurological symptoms, detailed clinical, neurological, and electrophysiological investigations of the patients, attempts to isolate SARS-CoV-2 from cerebrospinal fluid, and autopsies of the COVID-19 victims may clarify the role played by this virus in causing neurological manifestations.

title: COVID-19: Challenges to GIS with Big Data
abstract: The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more than 100,000 people infected and thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multi-source big data, rapid visualization of epidemic information, spatial tracking of confirmed cases, prediction of regional transmission, spatial segmentation of the epidemic risk and prevention level, balancing and management of the supply and demand of material resources, and social-emotional guidance and panic elimination, which provided solid spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against the widespread epidemic, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management. At the data level, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.

title: Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study
abstract: This study examined Twitter for public health surveillance during a mass gathering in Canada with two objectives: to explore the feasibility of acquiring, categorizing and using geolocated Twitter data and to compare Twitter data against other data sources used for Pan Parapan American Games (P/PAG) surveillance.  Syndrome definitions were created using keyword categorization to extract posts from Twitter. Categories were developed iteratively for four relevant syndromes: respiratory, gastrointestinal, heat-related illness, and influenza-like illness (ILI). All data sources corresponded to the location of Toronto, Canada. Twitter data were acquired from a publicly available stream representing a 1% random sample of tweets from June 26 to September 10, 2015. Cross-correlation analyses of time series data were conducted between Twitter and comparator surveillance data sources: emergency department visits, telephone helpline calls, laboratory testing positivity rate, reportable disease data, and temperature.  The frequency of daily tweets that were classified into syndromes was low, with the highest mean number of daily tweets being for ILI and respiratory syndromes (22.0 and 21.6, respectively) and the lowest, for the heat syndrome (4.1). Cross-correlation analyses of Twitter data demonstrated significant correlations for heat syndrome with two data sources: telephone helpline calls (r = 0.4) and temperature data (r = 0.5).  Using simple syndromes based on keyword classification of geolocated tweets, we found a correlation between tweets and two routine data sources for heat alerts, the only public health event detected during P/PAG. Further research is needed to understand the role for Twitter in surveillance.  The online version of this article (10.17269/s41997-018-0059-0) contains supplementary material, which is available to authorized users.

title: The 2019 novel coronavirus resource.
abstract: An ongoing outbreak of a novel coronavirus infection in Wuhan, China since December 2019 has led to 31,516 infected persons and 638 deaths across 25 countries (till 16:00 on February 7, 2020). The virus causing this pneumonia was then named as the 2019 novel coronavirus (2019-nCoV) by the World Health Organization. To promote the data sharing and make all relevant information of 2019-nCoV publicly available, we construct the 2019 Novel Coronavirus Resource (2019nCoVR, https://bigd.big.ac.cn/ncov). 2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the Global Initiative on Sharing All Influenza Data, National Center for Biotechnology Information, China National GeneBank, National Microbiology Data Center and China National Center for Bioinformation (CNCB)/National Genomics Data Center (NGDC). It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains. Moreover, by linking seamlessly with related databases in CNCB/NGDC, 2019nCoVR offers virus data submission and sharing services for raw sequence reads and assembled sequences. In this report, we provide comprehensive descriptions on data deposition, management, release and utility in 2019nCoVR, laying important foundations in aid of studies on virus classification and origin, genome variation and evolution, fast detection, drug development and pneumonia precision prevention and therapy.

title: Recognizing Semantic Relations: Attention-Based Transformers vs. Recurrent Models
abstract: Automatically recognizing an existing semantic relation (such as “is a”, “part of”, “property of”, “opposite of” etc.) between two arbitrary words (phrases, concepts, etc.) is an important task affecting many information retrieval and artificial intelligence tasks including query expansion, common-sense reasoning, question answering, and database federation. Currently, two classes of approaches exist to classify a relation between words (concepts) X and Y: (1) path-based and (2) distributional. While the path-based approaches look at word-paths connecting X and Y in text, the distributional approaches look at statistical properties of X and Y separately, not necessary in the proximity of each other. Here, we suggest how both types can be improved and empirically compare them using several standard benchmarking datasets. For our distributional approach, we are suggesting using an attention-based transformer. While they are known to be capable of supporting knowledge transfer between different tasks, and recently set a number of benchmarking records in various applications, we are the first to successfully apply them to the task of recognizing semantic relations. To improve a path-based approach, we are suggesting our original neural word path model that combines useful properties of convolutional and recurrent networks, and thus addressing several shortcomings from the prior path-based models. Both our models significantly outperforms the state-of-the-art within its type accordingly. Our transformer-based approach outperforms current state-of-the-art by 1–12% points on 4 out of 6 standard benchmarking datasets. This results in 15–40% error reduction and is closing the gap between the automated and human performance by up to 50%. It also needs much less training data than prior approaches. For the ease of re-producing our results, we make our source code and trained models publicly available.

title: The COronavirus Pandemic Epidemiology (COPE) Consortium: A Call to Action
abstract: The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) pandemic presents challenges to the real-time collection of population-scale data to inform near-term public health needs as well as future investigations. We established the COronavirus Pandemic Epidemiology (COPE) consortium to address this unprecedented crisis on behalf of the epidemiology research community. As a central component of this initiative, we have developed a COVID Symptom Study (previously known as the COVID Symptom Tracker) mobile application as a common data collection tool for epidemiologic cohort studies with active study participants. This mobile application collects information on risk factors, daily symptoms, and outcomes through a user-friendly interface that minimizes participant burden. Combined with our efforts within the general population, data collected from nearly 3 million participants in the United States and United Kingdom are being used to address critical needs in the emergency response, including identifying potential hot spots of disease and clinically actionable risk factors. The linkage of symptom data collected in the app with information and biospecimens already collected in epidemiology cohorts will position us to address key questions related to diet, lifestyle, environmental, and socioeconomic factors on susceptibility to COVID-19, clinical outcomes related to infection, and long-term physical, mental health, and financial sequalae. We call upon additional epidemiology cohorts to join this collective effort to strengthen our impact on the current health crisis and generate a new model for a collaborative and nimble research infrastructure that will lead to more rapid translation of our work for the betterment of public health.

title: Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
abstract: “Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)”, the novel coronavirus, is responsible for the ongoing worldwide pandemic. “World Health Organization (WHO)” assigned an “International Classification of Diseases (ICD)” code—“COVID-19”-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with “Middle East Respiratory Syndrome (MERS-CoV)”, “Severe Acute Respiratory Syndrome (SARS-CoV)”, and now with this new virus, namely “SARS-CoV-2”, or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a “spread factor (f)”, and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in “ourworldindata.org (Oxford University database)”, and newly simulated datasets, following the analysis of different univariate “Long Short Term Memory (LSTM)” models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months—January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.

title: International Public Health Responses to COVID-19 Outbreak: A Rapid Review
abstract: The outbreak of Coronavirus disease 2019 (COVID-19) has posed a significant threat to many countries. Since the disease does not currently have a particular treatment, there is a compelling need to find substitute means to dominate its expansion. In this rapid review, we aimed to determine some countries’ public responses to the COVID-19 epidemic.  In this study, academic databases, including MEDLINE, Scopus, and Embase, were investigated. The keywords applied in the search strategy besides the names of each country were: “Public Health,” “Public Response”, “Health Policy”, “COVID-19”, “Novel Coronavirus,” “2019-nCoV”, and “SARS-CoV-2”. The countries included China, Italy, Iran, Spain, South Korea, Germany, France, United States, Australia, Canada, Japan, and Singapore.  The total number of retrieved articles in MEDLINE, Scopus, and Embase in April 2020 was 594, and after removing 259 duplicate articles, 335 papers were screened by the experts. After this investigation, 50 articles, in addition to 12 webpages, were extensively reviewed for the results section. Public health strategies and responses can be divided into four main areas, including monitoring, public education, crowd controlling, and care facilities.  According to the results of the management decisions of some governments on quarantining, social isolation, screening methods, and flight suspensions due to the severity and anonymity of COVID-19, it is highly assured that these strategies would be the most successful approaches to confront the present pandemic. Governments should put in place timely and strict measures to halt the spread and diminish its unintended deadly consequences.

title: Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
abstract: Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19–related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.

title: Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients
abstract: Circulating in China and 158 other countries and areas, the ongoing COVID-19 outbreak has caused devastating mortality and posed a great threat to public health. However, efforts to identify effectively supportive therapeutic drugs and treatments has been hampered by our limited understanding of host immune response for this fatal disease. To characterize the transcriptional signatures of host inflammatory response to SARS-CoV-2 (HCoV-19) infection, we carried out transcriptome sequencing of the RNAs isolated from the bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) specimens of COVID-19 patients. Our results reveal distinct host inflammatory cytokine profiles to SARS-CoV-2 infection in patients, and highlight the association between COVID-19 pathogenesis and excessive cytokine release such as CCL2/MCP-1, CXCL10/IP-10, CCL3/MIP-1A, and CCL4/MIP1B. Furthermore, SARS-CoV-2 induced activation of apoptosis and P53 signalling pathway in lymphocytes may be the cause of patients' lymphopenia. The transcriptome dataset of COVID-19 patients would be a valuable resource for clinical guidance on anti-inflammatory medication and understanding the molecular mechansims of host response.

title: Open access institutional and news media tweet dataset for COVID-19 social science research
abstract: As COVID-19 quickly became one of the most concerned global crisis, the demand for data in academic research is also increasing. Currently, there are several open access Twitter datasets, but none of them is dedicated to the institutional and news media Twitter data collection, to fill this blank, we retrieved data from 69 institutional/news media Twitter accounts, 17 of them were related to government and international organizations, 52 of them were news media across North America, Europe and Asia. We believe our open access data can provide researchers more availability to conduct social science research.

title: Quantitative Analysis and Prognostic Implication of SARS Coronavirus RNA in the Plasma and Serum of Patients with Severe Acute Respiratory Syndrome
abstract: The availability of an early diagnostic tool for severe acute respiratory syndrome (SARS) would have major public health implications. We investigated whether the SARS coronavirus (SARS-CoV) can be detected in serum and plasma samples during the early stages of SARS and studied the potential prognostic implications of such an approach.  We developed two real-time quantitative reverse transcription-PCR (RT-PCR) assays, one for the polymerase and the other for the nucleocapsid region of the virus genome, for measuring the concentration of SARS-CoV RNA in serum/plasma samples from SARS patients. Plasma samples were obtained from 12 confirmed SARS patients on the day of hospital admission, as well as on days 7 and 14 after fever onset. Serum samples were also obtained from 23 confirmed SARS patients on the day of hospital admission, 11 of whom subsequently required intensive care. Viral RNA was extracted from the plasma/serum samples. The extracted RNA was subjected to analysis by the RT-PCR assays.  The RT-PCR system for the polymerase region detected SARS-CoV RNA in 50% of plasma and 78% of serum samples from SARS patients during the first week of illness. The detection rates for plasma dropped to 25% at day 14 after fever onset. The median serum SARS-CoV concentrations in patients who required and did not require intensive care unit admission during the course of hospitalization were 5800 and 140 copies/mL, respectively (Mann–Whitney test, P <0.005). These data were confirmed by the RT-PCR system for the nucleocapsid region, which showed an even higher detection rate of 87%. The correlation between the results obtained by the two RT-PCR systems was high (Pearson correlation analysis, r = 0.998; P <0.001).  Plasma/serum SARS-CoV quantification represents a potentially useful early diagnostic and prognostic tool for SARS.

title: Clinical Characteristics of Hospitalized Patients with SARS-CoV-2 and Hepatitis B virus Co-infection
abstract: The coronavirus disease 2019 (COIVD-19) caused by SARS-CoV-2 has been characterized as a pandemic, which causes a serious public health challenge in the world. A very large group of patients infected by HBV has been reported worldwide, especially in China. In order to answer whether specific treatment strategy on the patients coinfected with HBV and SARS-CoV-2, it requires profound understanding of the clinical characteristics on those patients. However, the impacts of SARS-CoV-2 infection on HBV patients remain largely unknown.  In this retrospective investigation, we included 123 COVID-19 patients admitted to Zhongnan Hospital of Wuhan University, Wuhan, China, from January 5 to March 7, 2020. All enrolled patients are the laboratory confirmed COVID-19 pneumonia cases according to the criteria reported previously. A total of 123 patients were analyzed for their Clinical records, laboratory results including the diagnosis of HBV infection and liver function. Among 123 confirmed COVID-19 patients, the mean age was 51 years old and 59.3% were females (73/123). Fifteen were previously HBV infected patients, 66.7% of them were males (10/15), patients with HBV infection appeared to have a higher incidence of liver cirrhosis and an increased level of total bilirubin. Seven (46.7%) patients with HBV infection were defined as severe cases, while the severity rate was 24.1% for the patients without HBV infection (26/108). The mortality of patients with HBV infection was 13.3% (2/15) compared to 2.8% (3/108) for the patients without HBV infection.  SARS-CoV-2 infection may cause liver function damage in COVID-19 cases and the patients with HBV infection are likely to have more severe disease outcome.

title: Author Correction: Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing
abstract: [This corrects the article DOI: 10.1038/s41421-020-0168-9.].

title: Examining the relationship between infectious diseases and flooding in Europe: A systematic literature review and summary of possible public health interventions
abstract: Many infectious diseases are sensitive to climatic changes; specifically, flooding. This systematic literature review aimed to strengthen the quality and completeness of evidence on infectious diseases following flooding, relevant to Europe.  A systematic literature review from 2004–2012 was performed. Focused searches of the following databases were conducted: Medline, Scopus, PubMed, Cochrane Library, and Evidence Aid. Personal communications with key informants were also reviewed.  Thirty-eight studies met the inclusion criteria. Evidence suggested that water-borne, rodent-borne, and vector-borne diseases have been associated with flooding in Europe, although at a lower incidence than developing countries.  Disease surveillance and early warning systems, coupled with effective prevention and response capabilities, can reduce current and future vulnerability to infectious diseases following flooding.

title: Relative antioxidant activities of quercetin and its structurally related substances and their effects on NF-κB/CRE/AP-1 signaling in murine macrophages
abstract: Reactive oxygen species (ROS) and reactive nitrogen species (RNS) produced by the oxidative burst in activated macrophages and neutrophils cause oxidative stressimplicated diseases. Quercetin is flavonoid that occurs naturally in plants and is widely used as a nutritional supplement due to its antioxidant and anti-inflammatory properties. In this study, we investigated antioxidant activities and mechanisms of action in zymosan-induced macrophages of quercetin and quercetin-related flavonoids such as quercitrin, isoquercitrin, quercetin 3-O-β-(2″-galloyl)-rhamnopyranoside (QGR) and quercetin 3-O-β-(2″-galloyl)-glucopyranoside (QGG) as well as gallic acid, a building moiety of QGR and QGG. QGR and QGG exhibited stronger antioxidant activities compared with quercetin, whereas quercitrin, isoquercitrin and gallic acid exhibited weak-tono antioxidant activities, assessed by 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, superoxide production, superoxide scavenging, nitric oxide (NO) production, peroxynitrite (ONOO(−)) scavenging and myeloperoxidase (MPO) activity. Regarding mechanisms, the quercetincontaining flavonoids QGR and QGG differentially targeted compared with quercetin in the NF-κB signaling pathway that inhibited the DNA binding activity of the NF-κB complex without affecting the degradation and phosphorylation of IκBα and NF-κB phosphorylation. In addition, QGR and QGG inhibited CRE and activator protein (AP-1) transcriptional activity and JNK phosphorylation by inhibiting the cAMP/protein kinase A (PKA) and protein kinase C (PKC) signaling in a different manner than quercetin. Our results showed that although QGR and QGG exhibited stronger antioxidant activities than querce-tin in macrophages, their mechanisms of action in terms of the NF-κB, PKA and PKC signaling pathways were different.

title: COVID19-Tracker: una aplicación Shiny para analizar datos de la epidemia de SARS-CoV-2 en España./ [COVID19-Tracker: a shiny app to analise data on SARS-CoV-2 epidemic in Spain]
abstract: Data visualization is an important tool for exploring and communicating findings in medical research, and specially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases and mortality, from February 24th, 2020 onwards. Three applications have already been developed: 1) to analyze data trends and estimating short-term projections; 2) to estimate the case fatality rate; and 3) to assess the effect of the lockdowns on the data trends. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain.

title: A publishing pandemic during the COVID-19 pandemic: how challenging can it become?

title: Impacts of Early Interventions on the Age-Specific Incidence of COVID-19 in New York, Los Angeles, Daegu and Nairobi
abstract: Background COVID-19 has caused an unprecedented public health crisis and economic shock to the global economy. While many countries were affected, regions with an older population and weaker public health interventions tended to suffer more morbidity and mortality. Here we model and quantify the age-specific incidence of COVID-19 in four pandemic cities under different interventions.  We developed an age-specific and multiple-stage susceptible-exposed-infected-recovered-hospitalized-quarantined-dead (SEIR-HQD) dynamical systems model expanded from the more basic SEIR model by incorporating location- and age-specific contact matrices to estimate the outcomes of COVID-19. Utilizing latest estimates of epidemiological parameters and demographic data, we model the potential effects of various interventions in four representative cities with different population structures - New York, Los Angeles, Daegu and Nairobi. We compared the effects of different interventions in the age-structure populations specific to each city. These policy options are then applied to determine the potential for effective containment. We model these dynamic policy scenarios to assess the risks of less-stringent social distancing, as has been proposed by those arguing to enhance economic activity over public health and safety. Finally, we explored the health impacts of different policy action timelines to understand the benefits of early interventions.  We find the spread of COVID-19 to be dramatically different in the regions modeled, with the primary drivers the variation of population age structures, and the dynamics of interactions of the younger demographics, whose higher interaction rates can lead to increasing transmission rates across these communities. A city with younger citizens may also have fewer hospitalized cases and deaths. Our modeling quantifies the value of early interventions, which avoided an additional 5%, 16%, 37% and 43% of the infections in Daegu, Nairobi, New York and Los Angeles, respectively, compared to what has been observed in the four cities. The finding is clear: in the absence of pharmaceutical options, delaying strict social policy interventions has resulted in substantial public health cost. This modeling can, and will, be applied to other cities and regions, and conducted in conjunction with other health insults, such as exposure to air pollution. Critically, we find that school closures, working from home, and reduction in other mobility were most beneficial for younger population (0-19 years old), middle-age (20-59 years old) population and older population (60 years and older), respectively across each city. Specifically, school closure avoided 25%, 18%, 16% and 12% of the infections for the population under 20 years old in Daegu, Los Angeles, New York and Nairobi, respectively. A 50% and 80% population working from home policy avoids 8% and 15% of the infections. Reduction in mobility was more effective than the working from home strategy. Any single social distancing policy if enacted alone can delay the spread of COVID-19 but was unable to totally suppress the infection. Coordinated policy action can be highly effective. Increasing the quarantine rate to 10% of infectious cases was more effective than strict social distancing alone in this study, although together they can suppress 80% of the epidemic. A combination of moderate social distancing and quarantine strategies was able to avoid 99% of the infections.  Moderate social distancing together with high quarantine rates was effective in each of the four cities. COVID-19 caused more deaths and hospitalization in cities with an ageing population than those with a younger population. However, in the cities with a younger population, there is a clear need to implement a social distancing strategy that is even more strict due to the higher transmission rates among younger people. Cities with more older people should prepare more hospital beds and healthcare facilities to save people who are in critical conditions. Cities with ageing population should take targeted action for the elderly to avoid the severe impacts on the vulnerable populations. Increasing quarantine rate is an effective strategy to avoid the substantial infection while also does not influence the economy fiercely. We recommend countries or regions experiencing, or likely to experience the rapid spread of COVID-19, to implement combination of multiple strategies in the early stage of the breakout which can avoid over 90% of infected cases.

title: The effect of corticosteroid treatment on patients with coronavirus infection: a systematic review and meta-analysis
abstract: An outbreak of novel coronavirus in 2019 threatens the health of people, and there is no proven pharmacological treatment. Although corticosteroids were widely used during outbreaks of severe acute respiratory syndrome and Middle East respiratory syndrome, their efficacy remainedhighly controversial. We aimed to further evaluate the influence of corticosteroids on patients with coronavirus infection.  We conducted a comprehensive search of literature published in PubMed, Embase, Cochrane library, and China National Knowledge Infrastructure (CNKI) from January 1, 2002 to March 15, 2020. All statistical analyses in this study were performed on stata14.0.  A total of 5270 patients from 15 studies were included in this meta-analysis. The result indicated that critical patients were more likely to require corticosteroids therapy (risk ratio [RR] = 1.56, 95% confidence interval [CI] = 1.28-1.90, P<0.001). However, corticosteroid treatment was associated with higher mortality (RR = 2.11, 95%CI = 1.13-3.94, P = 0.019), longer length of stay (weighted mean difference [WMD] = 6.31, 95%CI = 5.26–7.37, P<0.001), a higher rate of bacterial infection (RR = 2.08, 95%CI = 1.54–2.81, P<0.001), and hypokalemia (RR = 2.21, 95%CI = 1.07–4.55, P = 0.032) but not hyperglycemia (RR = 1.37, 95%CI=0.68–2.76, P = 0.376) or hypocalcemia (RR = 1.35, 95%CI = 0.77–2.37, P = 0.302).  Patients with severe conditions are more likely to require corticosteroids. Corticosteroid use is associated with increased mortality in patients with coronavirus pneumonia.

title: Comparative analysis of complete genome sequences of three avian coronaviruses reveals a novel group 3c coronavirus.
abstract: In this territory-wide molecular epidemiology study of coronaviruses (CoVs) in Hong Kong involving 1,541 dead wild birds, three novel CoVs were identified in three different bird families (bulbul CoV HKU11 [BuCoV HKU11], thrush CoV HKU12 [ThCoV HKU12], and munia CoV HKU13 [MuCoV HKU13]). Four complete genomes of the three novel CoVs were sequenced. Their genomes (26,396 to 26,552 bases) represent the smallest known CoV genomes. In phylogenetic trees constructed using chymotrypsin-like protease (3CL(pro)), RNA-dependent RNA polymerase (Pol), helicase, spike, and nucleocapsid proteins, BuCoV HKU11, ThCoV HKU12, and MuCoV HKU13 formed a cluster distantly related to infectious bronchitis virus and turkey CoV (group 3a CoVs). For helicase, spike, and nucleocapsid, they were also clustered with a CoV recently discovered in Asian leopard cats, for which the complete genome sequence was not available. The 3CL(pro), Pol, helicase, and nucleocapsid of the three CoVs possessed higher amino acid identities to those of group 3a CoVs than to those of group 1 and group 2 CoVs. Unique genomic features distinguishing them from other group 3 CoVs include a distinct transcription regulatory sequence and coding potential for small open reading frames. Based on these results, we propose a novel CoV subgroup, group 3c, to describe this distinct subgroup of CoVs under the group 3 CoVs. Avian CoVs are genetically more diverse than previously thought and may be closely related to some newly identified mammalian CoVs. Further studies would be important to delineate whether the Asian leopard cat CoV was a result of interspecies jumping from birds, a situation analogous to that of bat and civet severe acute respiratory syndrome CoVs.

title: COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning
abstract: One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image. In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from various sources are collected, and the largest publicly accessible dataset is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized for developing COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.

title: COVID-19 Is a Data Science Issue

title: A comprehensive collection of systems biology data characterizing the host response to viral infection
abstract: The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). By comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection.

title: English dictionaries, gold and silver standard corpora for biomedical natural language processing related to SARS-CoV-2 and COVID-19
abstract: Here we present a toolbox for natural language processing tasks related to SARS-CoV-2. It comprises English dictionaries of synonyms for SARS-CoV-2 and COVID-19, a silver standard corpus generated with the dictionaries and a gold standard corpus of 10 Pubmed abstracts manually annotated for disease, virus, symptom and protein/gene terms. This toolbox is freely available on github (on https://github.com/Aitslab/corona) and can be used for text analytics in a variety of settings related to the COVID-19 crisis. It will be expanded and applied in NLP tasks over the next weeks and the community is invited to contribute.

title: JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
abstract: Recently, the novel coronavirus 2019 (COVID-19) has caused a pandemic disease over 200 countries, influencing billions of humans. To control the infection, the first and key step is to identify and separate the infected people. But due to the lack of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, it is essential to discover suspected COVID-19 patients via CT scan analysis by radiologists. However, CT scan analysis is usually time-consuming, requiring at least 15 minutes per case. In this paper, we develop a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID-19 diagnosis. To train our JCS system, we construct a large scale COVID-19 Classification and Segmentation (COVID-CS) dataset, with 144,167 CT images of 400 COVID-19 patients and 350 uninfected cases. 3,855 CT images of 200 patients are annotated with fine-grained pixel-level labels, lesion counts, infected areas and locations, benefiting various diagnosis aspects. Extensive experiments demonstrate that, the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.3% Dice score on the segmentation test set, of our COVID-CS dataset. The online demo of our JCS diagnosis system will be available soon.

title: What Types of COVID-19 Conspiracies are Populated by Twitter Bots?
abstract: With people moving out of physical public spaces due to containment measures to tackle the novel coronavirus (COVID-19) pandemic, online platforms become even more prominent tools to understand social discussion. Studying social media can be informative to assess how we are collectively coping with this unprecedented global crisis. However, social media platforms are also populated by bots, automated accounts that can amplify certain topics of discussion at the expense of others. In this paper, we study 43.3M English tweets about COVID-19 and provide early evidence of the use of bots to promote political conspiracies in the United States, in stark contrast with humans who focus on public health concerns.

title: Using altmetrics for detecting impactful research in quasi-zero-day time-windows: the case of COVID-19
abstract: On December 31st 2019, the World Health Organization (WHO) China Country Office was informed of cases of pneumonia of unknown etiology detected in Wuhan City. The cause of the syndrome was a new type of coronavirus isolated on January 7th 2020 and named Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2). SARS-CoV-2 is the cause of the coronavirus disease 2019 (COVID-19). Since January 2020 an ever increasing number of scientific works have appeared in literature. Identifying relevant research outcomes at very early stages is challenging.In this work we use COVID-19 as a use-case for investigating: (i) which toos and frameworkds are mostly used for early scholarly communication; (ii) to what extent altmetrics can be used to identify potential impactful research in tight (i.e. {\em quasi-zero-day}) time-windows. A literature review with rigorous eligibility criteria is performed for gathering a sample composed of scientific papers about SARS-CoV-2/COVID-19 appeared in literature in the tight time-window ranging from January 15th 2020 to February 24th 2020. This sample is used for building a knowledge graph that represents the knowledge about papers and indicators formally. This knowledge graph feeds a data analysis process which is applied for experimenting with altmetrics as impact indicators. We find moderate correlation among traditional citation count, citations on social media, and mentions on news and blogs. This suggests there is a common intended meaning of the citational acts associated with aforementioned indicators. Additionally, we define a method that harmonises different indicators for providing a multi-dimensional impact indicator.

title: Inbetweening auto-animation via Fokker-Planck dynamics and thresholding
abstract: We propose an equilibrium-driven deformation algorithm (EDDA) to simulate the inbetweening transformations starting from an initial image to an equilibrium image, which covers images varying from a greyscale type to a colorful type on plane or manifold. The algorithm is based on Fokker-Planck dynamics on manifold, which automatically cooperates positivity, unconditional stability, mass conservation law, exponentially convergence and also the manifold structure suggested by dataset. The thresholding scheme is adapted for the sharp interface dynamics and is used to achieve the finite time convergence. Using EDDA, three challenging examples, (I) facial aging process, (II) coronavirus disease 2019 (COVID-19) invading/treatment process, and (III) continental evolution process are conducted efficiently.

title: Unlikely SARS-CoV-2 vertical transmission from mother to child: A case report
abstract: As the 2019 novel coronavirus disease (COVID-19) rapidly spread across China and to more than 70 countries, an increasing number of pregnant women were affected. The vertical transmission potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of great concern to the obstetrics, neonatologists, and public health agencies. Though some studies indicated the risk of vertical transmission is low, few cases have been reported with comprehensive serial tests from multiple specimens. In this case, a female preterm infant was born to a mother with confirmed COVID-19. She presented with mild respiratory distress and received general management and a short period of nasal continuous positive airway pressure support. During her stay at the hospital, a series of SARS-CoV-2 nucleic test from her throat and anal swab, serum, bronchoalveolar lavage fluid, and urine were negative. The nucleic acid test from the mother's amniotic fluid, vaginal secretions, cord blood, placenta, serum, anal swab, and breast milk were also negative. The most comprehensively tested case reported to date confirmed that the vertical transmission of COVID is unlikely, but still, more evidence is needed.

title: A Model Based Analysis for COVID-19 Pandemic in India: Implications for Health Systems and Policy for Low- and Middle-Income Countries
abstract: Our analysis aims to model COVID-19 pandemic in India, potential impact of various measures, along with assessment of health system preparedness and cost to manage the epidemic.  We developed a susceptible-exposed-infectious-recovered (SEIR) mathematical model to predict the health outcomes under an unmitigated scenario which comprises of air travel restrictions alone, and the current scenario consisting of air travel restrictions along with 8-week lockdown. In addition, we also evaluate the effectiveness of 8-week lockdown along with intensified public health measures at varying level of effectiveness. We assessed the impact of these interventions on COVID-19 related health outcomes in comparison to the unmitigated scenario. Next, we ascertain the need for augmenting infrastructure and the costs of COVID-19 management in India.  In the event of a lockdown for 8 weeks, the peak of the epidemic shifts by 34-76 days, and the number of cases at the end of 8-week lockdown reduces by 69% to 97% with varying effectiveness of lockdown. However, the cumulative long-term cases remain the same. Intensification of public health surveillance measures with 60% effectiveness is estimated to reduce the cases at peak and cumulative number of infections by 70% and 26.6% respectively. The requirement of ICU beds and ventilators would reduce by 83% with intensified public health measures. The cost of managing COVID-19 in India is nearly 4.5% of the gross domestic product (GDP) in the absence of any intervention which increases to 6.2% with intensified public health measures for COVID-19 response.  Lockdown measures delay the onset of peak, and give much needed time to health system to prepare. Strengthening the public health system response in terms of testing, isolation treatment of cases, and contact tracing needs would lead to significant gains in terms of case load, and meeting health system needs.

title: A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017
abstract: because even the same facts may lead to conflicting understandings if deliberate framing is employed. Therefore, identifying media framing is a crucial step to understanding how news media influence the public. Framing is, however, difficult to operationalize and detect, and thus traditional media framing studies had to rely on manual annotation, which is challenging to scale up to massive news datasets. Here, by developing a media frame classifier that achieves state-of-the-art performance, we systematically analyze the media frames of 1.5 million New York Times articles published from 2000 to 2017. By examining the ebb and flow of media frames over almost two decades, we show that short-term frame abundance fluctuation closely corresponds to major events, while there also exist several long-term trends, such as the gradually increasing prevalence of the ``Cultural identity'' frame. By examining specific topics and sentiments, we identify characteristics and dynamics of each frame. Finally, as a case study, we delve into the framing of mass shootings, revealing three major framing patterns. Our scalable, computational approach to massive news datasets opens up new pathways for systematic media framing studies.

title: The site of origin of the 1918 influenza pandemic and its public health implications

title: Transcriptional landscape of SARS-CoV-2 infection dismantles pathogenic pathways activated by the virus, proposes unique sex-specific differences and predicts tailored therapeutic strategies
abstract: The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) has posed a serious threat to global health. As no specific therapeutics are yet available to control disease evolution, more in-depth understanding of the pathogenic mechanisms induced by SARS-CoV-2 will help to characterize new targets for the management of COVID-19. The present study identified a specific set of biological pathways altered in primary human lung epithelium upon SARS-CoV-2 infection, and a comparison with SARS-CoV from the 2003 pandemic was studied. The transcriptomic profiles were also exploited as possible novel therapeutic targets, and anti-signature perturbation analysis predicted potential drugs to control disease progression. Among them, Mitogen-activated protein kinase kinase (MEK), serine-threonine kinase (AKT), mammalian target of rapamycin (mTOR) and I kappa B Kinase (IKK) inhibitors emerged as candidate drugs. Finally, sex-specific differences that may underlie the higher COVID-19 mortality in men are proposed.

title: Detection and full genome characterization of two beta CoV viruses related to Middle East respiratory syndrome from bats in Italy
abstract: Middle East respiratory syndrome coronavirus (MERS-CoV), which belongs to beta group of coronavirus, can infect multiple host species and causes severe diseases in humans. Multiple surveillance and phylogenetic studies suggest a bat origin. In this study, we describe the detection and full genome characterization of two CoVs closely related to MERS-CoV from two Italian bats, Pipistrellus kuhlii and Hypsugo savii.  Pool of viscera were tested by a pan-coronavirus RT-PCR. Virus isolation was attempted by inoculation in different cell lines. Full genome sequencing was performed using the Ion Torrent platform and phylogenetic trees were performed using IQtree software. Similarity plots of CoV clade c genomes were generated by using SSE v1.2. The three dimensional macromolecular structure (3DMMS) of the receptor binding domain (RBD) in the S protein was predicted by sequence-homology method using the protein data bank (PDB).  Both samples resulted positive to the pan-coronavirus RT-PCR (IT-batCoVs) and their genome organization showed identical pattern of MERS CoV. Phylogenetic analysis showed a monophyletic group placed in the Beta2c clade formed by MERS-CoV sequences originating from humans and camels and bat-related sequences from Africa, Italy and China. The comparison of the secondary and 3DMMS of the RBD of IT-batCoVs with MERS, HKU4 and HKU5 bat sequences showed two aa deletions located in a region corresponding to the external subdomain of MERS-RBD in IT-batCoV and HKU5 RBDs.  This study reported two beta CoVs closely related to MERS that were obtained from two bats belonging to two commonly recorded species in Italy (P. kuhlii and H. savii). The analysis of the RBD showed similar structure in IT-batCoVs and HKU5 respect to HKU4 sequences. Since the RBD domain of HKU4 but not HKU5 can bind to the human DPP4 receptor for MERS-CoV, it is possible to suggest also for IT-batCoVs the absence of DPP4-binding potential. More surveillance studies are needed to better investigate the potential intermediate hosts that may play a role in the interspecies transmission of known and currently unknown coronaviruses with particular attention to the S protein and the receptor specificity and binding affinity.

title: Vaginal delivery report of a healthy neonate born to a convalescent mother with COVID­‐19
abstract: The outbreak of the infection of 2019 novel coronavirus disease (COVID­‐19) has become a challenging public health threat worldwide. Limited data are available for pregnant women with COVID‐19 pneumonia. We report a case of a convalescing pregnant woman diagnosed with COVID‐19 infection 37 days before delivery in the third trimester. A live birth without severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection was performed successfully via the vagina. The findings from our case indicate that there is no intrauterine transmission in this woman who developed COVID‐19 pneumonia in late pregnancy.

title: Structural basis of a public antibody response to SARS-CoV-2
abstract: Molecular-level understanding of human neutralizing antibody responses to SARS-CoV-2 could accelerate vaccine design and facilitate drug discovery. We analyzed 294 SARS-CoV-2 antibodies and found that IGHV3–53 is the most frequently used IGHV gene for targeting the receptor binding domain (RBD) of the spike (S) protein. We determined crystal structures of two IGHV3–53 neutralizing antibodies +/− Fab CR3022 ranging from 2.33 to 3.11 Å resolution. The germline-encoded residues of IGHV3–53 dominate binding to the ACE2 binding site epitope with no overlap with the CR3022 epitope. Moreover, IGHV3–53 is used in combination with a very short CDR H3 and different light chains. Overall, IGHV3–53 represents a versatile public VH in neutralizing SARS-CoV-2 antibodies, where their specific germline features and minimal affinity maturation provide important insights for vaccine design and assessing outcomes.

title: Molecular Diagnosis of a Novel Coronavirus (2019-nCoV) Causing an Outbreak of Pneumonia
abstract: A novel coronavirus of zoonotic origin (2019-nCoV) has recently been identified in patients with acute respiratory disease. This virus is genetically similar to SARS coronavirus and bat SARS-like coronaviruses. The outbreak was initially detected in Wuhan, a major city of China, but has subsequently been detected in other provinces of China. Travel-associated cases have also been reported in a few other countries. Outbreaks in health care workers indicate human-to-human transmission. Molecular tests for rapid detection of this virus are urgently needed for early identification of infected patients.  We developed two 1-step quantitative real-time reverse-transcription PCR assays to detect two different regions (ORF1b and N) of the viral genome. The primer and probe sets were designed to react with this novel coronavirus and its closely related viruses, such as SARS coronavirus. These assays were evaluated using a panel of positive and negative controls. In addition, respiratory specimens from two 2019-nCoV-infected patients were tested.  Using RNA extracted from cells infected by SARS coronavirus as a positive control, these assays were shown to have a dynamic range of at least seven orders of magnitude (2x10(−4)-2000 TCID(50)/reaction). Using DNA plasmids as positive standards, the detection limits of these assays were found to be below 10 copies per reaction. All negative control samples were negative in the assays. Samples from two 2019-nCoV-infected patients were positive in the tests.  The established assays can achieve a rapid detection of 2019n-CoV in human samples, thereby allowing early identification of patients.

title: Estimation of Evolutionary Dynamics and Selection Pressure in Coronaviruses
abstract: Evolution of coronaviruses is facilitated by the strong selection, large population size, and great genetic diversity within the susceptible hosts. This predisposition is primarily due to high error rate, and limited proofreading capability of the viral polymerase and by recombination. These characteristics make coronaviruses an interesting model system to study the mechanisms involved in viral evolution and the ways viruses adapt to switch host or to gain novel functions. Here we describe the protocol to estimate selection pressures for the spike gene and evolutionary dynamics of bovine coronaviruses.

title: COVID-19 in Children in the United States: Intensive Care Admissions, Estimated Total Infected, and Projected Numbers of Severe Pediatric Cases in 2020
abstract: A surge in severe cases of COVID-19 (coronavirus disease 2019) in children would present unique challenges for hospitals and public health preparedness efforts in the United States.  To provide evidence-based estimates of children infected with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and projected cumulative numbers of severely ill pediatric COVID-19 cases requiring hospitalization during the US 2020 pandemic.  Empirical case projection study.  Adjusted pediatric severity proportions and adjusted pediatric criticality proportions were derived from clinical and spatiotemporal modeling studies of the COVID-19 epidemic in China for the period January-February 2020. Estimates of total children infected with SARS-CoV-2 in the United States through April 6, 2020, were calculated using US pediatric intensive care unit (PICU) cases and the adjusted pediatric criticality proportion. Projected numbers of severely and critically ill children with COVID-19 were derived by applying the adjusted severity and criticality proportions to US population data, under several scenarios of cumulative pediatric infection proportion (CPIP).  By April 6, 2020, there were 74 children who had been reported admitted to PICUs in 19 states, reflecting an estimated 176 190 children nationwide infected with SARS-CoV-2 (52 381 infants and toddlers younger than 2 years, 42 857 children aged 2-11 years, and 80 952 children aged 12-17 years). Under a CPIP scenario of 5%, there would be 3.7 million children infected with SARS-CoV-2, 9907 severely ill children requiring hospitalization, and 1086 critically ill children requiring PICU admission. Under a CPIP scenario of 50%, 10 865 children would require PICU admission, 99 073 would require hospitalization for severe pneumonia, and 37.0 million would be infected with SARS-CoV-2.  Because there are 74.0 million children 0 to 17 years old in the United States, the projected numbers of severe cases could overextend available pediatric hospital care resources under several moderate CPIP scenarios for 2020 despite lower severity of COVID-19 in children than in adults.

title: Statistical and network-based analysis of Italian COVID-19 data: communities detection and temporal evolution
abstract: Coronavirus disease (COVID-19) outbreak started at Wuhan, China, and it has rapidly spread across China and many other countries. Italy is one of the European countries most affected by the COVID-19 disease, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period February 24 to March 29, 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices (reported in Supplementary file). Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behaviour. Then, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behaviour. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e. how they join and leave them, along time and how community consistency changes along time and with respect to the different available data.

title: COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts
abstract: The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The results confirm a rapid increase in the volume of research, which particularly accessible through Google Scholar and Dimensions, and less through Scopus, the Web of Science, PubMed. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Google Scholar or Dimensions and can use tweet and Mendeley reader counts as indicators of likely importance.

title: Automatic Text Summarization of COVID-19 Medical Research Articles using BERT and GPT-2
abstract: With the COVID-19 pandemic, there is a growing urgency for medical community to keep up with the accelerating growth in the new coronavirus-related literature. As a result, the COVID-19 Open Research Dataset Challenge has released a corpus of scholarly articles and is calling for machine learning approaches to help bridging the gap between the researchers and the rapidly growing publications. Here, we take advantage of the recent advances in pre-trained NLP models, BERT and OpenAI GPT-2, to solve this challenge by performing text summarization on this dataset. We evaluate the results using ROUGE scores and visual inspection. Our model provides abstractive and comprehensive information based on keywords extracted from the original articles. Our work can help the the medical community, by providing succinct summaries of articles for which the abstract are not already available.

title: Searching therapeutic strategy of new coronavirus pneumonia from angiotensin-converting enzyme 2: the target of COVID-19 and SARS-CoV
abstract: Since December 2019, the infection of the new coronavirus (COVID-19) caused an outbreak of new coronavirus pneumonia in Wuhan, China, and caused great public concern. Both COVID-19 and SARS-CoV belong to the coronavirus family and both invade target cells through ACE2. An in-depth understanding of ACE2 and a series of physiological and physiological changes caused by the virus invading the human body may help to discover and explain the corresponding clinical phenomena and then deal with them timely. In addition, ACE2 is a potential therapeutic target. This article will summarize the role of ACE2 in multiple organ damage caused by COVID-19 and SARS-CoV, targeted blocking drugs against ACE2, and drugs that inhibit inflammation in order to provide the basis for subsequent related research, diagnosis and treatment, and drug development.

title: Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance
abstract: Free or low-cost sources of unstructured information, such as Internet news and online discussion sites, provide detailed local and near real-time data on disease outbreaks, even in countries that lack traditional public health surveillance. To improve public health surveillance and, ultimately, interventions, we examined 3 primary systems that process event-based outbreak information: Global Public Health Intelligence Network, HealthMap, and EpiSPIDER. Despite similarities among them, these systems are highly complementary because they monitor different data types, rely on varying levels of automation and human analysis, and distribute distinct information. Future development should focus on linking these systems more closely to public health practitioners in the field and establishing collaborative networks for alert verification and dissemination. Such development would further establish event-based monitoring as an invaluable public health resource that provides critical context and an alternative to traditional indicator-based outbreak reporting.

title: A rate equation approach to model the denaturation or replication behavior of the SARS coronavirus
abstract: As a newly emerging virus, little is known about the SARS coronavirus, whose outbreak has brought away several hundred people’s lives over the world in the year of 2003 and is seriously imperiling the human health. Revealing the denaturation and replication mechanisms of SARS coronavirus has great importance for successfully fighting SARS. However, experiments related to SARS coronavirus are extremely dangerous and therefore restricted only to certain specific labs with high safety standard. Clearly, predicting the behaviors of SARS coronavirus in a wide variety of environmental conditions, which are not easily accessible, are thus critically necessary. In this study, we proposed to quantify the survival time of SARS coronavirus either in vitro or in vivo, through introducing thermal rate process models established from the well-known Arrhenius law. The complex physical and chemical behaviors of the SARS coronavirus can then be attributed to its activation energy, frequency factor, damage function as well as the surrounding environmental conditions. Based on the first data on stability and resistance of SARS coronavirus measured by members of WHO laboratory network, the rate coefficients involved in the above equations were estimated for the first time. Predictions on the survival time of SARS coronavirus in different temperature scale were then performed. It was found theoretically that, such survival time falls in an extremely wide range, say from several seconds in high temperature to an almost infinitely long time in a low temperature environment, which has already or is being supported by the currently available tests data. Applications of the present theory to interpret several existing phenomena were presented and their implementations in developing new technical ways for SARS prevention and clinical therapy were discussed. Uncertainties involved in the theoretical models were also analyzed and predicted. Parametric studies were performed to test the effects of the rate coefficients to the survival time of SARS coronavirus. Some important factors, which can significantly vary the denaturation or replication process of SARS coronavirus were pointed out. Through regulating the parameters involved in the equation, certain potential therapies either through drug delivery or engineering approach to treat the SARS disease can possibly be established. Extension of the present model for further studies was also suggested. This study opens a new theoretical way for probing into the complex behaviors of SARS coronavirus. Modellierung der Denaturierung oder Repliziryng von SARS-Korona-Viren Zusammenfassung Der Kenntnisstand über die Eigenschaften des in 2003 neu aufgetretenen SARS Korona Virus, der einige Hundert Menschenleben gekostet hat, ist relativ gering. Die Ermittlung des Denaturierungs- und Replizierungsmechanismuses des SARS Virus ist für seine Bekämpfung von hoher Bedeutung. Experimentelle Untersuchungen an diesem extrem gefährlichen Virus dürfen nur durch Laboratorien mit einem hohen Sicherheitsstandard erfolgen. Die Vorhersage des Verhaltens des SARS Virus in unterschiedlichen Umgebungsbedingungen ist dabei erforderlich. In der vorliegenden Studie wird die überlebensdauer des Virus unter Labor- und realen Bedingungen durch Anwendung der bekannten Arrhenius-Beziehung für temperaturabhängige Vorgänge ermittelt. Das physikalische und chemische Verhalten des SARS Virus wird anhand der zugrundeliegenden Modell- Parameter beschrieben. Basierend auf den ersten Messungen von Mitgliedern des WHO-laboratory-network über die Stabilität und Widerstandsfähigkeit des Virus wurden erstmalig die Geschwindigkeitskoeffizienten des Berechnungsmodells bestimmt. Vorhersagen der Überlebensdauer des SARS-Virus unter unterschiedlichen Temperaturbedingungen wurden ausgeführt. Das sich hieraus ergebende, sehr unterschiedliche Ausmaß der Überlebensfähigkeit in Abhängigkeit der Umgebungstemperatur ist durch den Vergleich mit verfügbaren experimentellen Ergebnissen bestätigt worden. Die Anwendung der vorgestellten Modellierung zur Interpretation realer Phänomene und zur Entwicklung technischer Maßnahmen zur Vorbeugung und klinischen Therapierung von SARS wird diskutiert. Der Einfluß von Unsicherheiten des Modells wird analysiert und abgeschätzt. Parametrische Studien sind durchgeführt worden, um den Einfluß der Geschwindigkeitskoeffizienten auf die Überlebensdauer des SARS Virus darzustellen. Einige wichtige Einflußgrößen auf die Denaturierung und Replikationsfähigkeit des SARS Virus werden aufgezeigt. Durch eine Variation der Modellparameter kann die potentielle Wirksamkeit medikamentöser oder physikalischer Therapien abgeschätzt werden. Erweiterungsmöglichkeiten des vorgestellten Modells werden vorgeschlagen. Die vorliegende Studie ermöglicht neue, theoretische Vorgehensweisen zur Untersuchung des komplexen Verhaltensmusters des SARS Virus.

title: Improving an Escherichia coli-based biocatalyst for terpenol glycosylation by variation of the expression system
abstract: Glycosides are becoming increasingly more relevant for various industries as low-cost whole-cell-biocatalysts are now available for the manufacture of glycosides. However, there is still a need to optimize the biocatalysts. The aim of this work was to increase the titre of terpenyl glucosides in biotransformation assays with E. coli expressing VvGT14ao, a glycosyltransferase gene from grape (Vitis vinifera). Seven expression plasmids differing in the resistance gene, origin of replication, promoter sequence, and fusion protein tag were generated and transformed into four different E. coli expression strains, resulting in 18 strains that were tested for glycosylation efficiency with terpenols and a phenol. E. coli BL21(DE3)/pET-SUMO_VvGT14ao yielded the highest titres. The product concentration was improved 8.6-fold compared with E. coli BL21(DE3)pLysS/pET29a_VvGT14ao. The selection of a small solubility-enhancing protein tag and exploitation of the T7 polymerase-induction system allowed the formation of increased levels of functional recombinant protein, thereby improving the performance of the whole-cell biocatalyst.  contains supplementary material, which is available to authorized users.

title: Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCov literature
abstract: During the current century, each major coronavirus outbreak has triggered a quick surge of academic publications on this topic. The spike in research publications following the 2019 Novel Coronavirus (Covid-19), however, has been like no other. The global crisis caused by the Covid-19 pandemic has mobilised scientific efforts in an unprecedented way. In less than five months, more than 12,000 research items have been indexed while the number increasing every day. With the crisis affecting all aspects of life, research on Covid-19 seems to have become a focal point of interest across many academic disciplines. Here, scientometric aspects of the Covid-19 literature are analysed and contrasted with those of the two previous major Coronavirus diseases, i.e. SARS and MERS. The focus is on the co-occurrence of key-terms, bibliographic coupling and citation relations of journals and collaborations between countries. Certain recurring patterns across all three literatures were discovered. All three outbreaks have commonly generated three distinct and major cohort of studies: (i) studies linked to the public health response and epidemic control, (ii) studies associated with the chemical constitution of the virus and (iii) studies related to treatment, vaccine and clinical care. While studies affiliated with the category (i) seem to have been the first to emerge, they overall received least numbers of citations compared to those of the two other categories. Covid-19 studies seem to have been distributed across a broader variety of journals and subject areas. Clear links are observed between the geographical origins of each outbreak or the local geographical severity of each outbreak and the magnitude of research originated from regions. Covid-19 studies also display the involvement of authors from a broader variety of countries compared to SARS and MRS.

title: Leveraging Data Preparation, HBase NoSQL Storage, and HiveQL Querying for COVID-19 Big Data Analytics Projects
abstract: Epidemiologist, Scientists, Statisticians, Historians, Data engineers and Data scientists are working on finding descriptive models and theories to explain COVID-19 expansion phenomena or on building analytics predictive models for learning the apex of COVID-19 confimed cases, recovered cases, and deaths evolution curves. In CRISP-DM life cycle, 75% of time is consumed only by data preparation phase causing lot of pressions and stress on scientists and data scientists building machine learning models. This paper aims to help reducing data preparation efforts by presenting detailed schemas design and data preparation technical scripts for formatting and storing Johns Hopkins University COVID-19 daily data in HBase NoSQL data store, and enabling HiveQL COVID-19 data querying in a relational Hive SQL-like style.

title: COVID-19 in a social reinsurance framework: Forewarned is forearmed
abstract: The crisis caused by COVID-19 revealed the global unpreparedness to handle the impact of a pandemic. In this paper, we present a statistical analysis of the data related to the COVID-19 outbreak in China, specifically the infection speed, death and fatality rates in Hubei province. By fitting distributions of these quantities we design a parametric reinsurance contract whose trigger and cap are based on the probability distributions of the infection speed, death and fatality rates. In particular, fitting the distribution for the infection speed and death rates we provide a measure of the effectiveness of a state's action during an epidemic, and propose a reinsurance contract as a supplement to a state's social insurance to alleviate financial costs.

title: Sense and Sensibility: Characterizing Social Media Users Regarding the Use of Controversial Terms for COVID-19
abstract: With the world-wide development of 2019 novel coronavirus, although WHO has officially announced the disease as COVID-19, one controversial term -"Chinese Virus"is still being used by a great number of people. In the meantime, global online media coverage about COVID-19-related racial attacks increases steadily, most of which are anti-Chinese or anti-Asian. As this pandemic becomes increasingly severe, more people start to talk about it on social media platforms such as Twitter. When they refer to COVID-19, there are mainly two ways: using controversial terms like"Chinese Virus"or"Wuhan Virus", or using non-controversial terms like"Coronavirus". In this study, we attempt to characterize the Twitter users who use controversial terms and those who use non-controversial terms. We use the Tweepy API to retrieve 17 million related tweets and the information of their authors. We find significant differences between these two groups of Twitter users across their demographics, user-level features like the number of followers, political following status, as well as their geo-locations. Moreover, we apply classification models to predict Twitter users who are more likely to use controversial terms. To our best knowledge, this is the first large-scale social media-based study to characterize users with respect to their usage of controversial terms during a major crisis.

title: Transcriptional Difference between SARS-COV-2 and other Human Coronaviruses Revealed by Sub-genomic RNA Profiling
abstract: SARS-COV-2 and all other coronaviruses express its 3 prime genes by forming sub-genomic RNA. As the genome of these virus exist in RNA form, only by profiling the relative abundance of these sgRNAs, can the viral transcriptome be revealed. Utilizing publically available meta-transcriptomic data generated from patient samples, we were able to infer the viral transcriptome in vivo, which is distinct from the in vitro one derived from cell culture. Inter-sample diversity was also observed and a sample specific transcript was identified. By doing the same analysis to MERS and SARS data, we were able to compare the three in terms of transcription. Among the differences, SARS-COV-2 has significantly elevated expression of the Spike gene, which may contribute to its high transmissibility.  The in vivo transcriptome of SARS-CoV-2 revealed by sgRNA profiling, for 25 patient samples around the globe. The Spike protein expression is an order of magnitude higher in SARS-CoV-2 than MERS-CoV or SARS-CoV, possibly contributing to the virus’ elevated transmissibility. The in vivo SARS-CoV-2 transcriptomes, as inferred from human patient data was distinct from the in vitro one derived from cell line culture, all the accessory genes were up-regulated in vivo, suggesting intricate expression regulation mechanism for the small viral genome.

title: Epidemiology of seasonal coronaviruses: Establishing the context for COVID-19 emergence
abstract: Public health preparedness for coronavirus disease 2019 (COVID-19) is challenging in the absence of setting-specific epidemiological data. Here we describe the epidemiology of seasonal coronaviruses (sCoVs) and other cocirculating viruses in the West of Scotland, UK. We analyzed routine diagnostic data for >70,000 episodes of respiratory illness tested molecularly for multiple respiratory viruses between 2005 and 2017. Statistical associations with patient age and sex differed between CoV-229E, CoV-OC43 and CoV-NL63. Furthermore, the timing and magnitude of sCoV outbreaks did not occur concurrently and coinfections were not reported. With respect to other cocirculating respiratory viruses, we found evidence of positive, rather than negative, interactions with sCoVs. These findings highlight the importance of considering cocirculating viruses in the differential diagnosis of COVID-19. Further work is needed to establish the occurrence/degree of cross-protective immunity conferred across sCoVs and with COVID-19, as well as the role of viral coinfection in COVID-19 disease severity.

title: Highly ACE2 Expression in Pancreas May Cause Pancreas Damage After SARS-CoV-2 Infection
abstract: The ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) started in the end of 2019 in China has triggered a global public health crisis. Previous studies have shown that SARS-CoV-2 infects cells by binding angiotensin-converting enzyme 2 (ACE2), which is the same as SARS-CoV. The expression and distribution of ACE2 in the pancreas are unknown. At the same time, the injury of pancreas after SARS-CoV-2 infection has not been concerned. Here, we collected public datasets (bulk RNA-seq and single-cell RNA-seq) to indicate the expression and the distribution of ACE2 in pancreas (in both exocrine glands and islets). And further, clinical data including mild and severe patients with COVID-19 demonstrated there existed mild pancreatitis. In the 67 severe cases, 11 patients (16.41%) showed elevated levels of both amylase and lipase, and 5 patients (7.46%) showed imaging alterations. Only one patient (1.85%) showed elevated levels of both amylase and lipase in 54 mild cases, without imaging changes. Our study revealed the phenomenon and possible cause of mild pancreatic injury in patients with COVID-19. This suggests that pancreatitis after SARS-CoV-2 infection should also be paid attention in clinical work.

title: A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms
abstract: The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries [1,2], as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360[3] and TCdata360[4] platforms. From these platforms, only annual indicators from 2015 and later were collected, and their missing values were replaced with previous annual values, in descending order by year, until 2015. During preprocessing, indicators (columns) were filtered out when the ratio of missing values exceeded 50%. Then, the same filtration was applied for the ratio of missing values above 25% in the case of countries (rows). Finally, duplicated variables were removed from the dataset. As a result of these steps, the missing value rate of the employed indicators was reduced to 4.25% on average. In addition to the database, the Kendall rank correlation matrix is provided to facilitate subsequent analysis. The dataset and the correlation matrix can be updated and customized with an R Notebook file, which is also available publicly in Mendeley Data .

title: Polymorphism of SARS-CoV Genomes
abstract: In this work, severe acute respiratory syndrome associated coronavirus (SARS-CoV) genome BJ202 (AY864806) was completely sequenced. The genome was directly accessed from the stool sample of a patient in Beijing. Comparative genomics methods were used to analyze the sequence variations of 116 SARS-CoV genomes (including BJ202) available in the NCBI Gen-Bank. With the genome sequence of GZ02 as the reference, there were 41 polymorphic sites identified in BJ202 and a total of 278 polymorphic sites present in at least two of the 116 genomes. The distribution of the polymorphic sites was biased over the whole genome. Nearly half of the variations (50.4%, 140/278) clustered in the one third of the whole genome at the 3′ end (19.0 kb-29.7 kb). Regions encoding Orf10–11, Orf3/4, E, M and S protein had the highest mutation rates. A total of 15 PCR products (about 6.0 kb of the genome) including 11 fragments containing 12 known polymorphic sites and 4 fragments without identified polymorphic sites were cloned and sequenced. Results showed that 3 unique polymorphic sites of BJ202 (positions 13 804, 15 031 and 20 792) along with 3 other polymorphic sites (26 428, 26 477 and 27 243) all contained 2 kinds of nucleotides. It is interesting to find that position 18379 which has not been identified to be polymorphic in any of the other 115 published SARS-CoV genomes is actually a polymorphic site. The nucleotide composition of this site is A (8) to G (6). Among 116 SARS-CoV genomes, 18 types of deletions and 2 insertions were identified. Most of them were related to a 300 bp region (27 700–28 000) which encodes parts of the putative ORF9 and ORF10–11. A phylogenetic tree illustrating the divergence of whole BJ202 genome from 115 other completely sequenced SARS-CoVs was also constructed. BJ202 was phylogeneticly closer to BJ01 and LLJ-2004.

title: BIP4COVID19: Releasing impact measures for articles relevant to COVID-19
abstract: Since the beginning of the 2019-20 coronavirus pandemic, a large number of relevant articles has been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies, or even government actions to limit the spread of the disease and treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate making the exploration of the relevant literature and the identification of useful knowledge in it challenging. In this work, we describe BIP4COVID19, an open dataset compiled to facilitate the coronavirus-related literature exploration, by providing various indicators of scientific impact for the relevant articles. Finally, we provide a publicly accessible Web interface on top of our data, allowing the exploration of the publications based on the computed indicators.

title: Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach
abstract: The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98%.

title: Using Reports of Own and Others' Symptoms and Diagnosis on Social Media to Predict COVID-19 Case Counts: Observational Infoveillance Study in Mainland China
abstract: Can public social media data be harnessed to predict COVID-19 case counts? We analyzed approximately 15 million COVID-19 related posts on Weibo, a popular Twitter-like social media platform in China, from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify"sick posts,"which are reports of one's own and other people's symptoms and diagnosis related to COVID-19. We then modeled the predictive power of sick posts and other COVID-19 posts on daily case counts. We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts, up to 14 days ahead of official statistics. But other COVID-19 posts did not have similar predictive power. For a subset of geotagged posts (3.10% of all retrieved posts), we found that the predictive pattern held true for both Hubei province and the rest of mainland China, regardless of unequal distribution of healthcare resources and outbreak timeline. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. On top of monitoring overall search and posting activities, it is crucial to sift through the contents and efficiently identify true signals from noise.

title: Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea
abstract: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations.  Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak.  Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources.  The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word “Coronavirus” communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers’ attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852).  Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

title: COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown
abstract: Italy has been severely affected by the COVID-19 pandemic, reporting the highest death toll in Europe as of April 2020. Following the identification of the first infections, on February 21, 2020, national authorities have put in place an increasing number of restrictions aimed at containing the outbreak and delaying the epidemic peak. On March 12, the government imposed a national lockdown. To aid the evaluation of the impact of interventions, we present daily time-series of three different aggregated mobility metrics: the origin-destination movements between Italian provinces, the radius of gyration, and the average degree of a spatial proximity network. All metrics were computed by processing a large-scale dataset of anonymously shared positions of about 170,000 de-identified smartphone users before and during the outbreak, at the sub-national scale. This dataset can help to monitor the impact of the lockdown on the epidemic trajectory and inform future public health decision making.

title: Clinical diagnosis of 8274 samples with 2019-novel coronavirus in Wuhan
abstract: 2019-Novel coronavirus (2019-nCoV) outbreaks create challenges for hospital laboratories because thousands of samples must be evaluated each day. Sample types, interpretation methods, and corresponding laboratory standards must be established. The possibility of other infections should be assessed to provide a basis for clinical classification, isolation, and treatment. Accordingly, in the present study, we evaluated the testing methods for 2019-nCoV and co-infections.  We used a fluorescence-based quantitative PCR kit urgently distributed by the Chinese CDC to detect 8274 close contacts in the Wuhan region against two loci on the 2019-nCoV genome. We also analyzed 613 patients with fever who underwent multiple tests for 13 respiratory pathogens; 316 subjects were also tested for 2019-nCoV.  Among the 8274 subjects, 2745 (33.2%) had 2019-nCoV infection; 5277 (63.8%) subjects showed negative results in the 2019-nCoV nucleic acid test (non-019-nCoV); and 252 cases (3.0%) because only one target was positive, the diagnosis was not definitive. Sixteen patients who originally had only one positive target were re-examined a few days later; 14 patients (87.5%) were finally defined as 2019-nCoV-positive, and 2 (12.5%) were finally defined as negative. The positive rates of nCoV-NP and nCovORF1ab were 34.7% and 34.7%, respectively. nCoV-NP-positive only and nCovORF1ab-positive cases accounted for 1.5% and 1.5%, respectively. In the 316 patients with multiple respiratory pathogens, 104 were positive for 2019-nCov and 6/104 had co-infection with coronavirus (3/104), influenza A virus (2/104), rhinovirus (2/104), and influenza A H3N2 (1/104); the remaining 212 patients had influenza A virus (11/202), influenza A H3N2 (11/202), rhinovirus (10/202), respiratory syncytial virus (7/202), influenza B virus (6/202), metapneumovirus (4/202), and coronavirus (2/202).  Clinical testing methods for 2019-nCoV require improvement. Importantly, 5.8% of 2019-nCoV infected and 18.4% of non-2019-nCoV-infected patients had other pathogen infections. It is important to treat combined infections and perform rapid screening to avoid cross-contamination of patients. A test that quickly and simultaneously screens as many pathogens as possible is needed.

title: Misinformation of COVID-19 on the Internet: Infodemiology Study
abstract: The internet has become an important source of health information for users worldwide. The novel coronavirus caused a pandemic search for information with broad dissemination of false or misleading health information.  The aim of this study was to evaluate the quality and readability of online information about the coronavirus disease (COVID-19), which was a trending topic on the internet, using validated instruments and relating the quality of information to its readability.  The search was based on the term “Wuhan Coronavirus” on the Google website (February 6, 2020). At the search time, the terms “COVID-19” or “SARS-CoV-2” (severe acute respiratory syndrome coronavirus 2) did not exist. Critical analysis was performed on the first 110 hits using the Health on the Net Foundation Code of Conduct (HONcode), the Journal of the American Medical Association (JAMA) benchmark, the DISCERN instrument, and Google ranking.  The first 110 websites were critically analyzed, and only 1.8% (n=2) of the websites had the HONcode seal. The JAMA benchmark showed that 39.1% (n=43) of the websites did not have any of the categories required by this tool, and only 10.0% (11/110) of the websites had the four quality criteria required by JAMA. The DISCERN score showed that 70.0% (n=77) of the websites were evaluated as having a low score and none were rated as having a high score.  Nonhealth personnel and the scientific community need to be aware about the quality of the information they read and produce, respectively. The Wuhan coronavirus health crisis misinformation was produced by the media, and the misinformation was obtained by users from the internet. The use of the internet has a risk to public health, and, in cases like this, the governments should be developing strategies to regulate health information on the internet without censuring the population. By February 6, 2020, no quality information was available on the internet about COVID-19.

title: No evidence of SARS-CoV-2 in semen of males recovering from COVID-19
abstract: To describe detection of SARS-CoV-2 in seminal fluid of patients recovering from COVID-19 and describe the expression profile of ACE2 and TMPRSS2 within the testicle. Design observational, cross-sectional study Setting Tertiary referral center Patients Thirty-four adult Chinese males diagnosed with COVID-19 through confirmatory quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) from pharyngeal swab samples Intervention None Main Outcome Measures Identification of SARS-CoV-2 on qRT-PCR of single ejaculated semen samples. Semen quality was not assessed. Expression patterns of ACE2 and TMPRSS2 in the human testis are explored through previously published single-cell transcriptome datasets.  Six patients (19%) demonstrated scrotal discomfort concerning for viral orchitis around the time of COVID-19 confirmation. SARS-CoV-2 was not detected in semen after a median of 31 days (IQR: 29-36 days) from COVID-19 diagnosis. Single-cell transcriptome analysis demonstrates sparse expression of ACE2 and TMPRSS2, with almost no overlapping gene expression.  SARS-CoV-2 was not detected in the semen of patients recovering from COVID-19 one month after COVID-19 diagnosis. ACE2-mediated viral entry of SARS-CoV-2 into target host cells is unlikely to occur within the human testicle based on ACE2 and TMPRSS2 expression. The long-term effects of SARS-CoV-2 on male reproductive function remain unknown.

title: Mapping the Immunodominance Landscape of SARS-CoV-2 Spike Protein for the Design of Vaccines against COVID-19
abstract: The ongoing coronavirus disease 2019 (COVID-19) pandemic is a serious threat to global public health, and imposes severe burdens on the entire human society. The severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) can cause severe respiratory illness and death. Currently, there are no specific antiviral drugs that can treat COVID-19. Several vaccines against SARS-CoV-2 are being actively developed by research groups around the world. The surface S (spike) protein and the highly expressed internal N (nucleocapsid) protein of SARS-CoV-2 are widely considered as promising candidates for vaccines. In order to guide the design of an effective vaccine, we need experimental data on these potential epitope candidates. In this study, we mapped the immunodominant (ID) sites of S protein using sera samples collected from recently discharged COVID-19 patients. The SARS-CoV-2 S protein-specific antibody levels in the sera of recovered COVID-19 patients were strongly correlated with the neutralising antibody titres. We used epitope mapping to determine the landscape of ID sites of S protein, which identified nine linearized B cell ID sites. Four out of the nine ID sites were found in the receptor-binding domain (RBD). Further analysis showed that these ID sites are potential high-affinity SARS-CoV-2 antibody binding sites. Peptides containing two out of the nine sites were tested as vaccine candidates against SARS-CoV-2 in a mouse model. We detected epitope-specific antibodies and SARS-CoV-2-neutralising activity in the immunised mice. This study for the first time provides human serological data for the design of vaccines against COVID-19.

title: Expression of SARS-CoV-2 receptor, ACE2, and TMPRSS2 in the lung airways is lower in children compared to adults and increases due to smoking and COPD
abstract: It has been reported that ACE2 and TMPRSS2 are the main cell entry proteins for SARS-CoV-2 and play a critical role in causing COVID-19. To investigate the expression level of these SARS-CoV-2 host cell entry genes in lung airway, public gene expression datasets were used. We have found a differential expression of ACE2 and TMPRSS2 in nasal and bronchial airways relative to age and diseases status. Children were found to have significantly lower expression of COVID-19 receptors in the upper and lower airways (nasal and bronchial). Moreover, the lung airway expression of both ACE2 and TMPRSS2 was found to be significantly upregulated in smokers compared to non-smokers; and in patients with COPD compared to healthy. No difference was observed in the blood expression levels of ACE2 and TMPRSS2 between children and adults, or in COPD or diabetic patients. However, a significant increase in blood expression levels of these genes was observed in patients with essential hypertension; while only ACE2 was upregulated in the blood of asthmatic. These results suggest that the observed difference in COVID-19 severity between children and adults could, in part, be attributed to the difference in ACE2 and TMPRSS2 airways tissue expression levels.

title: Why Are Lopinavir and Ritonavir Effective against the Newly Emerged Coronavirus 2019? Atomistic Insights into the Inhibitory Mechanisms
abstract: see text] Since the emergence of a novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported from Wuhan, China, neither a specific vaccine nor an antiviral drug against SARS-CoV-2 has become available. However, a combination of two HIV-1 protease inhibitors, lopinavir and ritonavir, has been found to be effective against SARS-CoV, and both drugs could bind well to the SARS-CoV 3C-like protease (SARS-CoV 3CL(pro)). In this work, molecular complexation between each inhibitor and SARS-CoV-2 3CL(pro) was studied using all-atom molecular dynamics simulations, free energy calculations, and pair interaction energy analyses based on MM/PB(GB)SA and FMO-MP2/PCM/6-31G* methods. Both anti-HIV drugs interacted well with the residues at the active site of SARS-CoV-2 3CL(pro). Ritonavir showed a somewhat higher number atomic contacts, a somewhat higher binding efficiency, and a somewhat higher number of key binding residues compared to lopinavir, which correspond with the slightly lower water accessibility at the 3CL(pro) active site. In addition, only ritonavir could interact with the oxyanion hole residues N142 and G143 via the formation of two hydrogen bonds. The interactions in terms of electrostatics, dispersion, and charge transfer played an important role in the drug binding. The obtained results demonstrated how repurposed anti-HIV drugs could be used to combat COVID-19.

title: Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
abstract: With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic is ranked second in the list of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. Thus, here we design, annotate, and release to the research community a new dataset for fine-grained disinformation analysis that (i)focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society as a whole, and (iii) covers both English and Arabic. Finally, we show strong evaluation results using state-of-the-art Transformers, thus confirming the practical utility of the annotation schema and of the dataset.

title: Identification of Repurposal Drugs and Adverse Drug Reactions for Various Courses of Coronavirus Disease 2019 (COVID-19) Based on Single-cell RNA Sequencing Data
abstract: With more than 3.8 million people infected Coronavirus Disease 2019 (COVID-19), caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a critical threat to human health. There is no proven vaccine or specific drug to date, which highlights the urgent need for rapid development of therapeutics for COVID-19. To identify potentially repurposable drugs, we employed a systematic approach to mine candidates from U.S. FDA approved drugs and pre-clinical small-molecule compounds by integrating the gene expression perturbation data by chemicals from the Library of Integrated Network-Based Cellular Signatures (LINCS) project with publically available single-cell RNA sequencing dataset from mild and severe COVID-19 patients. We identified 281 FDA approved drugs that have the potential to be effective against SARS-CoV-2 infection, 10 of which are currently undergoing clinical trials to evaluate their efficacy against COVID-19. In conclusion, we have identified a list of repurposable anti-SARS- CoV-2 drugs using a systems biology approach.

title: Visible Insights of the Invisible Pandemic: A Scientometric, Altmetric and Topic Trend Analysis
abstract: The recent SARS-COV-2 virus outbreak has created an unprecedented global health crisis! The disease is showing alarming trends with the number of people getting infected with this disease, new cases and death rate are all highlighting the need to control this disease at the earliest. The strategy now for the governments around the globe is how to limit the spread of the virus until the research community develops treatment/drug or vaccination against the virus. The outbreak of this disease has unsurprisingly led to huge volume of research within a short period of time surrounding this disease. It has also led to aggressive social media activity on twitter, Facebook, dedicated blogs, news reports and other online sites actively involved in discussing about the various aspects of and related to this disease. It becomes a useful and challenging exercise to draw from this huge volume of research, the key papers that form the research front, its influence in the research community, and other important research insights. Similarly, it becomes important to discern the key issues that influence the society concerning this disease. The paper is motivated by this. It attempts to distinguish which are the most influential papers, the key knowledge base and major topics surrounding the research covered by COVID-19. Further it attempts to capture the society's perception by discerning key topics that are trending online. The study concludes by highlighting the implications of this study.

title: Traces of SARS-CoV-2 RNA in the Blood of COVID-19 Patients
abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third virus that caused coronavirus-related outbreaks over the past 20 years. The outbreak was first reported in December 2019 from Wuhan, China, but rapidly progressed into a pandemic of an unprecedented scale since the 1918 flu pandemic. In addition to respiratory complications in COVID-19 patients, clinical characterizations of severe infection cases indicated a number of other comorbidities, including multiple organ failure (liver, kidney, and heart) and septic shock. In an attempt to elucidate COVID-19 pathogenesis in different human organs, we interrogated the presence of the virus in the blood, or any of its components, which might provide a form of trafficking or hiding to the virus. By computationally analyzing high-throughput sequence data from patients with active COVID-19 infection, we found evidence of traces of SARS-CoV-2 RNA in peripheral blood mononuclear cells (PBMC), while the virus RNA was abundant in bronchoalveolar lavage specimens from the same patients. To the best of our knowledge, the presence of SAR-CoV-2 RNA in the PBMC of COVID-19 patients has not been reported before, and this observation could suggest immune presentation, but discounts the possibility of extensive viral infection of lymphocytes or monocytes.

title: Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies
abstract: The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally-determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the available SARS-CoV-2 sequences (as of 9 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.

title: The uses of disorder in negotiated information orders: information leveraging and changing norms in global public health governance
abstract: The SARS epidemic that broke out in late 2002 in China’s Guangdong Province highlighted the difficulties of reliance on state‐provided information when states have incentives to conceal discrediting information about public health threats. Using SARS and the International Health Regulations (IHR) as a starting point, this article examines negotiated information orders in global public health governance and the irregularities in the supply of data that underlie them. Negotiated information orders within and among the organizations in a field (here, e.g., the World Health Organization, member states, government agencies, and international non‐governmental organizations) spell out relationships among different categories of knowledge and non‐knowledge – what is known, acknowledged to be known, and available for use in decision making versus what might be known but cannot be acknowledged or officially used. Through information leveraging, technically sufficient information then becomes socially sufficient information. Thus it is especially information initially categorized as non‐knowledge – including suppressed data, rumour, unverified evidence, and unofficial information – that creates pressure for the renegotiation of information orders. The argument and evidence of the article also address broader issues about how international law and global norms are realigned, how global norms change, and how social groups manage risk.

title: Where to buy face masks? Survey of applications using Taiwan’s open data in the time of coronavirus disease 2019
abstract: The coronavirus disease 2019 (COVID-19) had spread rapidly since late December 2019. Personal protective equipment was essential to prevent transmission. Owing to shortage of face masks, Taiwan government began to implement quasi rationing on February 6, 2020, by allowing each resident to purchase two masks in seven days. Taiwan National Health Insurance Administration offered online data with real-time updates on face mask availability in all contracted pharmacies and selected local health centers. Based on the open data, numerous software applications quickly emerged to assist the public in finding sales locations efficiently.  Up until March 15, 2020, the Public Digital Innovation Space of Taiwan government had recorded 134 software applications of face mask availability, and 24 software applications were excluded due to defect, duplicate, and unavailability. These applications were analyzed according to platform, developer type, and display mode.  Of the 110 valid software applications, 67 (60.9%) applications were deployed on websites, followed by 21 (19.1%) on social networking sites, 19 (17.3%) as mobile applications, and 3 (2.7%) in other modes. Nearly two thirds (n = 70) of applications were developed by individuals, one third (n = 37) by commercial companies, only two applications by central and local governments, and one by a nongovernmental organization. With respect to the display mode, 47 (42.7%) applications adopted map-view only, 41 (37.3%) adopted table-view only, and 19 (17.3%) adopted both modes. Of the remaining three applications, two offered voice user interfaces and one used augmented reality.  Taiwan’s open data strategy facilitated rapid development of software applications for information dissemination to the public during the COVID-19 crisis. The transparency of real-time data could help alleviate the panic of the public. The collaborative contributions from the grassroots in disasters were priceless treasures.

title: Angiotensin-Converting Enzyme 2 and Antihypertensives (Angiotensin Receptor Blockers and Angiotensin-Converting Enzyme Inhibitors) in Coronavirus Disease 2019
abstract: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, is being defined as the worst pandemic disease of modern times. Several professional health organizations have published position papers stating that there is no evidence to change the use of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) in the management of elevated blood pressure in the context of avoiding or treating COVID-19 infection. In this article, we review the evidence on the relationship between the renin-angiotensin-aldosterone system and COVID-19 infection. In agreement with current guidelines, patients with hypertension should continue taking antihypertensive medications as prescribed without interruption. Because ACEIs and ARBs are also used to retard the progression of chronic kidney disease, we suggest that these recommendations also apply to the use of these agents in chronic kidney disease. No differences generally exist between ARBs and ACEIs in terms of efficacy in decreasing blood pressure and improving other outcomes, such as all-cause mortality, cardiovascular mortality, myocardial infarction, heart failure, stroke, and end-stage renal disease. The ACEIs are associated with cough secondary to accumulation of bradykinin and angioedema, and withdrawal rates due to adverse events are lower with ARBs. Given their equal efficacy but fewer adverse events, ARBs could potentially be a more favorable treatment option in patients with COVID-19 at higher risk for severe forms of disease.

title: OUTBREAK: A user-friendly georeferencing online tool for disease surveillance
abstract: The current COVID-19 pandemic has already claimed more than 100,000 victims and it will cause more deaths in the coming months. Tools that can track the number and locations of cases are critical for surveillance and can help in making policy decisions for controlling the outbreak. The current surveillance web-based dashboards run on proprietary platforms, which are often expensive and require specific computational knowledge. We present a new tool (OUTBREAK) for studying and visualizing epidemiological data. It permits even non-specialist users to input data most conveniently and track outbreaks in real-time. This tool has the potential to guide and help health authorities to intervene and minimize the effects of the outbreaks. It is freely available at http://outbreak.sysbio.tools/.

title: We have to write and share valid and reliable information on COVID-19.

title: NLPExplorer: Exploring the Universe of NLP Papers
abstract: Understanding the current research trends, problems, and their innovative solutions remains a bottleneck due to the ever-increasing volume of scientific articles. In this paper, we propose NLPExplorer, a completely automatic portal for indexing, searching, and visualizing Natural Language Processing (NLP) research volume. NLPExplorer presents interesting insights from papers, authors, venues, and topics. In contrast to previous topic modelling based approaches, we manually curate five course-grained non-exclusive topical categories namely Linguistic Target (Syntax, Discourse, etc.), Tasks (Tagging, Summarization, etc.), Approaches (unsupervised, supervised, etc.), Languages (English, Chinese, etc.) and Dataset types (news, clinical notes, etc.). Some of the novel features include a list of young popular authors, popular URLs and datasets, list of topically diverse papers and recent popular papers. Also, it provides temporal statistics such as yearwise popularity of topics, datasets, and seminal papers. To facilitate future research and system development, we make all the processed dataset accessible through API calls. The current system is available at http://nlpexplorer.org.

title: COVID-19 in Italy: Dataset of the Italian Civil Protection Department
abstract: The database here described contains data of integrated surveillance for the “Coronavirus disease 2019” (abbreviated as COVID-19 by the World Health Organization) in Italy, caused by the novel coronavirus SARS-CoV-2. The database, included in a main folder called COVID-19, has been designed and created by the Italian Civil Protection Department, which currently manages it. The database consists of six folders called ‘aree’ (containing charts of geographical areas interested by containment measures), ‘dati-andamento-nazionale’ (containing data relating to the national trend of SARS-CoV-2 spread), ‘dati-json’ (containing data that summarize the national, provincial and regional trends of SARS-CoV-2 spread), ‘dati-province’ (containing data relating to the provincial trend of SARS-CoV-2 spread), ‘dati-regioni’ (containing data relating to the regional trend of SARS-CoV-2 spread) and ‘schede-riepilogative’ (containing summary sheets relating to the provincial and regional trends of SARS-CoV-2 spread). The Italian Civil Protection Department daily receives data by the Italian Ministry of Health, analyzes them and updates the database. Thus, the database is subject to daily updates and integrations. The database is freely accessible (CC-BY-4.0 license) at https://github.com/pcm-dpc/COVID-19. This database is useful to provide insight on the spread mechanism of SARS-CoV-2, to support organizations in the evaluation of the efficiency of current prevention and control measures, and to support governments in the future prevention decisions.

title: A pharmacological interactome between COVID-19 patient samples and human sensory neurons reveals potential drivers of neurogenic pulmonary dysfunction
abstract: The SARS-CoV-2 virus infects cells of the airway and lungs in humans causing the disease COVID-19. This disease is characterized by cough, shortness of breath, and in severe cases causes pneumonia and acute respiratory distress syndrome (ARDS) which can be fatal. Bronchial alveolar lavage fluid (BALF) and plasma from mild and severe cases of COVID-19 have been profiled using protein measurements and bulk and single cell RNA sequencing. Onset of pneumonia and ARDS can be rapid in COVID-19, suggesting a potential neuronal involvement in pathology and mortality. We hypothesized that SARS-CoV-2 infection drives changes in immune cell-derived factors that then interact with receptors expressed by the sensory neuronal innervation of the lung to further promote important aspects of disease severity, including ARDS. We sought to quantify how immune cells might interact with sensory innervation of the lung in COVID-19 using published data from patients, existing RNA sequencing datasets from human dorsal root ganglion neurons and other sources, and a genome-wide ligand-receptor pair database curated for pharmacological interactions relevant for neuro-immune interactions. Our findings reveal a landscape of ligand-receptor interactions in the lung caused by SARS-CoV-2 viral infection and point to potential interventions to reduce the burden of neurogenic inflammation in COVID-19 pulmonary disease. In particular, our work highlights opportunities for clinical trials with existing or under development rheumatoid arthritis and other (e.g. CCL2, CCR5 or EGFR inhibitors) drugs to treat high risk or severe COVID-19 cases.

title: Redundancy and methodological issues in articles on COVID-19

title: Feasibility Study of Mitigation and Suppression Intervention Strategies for Controlling COVID-19 Outbreaks in London and Wuhan
abstract: Recent outbreaks of coronavirus disease 2019 (COVID-19) has led a global pandemic cross the world. Most countries took two main interventions: suppression like immediate lockdown cities at epicentre or mitigation that slows down but not stopping epidemic for reducing peak healthcare demand. Both strategies have their apparent merits and limitations; it becomes extremely hard to conduct one intervention as the most feasible way to all countries. Targeting at this problem, this paper conducted a feasibility study by defining a mathematical model named SEMCR that can access effectiveness of mitigation, suppression and hybrid interventions for controlling COVID-19 outbreaks in London and Wuhan. It extended traditional SEIR (Susceptible-Exposed Infectious-Recovered) model by adding two key features: a direct connection between Exposed and Recovered populations, and separating infections into mild and critical cases. It defined parameters to classify two stages of COVID-19 control: active contain by isolation of cases and contacts, passive contain by suppression or mitigation. The model was fitted and evaluated with public dataset containing daily number of confirmed active cases including Wuhan and London during January, 2020 and March 2020. The simulated results showed that 1) Immediate suppression taken in Wuhan significantly reduced the total exposed and infectious populations to 119610, but it has to be consistently maintained at least 90 days (by the middle of April 2020). Its success heavily relied on sufficiently external support from other places of China. This mode were not suitable to other countries that have no sufficient health resources. 2) In London, it is possible to take a hybrid intervention of suppression and mitigation for every 2 or 3 weeks over a longer period to balance the total infections and economic loss. While the total infectious populations in this scenario would be possibly 2 times than the one taking suppression, economic loss and recovery of London would be less affected. 3) Both in Wuhan and London cases, one important issue of fitting practical data was that there were a large portion (probably 62.9% in Wuhan) of self-recovered populations that were asymptomatic or mild symptomatic. These people might think they have been healthy at home and did not go to hospital for COVID-19 tests. Early release of intervention intensity potentially increased a risk of the second outbreak. One limitation of our model was that our prediction of infections and deaths depended on a parameter estimation of intervention intensity that presented by average number contacts with susceptible individuals as infectious individuals in a certain region. It assumed that each intervention had equivalent effects on the reproduction number R in different regions over time. Practical effectiveness of implementing intervention intensity might be varied with respect to cultures or other issues of certain county.

title: Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned
abstract: We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. This web application exists as part of a suite of tools that we have developed over the past few weeks to help domain experts tackle the ongoing global pandemic. We hope that improved information access capabilities to the scientific literature can inform evidence-based decision making and insight generation. This paper describes our initial efforts and offers a few thoughts about lessons we have learned along the way.

title: Political Intrusions into the International Health Regulations Treaty and Its Impact on Management of Rapidly Emerging Zoonotic Pandemics: What History Tells Us
abstract: For a large number of health care providers world-wide, the coronavirus disease 2019 (COVID-19) pandemic is their first experience in population-based care. In past decades, lower population densities, infectious disease outbreaks, epidemics, and pandemics were rare and driven almost exclusively by natural disasters, predatory animals, and war. In the early 1900s, Sir William Osler first advanced the knowledge of zoonotic diseases that are spread from reservoir animals to human animals. Once rare, they now make up 71% or more of new diseases. Globally, zoonotic spread occurs for many reasons. Because the human population has grown in numbers and density, the spread of these diseases accelerated though rapid unsustainable urbanization, biodiversity loss, and climate change. Furthermore, they are exacerbated by an increasing number of vulnerable populations suffering from chronic deficiencies in food, water, and energy. The World Health Organization (WHO) and its International Health Regulation (IHR) Treaty, organized to manage population-based diseases such as Influenza, severe acute respiratory syndrome (SARS), H1N1, Middle East respiratory syndrome (MERS), HIV, and Ebola, have failed to meet population-based expectations. In part, this is due to influence from powerful political donors, which has become most evident in the current COVID-19 pandemic. The global community can no longer tolerate an ineffectual and passive international response system, nor tolerate the self-serving political interference that authoritarian regimes and others have exercised over the WHO. In a highly integrated globalized world, both the WHO with its IHR Treaty have the potential to become one of the most effective mechanisms for crisis response and risk reduction world-wide. Practitioners and health decision-makers must break their silence and advocate for a stronger treaty, a return of the WHO’s singular global authority, and support highly coordinated population-based management. As Osler recognized, his concept of “one medicine, one health” defines what global public health is today.

title: Canine coronavirus, Greece. Molecular analysis and genetic diversity characterization
abstract: Canine coronavirus (CCoV) is an etiologic agent of diarrhea in dogs and is known to have spread worldwide. Mild disease or asymptomatic carriage are probably in many cases common outcomes of infection. To date, two different genotypes of CCoV are known, CCoV type I (CCoV-I) and CCoV type II (CCoV-II). CCoV type II is divided in two subtypes, CCoV-IIa (classical strains) and CCoV-IIb, with CCoV-IIb emerging as a result of a putative recombination between CCoV-IIa and transmissible gastroenteritis virus (TGEV). The aim of the present study was to investigate the presence of CCoV in Greece and to genetically analyze the circulating strains. Between December 2007 and December 2009, 206 fecal samples were collected from dogs with diarrhea from kennels, pet shops and veterinary clinics of different country regions. RT-PCR and real time RT-PCR assays were used for CCoV detection and characterization. CCoV was identified in 65.1% of the dogs presenting diarrhea, being more frequently detected in animals younger than 3months old and in animals housed in groups. In 47% of the positive samples more than one CCoV genotype/subtype were detected, with triple CCoV-I/CCoV-IIa/CCoV-IIb infections being identified for the first time. Molecular and phylogenetic analysis revealed that CCoV-I Greek strains share low genetic relatedness to each other and to the prototype CCoV-I strains in the 5’ end of the S gene. Moreover, a divergent CCoV-IIa strain was identified. The circulation of highly variable CCoV-I and CCoV-IIb emerging strains, as well as the detection of the divergent strain, raise concerns on the importance of these new strains as primary pathogens of diarrhoeic syndromes diagnosed in dogs.

title: Clinical and historical features associated with severe COVID-19 infection: a systematic review
abstract: There is an urgent need for rapid assessment methods to guide pathways of care for COVID-19 patients, as frontline providers need to make challenging decisions surrounding rationing of resources. This study aimed to evaluate existing literature for factors associated with COVID-19 illness severity.  A systematic review identified all studies published between 1/12/19 and 19/4/20 that used primary data and inferential statistics to assess associations between the outcome of interest - disease severity - and historical or clinical variables. PubMed, Scopus, Web of Science, and the WHO Database of Publications on Coronavirus Disease were searched. Data were independently extracted and cross-checked independently by two reviewers using PRISMA guidelines, after which they were descriptively analysed. Quality and risk of bias in available evidence were assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework.   Of the 6202 relevant articles found, 63 were eligible for inclusion; these studies analysed data from 17648 COVID-19 patients. The majority (n=57, 90.5%) were from China and nearly all (n=51, 90.5%) focussed on admitted adult patients. Patients had a median age of 52.5 years and 52.8% were male. The predictors most frequently associated with COVID-19 disease severity were age, absolute lymphocyte count, hypertension, lactate dehydrogenase (LDH), C-reactive protein (CRP), and history of any pre-existing medical condition.  This study identified multiple variables likely to be predictive of severe COVID-19 illness. Due to the novelty of SARS-CoV-2 infection, there is currently no severity prediction tool designed to, or validated for, COVID-19 illness severity. Findings may inform such a tool that can offer guidance on clinical treatment and disposition, and ultimately reduce morbidity and mortality due to the pandemic.

title: Genomic diversity of SARS-CoV-2 in Coronavirus Disease 2019 patients
abstract: A novel coronavirus (SARS-CoV-2) has infected more than 75,000 individuals and spread to over 20 countries. It is still unclear how fast the virus evolved and how the virus interacts with other microorganisms in the lung.  We have conducted metatranscriptome sequencing for the bronchoalveolar lavage fluid of eight SARS-CoV-2 patients, 25 community-acquired pneumonia (CAP) patients, and 20 healthy controls.  The median number of intra-host variants was 1-4 in SARS-CoV-2 infected patients, which ranged between 0 and 51 in different samples. The distribution of variants on genes was similar to those observed in the population data (110 sequences). However, very few intra-host variants were observed in the population as polymorphism, implying either a bottleneck or purifying selection involved in the transmission of the virus, or a consequence of the limited diversity represented in the current polymorphism data. Although current evidence did not support the transmission of intra-host variants in a person-to-person spread, the risk should not be overlooked. The microbiota in SARS-CoV-2 infected patients was similar to those in CAP, either dominated by the pathogens or with elevated levels of oral and upper respiratory commensal bacteria.  SARS-CoV-2 evolves in vivo after infection, which may affect its virulence, infectivity, and transmissibility. Although how the intra-host variant spreads in the population is still elusive, it is necessary to strengthen the surveillance of the viral evolution in the population and associated clinical changes.

title: Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes
abstract: Genome detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next-generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present the Genome Detective Coronavirus Typing Tool that can accurately identify the novel severe acute respiratory syndrome (SARS)-related coronavirus (SARS-CoV-2) sequences isolated in China and around the world. The tool can accept up to 2000 sequences per submission and the analysis of a new whole-genome sequence will take approximately 1 min. The tool has been tested and validated with hundreds of whole genomes from 10 coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for SARS-CoV-2. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines to stop the COVID-19 disease.  koen@emweb.be or deoliveira@ukzn.ac.za.  Supplementary data are available at Bioinformatics online.

title: Atlas of ACE2 gene expression in mammals reveals novel insights in transmisson of SARS-Cov-2
abstract: Background COVID-19 has become a worldwide pandemic. It is caused by a novel coronavirus named SARS-CoV-2 with elusive origin. SARS-CoV-2 infects mammalian cells by binding to ACE2, a transmembrane protein. Therefore, the conservation of ACE2 and its expression pattern across mammalian species, which are yet to be comprehensively investigated, may provide valuable insights into tracing potential hosts of SARS-CoV-2.  We analyzed gene conservation of ACE2 across mammals and collected more than 140 transcriptome datasets from human and common mammalian species, including presumed hosts of SARS-CoV-2 and other animals in close contact with humans. In order to enable comparisons across species and tissues, we used a unified pipeline to quantify and normalize ACE2 expression levels.  We first found high conservation of ACE2 genes among common mammals at both DNA and peptide levels, suggesting that a broad range of mammalian species can potentially be the hosts of SARS-CoV-2. Next, we showed that high level of ACE2 expression in certain human tissues is consistent with clinical symptoms of COVID-19 patients. Furthermore, we observed that ACE2 expressed in a species-specific manner in the mammals examined. Notably, high expression in skin and eyes in cat and dog suggested that these animals may play roles in transmitting SARS-CoV-2 to humans.  Through building the first atlas of ACE2 expression in pets and livestock, we identified species and tissues susceptible to SARS-CoV-2 infection, yielding novel insights into the viral transmission.

title: Covid-19: Pandemonium in our time.
abstract: While pandemonium has come to mean wild and noisy disorder, the reference here is to John Milton's epic poem Paradise Lost and the upheaval following Lucifer's banishment from Heaven and his construction of Pandæmonium as his hub. Today's avalanche of conflicting news on how to deal with the coronavirus disease 2019 (Covid-19) brings to mind the Trinity nuclear bomb test with Enrico Fermi estimating its strength by releasing small pieces of paper into the air and measuring their displacement by the shock wave. Fermi's result, in fact not far from the true value, emphasised his ability to make good approximations with few or no actual data. The current wave of Covid-19 presents just this kind of situation as it engulfs the world from ground zero in Wuhan, China. Much information is indeed missing, but datasets that might lead to useful ideas on how to handle this pandemic are steadily accumulating.

title: COVID-19 and pets: When pandemic meets panic
abstract: As the novel coronavirus outbreak spreads globally with devastating effects on human health, pets are also becoming unnecessary victims amidst the pandemic panic. Many have been reluctantly left home alone by owners who have been forced to temporarily evacuate their homes. And, although no evidence exists to indicate that they can either transmit the virus or develop its associated coronavirus disease 2019 (COVID-19), fear among the public that pets might play a role in spreading COVID-19 has resulted in pets being abandoned or even killed. This article outlines some of the ways in which the current pandemic has negatively impacted the welfare of pets. It also highlights the relationships between animal, human, and environmental health, as well as the importance of taking a collaborative transdisciplinary One Health approach to help prevent future COVID-19 outbreaks.

title: Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review
abstract: Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called ‘COVID-19’, as a ‘public health emergency of international concern’. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

title: The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application
abstract: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 , which has important implications for surveillance and control activities.  To estimate the length of the incubation period of COVID-19 and describe its public health implications.  Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020.  News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China.  Persons with confirmed SARS-CoV-2 infection outside Hubei province, China.  Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization.  There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.  Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases.  This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases.  U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

title: Detecting fake news for the new coronavirus by reasoning on the Covid-19 ontology
abstract: In the context of the Covid-19 pandemic, many were quick to spread deceptive information. I investigate here how reasoning in Description Logics (DLs) can detect inconsistencies between trusted medical sources and not trusted ones. The not-trusted information comes in natural language (e.g."Covid-19 affects only the elderly"). To automatically convert into DLs, I used the FRED converter. Reasoning in Description Logics is then performed with the Racer tool.

title: Coronavirus Diseases (COVID-19) Current Status and Future Perspectives: A Narrative Review
abstract: At the end of 2019 a novel virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing severe acute respiratory syndrome expanded globally from Wuhan, China. In March 2020 the World Health Organization declared the SARS-Cov-2 virus a global pandemic. We performed a narrative review to describe existing literature with regard to Corona Virus Disease 2019 (COVID-19) epidemiology, pathophysiology, diagnosis, management and future perspective. MEDLINE, EMBASE and Scopus databases were searched for relevant articles. Although only when the pandemic ends it will be possible to assess the full health, social and economic impact of this global disaster, this review represents a picture of the current state of the art. In particular, we focus on public health impact, pathophysiology and clinical manifestations, diagnosis, case management, emergency response and preparedness.

title: The COronavirus Pandemic Epidemiology (COPE) Consortium: A Call to Action
abstract: The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) pandemic presents challenges to the real-time collection of population-scale data to inform near-term public health needs as well as future investigations. We established the COronavirus Pandemic Epidemiology (COPE) consortium to address this unprecedented crisis on behalf of the epidemiology research community. As a central component of this initiative, we have developed a COVID Symptom Study (previously known as the COVID Symptom Tracker) mobile application as a common data collection tool for epidemiologic cohort studies with active study participants. This mobile application collects information on risk factors, daily symptoms, and outcomes through a user-friendly interface that minimizes participant burden. Combined with our efforts within the general population, data collected from nearly 3 million participants in the United States and United Kingdom are being used to address critical needs in the emergency response, including identifying potential hot spots of disease and clinically actionable risk factors. The linkage of symptom data collected in the app with information and biospecimens already collected in epidemiology cohorts will position us to address key questions related to diet, lifestyle, environmental, and socioeconomic factors on susceptibility to COVID-19, clinical outcomes related to infection, and long-term physical, mental health, and financial sequalae. We call upon additional epidemiology cohorts to join this collective effort to strengthen our impact on the current health crisis and generate a new model for a collaborative and nimble research infrastructure that will lead to more rapid translation of our work for the betterment of public health.

title: On the responsible use of digital data to tackle the COVID-19 pandemic
abstract: Large-scale collection of data could help curb the COVID-19 pandemic, but it should not neglect privacy and public trust. Best practices should be identified to maintain responsible data-collection and data-processing standards at a global scale.

title: SARS-CoV-2 Entry Genes Are Most Highly Expressed in Nasal Goblet and Ciliated Cells within Human Airways
abstract: The SARS-CoV-2 coronavirus, the etiologic agent responsible for COVID-19 coronavirus disease, is a global threat. To better understand viral tropism, we assessed the RNA expression of the coronavirus receptor, ACE2, as well as the viral S protein priming protease TMPRSS2 thought to govern viral entry in single-cell RNA-sequencing (scRNA-seq) datasets from healthy individuals generated by the Human Cell Atlas consortium. We found that ACE2, as well as the protease TMPRSS2, are differentially expressed in respiratory and gut epithelial cells. In-depth analysis of epithelial cells in the respiratory tree reveals that nasal epithelial cells, specifically goblet/secretory cells and ciliated cells, display the highest ACE2 expression of all the epithelial cells analyzed. The skewed expression of viral receptors/entry-associated proteins towards the upper airway may be correlated with enhanced transmissivity. Finally, we showed that many of the top genes associated with ACE2 airway epithelial expression are innate immune-associated, antiviral genes, highly enriched in the nasal epithelial cells. This association with immune pathways might have clinical implications for the course of infection and viral pathology, and highlights the specific significance of nasal epithelia in viral infection. Our findings underscore the importance of the availability of the Human Cell Atlas as a reference dataset. In this instance, analysis of the compendium of data points to a particularly relevant role for nasal goblet and ciliated cells as early viral targets and potential reservoirs of SARS-CoV-2 infection. This, in turn, serves as a biological framework for dissecting viral transmission and developing clinical strategies for prevention and therapy.

title: Advertisers Jump on Coronavirus Bandwagon: Politics, News, and Business
abstract: In the age of social media, disasters and epidemics usher not only a devastation and affliction in the physical world, but also prompt a deluge of information, opinions, prognoses and advice to billions of internet users. The coronavirus epidemic of 2019-2020, or COVID-19, is no exception, with the World Health Organization warning of a possible"infodemic"of fake news. In this study, we examine the alternative narratives around the coronavirus outbreak through advertisements promoted on Facebook, the largest social media platform in the US. Using the new Facebook Ads Library, we discover advertisers from public health and non-profit sectors, alongside those from news media, politics, and business, incorporating coronavirus into their messaging and agenda. We find the virus used in political attacks, donation solicitations, business promotion, stock market advice, and animal rights campaigning. Among these, we find several instances of possible misinformation, ranging from bioweapons conspiracy theories to unverifiable claims by politicians. As we make the dataset available to the community, we hope the advertising domain will become an important part of quality control for public health communication and public discourse in general.

title: A Semantically Enriched Dataset based on Biomedical NER for the COVID19 Open Research Dataset Challenge
abstract: Research into COVID-19 is a big challenge and highly relevant at the moment. New tools are required to assist medical experts in their research with relevant and valuable information. The COVID-19 Open Research Dataset Challenge (CORD-19) is a"call to action"for computer scientists to develop these innovative tools. Many of these applications are empowered by entity information, i. e. knowing which entities are used within a sentence. For this paper, we have developed a pipeline upon the latest Named Entity Recognition tools for Chemicals, Diseases, Genes and Species. We apply our pipeline to the COVID-19 research challenge and share the resulting entity mentions with the community.

title: SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation
abstract: The global pandemic of COVID-19 has infected millions of people since its first outbreak in last December. A key challenge for preventing and controlling COVID-19 is how to quickly, widely, and effectively implement the test for the disease, because testing is the first step to break the chains of transmission. To assist the diagnosis of the disease, radiology imaging is used to complement the screening process and triage patients into different risk levels. Deep learning methods have taken a more active role in automatically detecting COVID-19 disease in chest x-ray images, as witnessed in many recent works. Most of these works first train a CNN on an existing large-scale chest x-ray image dataset and then fine-tune it with a COVID-19 dataset at a much smaller scale. However, direct transfer across datasets from different domains may lead to poor performance due to visual domain shift. Also, the small scale of the COVID-19 dataset on the target domain can make the training fall into the overfitting trap. To solve all these crucial problems and fully exploit the available large-scale chest x-ray image dataset, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting, through which we are motivated to reduce the domain shift and avoid overfitting when training on a very small dataset of COVID-19. In addressing this formulated problem, we propose a novel Semi-supervised Open set Domain Adversarial network (SODA), which is able to align the data distributions across different domains in a general domain space and also in a common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models, as well as effectively separating COVID-19 with common pneumonia.

title: A highly conserved WDYPKCDRA epitope in the RNA directed RNA polymerase of human coronaviruses can be used as epitope-based universal vaccine design
abstract: Coronaviruses are the diverse group of RNA virus. From 1960, six strains of human coronaviruses have emerged that includes SARS-CoV and the recent infection by deadly MERS-CoV which is now going to cause another outbreak. Prevention of these viruses is urgent and a universal vaccine for all strain could be a promising solution in this circumstance. In this study we aimed to design an epitope based vaccine against all strain of human coronavirus.  Multiple sequence alignment (MSA) approach was employed among spike (S), membrane (M), enveloped (E) and nucleocapsid (N) protein and replicase polyprotein 1ab to identify which one is highly conserve in all coronaviruses strains. Next, we use various in silico tools to predict consensus immunogenic and conserved peptide. We found that conserved region is present only in the RNA directed RNA polymerase protein. In this protein we identified one epitope WDYPKCDRA is highly immunogenic and 100% conserved among all available human coronavirus strains.  Here we suggest in vivo study of our identified novel peptide antigen in RNA directed RNA polymerase protein for universal vaccine – which may be the way to prevent all human coronavirus disease.

title: Middle East Respiratory Syndrome Coronavirus, Saudi Arabia, 2017–2018
abstract: We characterized exposures and demographics of Middle East respiratory syndrome coronavirus cases reported to the Saudi Arabia Ministry of Health during July 1–October 31, 2017, and June 1–September 16, 2018. Molecular characterization of available specimens showed that circulating viruses during these periods continued to cluster within lineage 5.

title: Low-density lipoprotein is a potential predictor of poor prognosis in patients with coronavirus disease 2019
abstract: The pandemic of coronavirus disease 2019 (COVID-19) has become a global threat to public health. The lipid pathophysiology in COVID-19 is unknown.  In this retrospective longitudinal study, we monitored the serum lipids in 17 surviving and 4 non-surviving COVID-19 cases prior to their viral infections and duration the entire disease courses.  In surviving cases, the low-density lipoprotein (LDL) levels decreased significantly on admission as compared with the levels before infection; the LDL levels remained constantly low during the disease progression and resumed to the original levels when patients recovered (pre-infection: 3.5 (3.0–4.4); on admission: 2.8 (2.3–3.1), p < .01; progression: 2.5 (2.3–3.0); discharge: 3.6 (2.7–4.1); median (IQR), in mmol/L). In non-surviving patients, LDL levels showed an irreversible and continuous decrease until death (1.1 (0.9–1.2), p = .02 versus the levels on admission). The ratio changes of LDL levels inversely correlated with ratio changes of high-sensitivity C-reactive protein levels. Logistic regression analysis showed increasing odds of lowered LDL levels associated with disease progression (odds ratio: 4.48, 95% IC: 1.55–12.92, p = .006) and in-hospital death (odds ratio: 21.72, 95% IC: 1.40–337.54, p = .028).  LDL levels inversely correlated to disease severities, which could be a predictor for disease progress and poor prognosis.

title: Nano Research for COVID-19

title: Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea
abstract: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations.  Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak.  Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources.  The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word "Coronavirus" communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers' attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852).  Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

title: Hong Kong Protests: Using Natural Language Processing for Fake News Detection on Twitter
abstract: The automation of fake news detection is the focus of a great deal of scientific research. With the rise of social media over the years, there has been a strong preference for users to be informed using their social media account, leading to a proliferation of fake news through them. This paper evaluates the veracity of politically-oriented news and in particular the tweets about the recent event of Hong Kong protests, with the aid of a dataset recently published by Twitter. From this dataset, Chinese tweets are translated into English, which are kept along with originally English tweets. By utilizing a language-independent filtering process, relevant tweets are identified. To complete the dataset, tweets originating from valid sources are used as the real portion, with journalists rather than news agencies being considered, which constitutes a novel aspect of the methodology. Well-known Machine Learning algorithms are used to classify tweets, which are represented by a feature value vector that is extracted, selected and preprocessed from the datasets and mainly revolves around language use, with word entropy being a novel feature. The results derived from these algorithms highlight morphological, lexical and vocabulary differences between tweets spreading fake and real news, which are for the most part in accordance with past related work.

title: How best to use limited tests? Improving COVID-19 surveillance in long-term care
abstract: Long-term care facilities (LTCFs) are particularly vulnerable to nosocomial outbreaks of coronavirus disease 2019 (COVID-19), with high rates of transmission and mortality. Timely epidemiological surveillance is essential to detect and respond to outbreaks, but testing resources are highly limited in the current pandemic context.  We used an individual-based transmission model to simulate COVID-19 spread along inter-individual contact networks in the LTCF setting. A range of surveillance strategies were evaluated for their ability to detect simulated outbreaks, assuming limited availability of standard RT-PCR tests. Various epidemiological scenarios were considered, including COVID-19 importation from patient transfers or staff members infected in the community.  We estimated a median delay of 7 (95% uncertainty interval: 2-15) days from importation of an asymptomatic COVID-19-infected patient to first presentation of COVID-19 symptoms among any patients or staff, at which point an additional 7 (0-25) individuals were infected but did not (yet) show symptoms. Across a range of scenarios, the reference surveillance strategy (testing individuals with COVID-like symptoms with signs of severity) took a median 11-21 days to detect an outbreak. Group testing (pooling specimens from multiple individuals for a single RT-PCR test) patients and staff with any COVID-like symptoms was both the most timely and efficient strategy, detecting outbreaks up to twice as quickly as the reference, and more quickly than other considered strategies while using fewer tests. Maximizing use of available tests via testing cascades was more effective than group testing only when substantial testing resources were available (on the order of 1 test/20 beds/day). Including not merely those with symptoms but also newly admitted patients in group tests and testing cascades reduced delays in outbreak detection for LTCFs actively admitting patients potentially already infected with COVID-19.  Improving COVID-19 surveillance can alert healthcare institutions to emerging outbreaks before they escalate, informing a need for urgent public health intervention in settings with ongoing nosocomial transmission.

title: Understanding the COVID19 Outbreak: A Comparative Data Analytics and Study
abstract: The Coronavirus, also known as the COVID-19 virus, has emerged in Wuhan China since late November 2019. Since that time, it has been spreading at large-scale until today all around the world. It is currently recognized as the world's most viral and severe epidemic spread in the last twenty years, as compared to Ebola 2014,  2012, and SARS 2003. Despite being still in the middle of the outbreak, there is an urgent need to understand the impact of COVID-19. The objective is to clarify how it was spread so fast in a short time worldwide in unprecedented fashion. This paper represents a first initiative to achieve this goal, and it provides a comprehensive analytical study about the Coronavirus. The contribution of this paper consists in providing descriptive and predictive models that give insights into COVID-19 impact through the analysis of extensive data updated daily for the outbreak in all countries. We aim at answering several open questions: How does COVID-19 spread around the world? What is its impact in terms of confirmed and death cases at the continent, region, and country levels? How does its severity compare with other epidemic outbreaks, including Ebola 2014,  2012, and SARS 2003? Is there a correlation between the number of confirmed cases and death cases? We present a comprehensive analytics visualization to address the questions mentioned above. To the best of our knowledge, this is the first systematic analytical papers that pave the way towards a better understanding of COVID-19. The analytical dashboards and collected data of this study are available online .

title: Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Transfer Learning
abstract: In this study, a dataset of X-ray images from patients with common viral pneumonia, bacterial pneumonia, confirmed Covid-19 disease was utilized for the automatic detection of the Coronavirus disease. The point of the investigation is to assess the exhibition of cutting edge convolutional neural system structures proposed over the ongoing years for clinical picture order. In particular, the system called Transfer Learning was received. With transfer learning, the location of different variations from the norm in little clinical picture datasets is a reachable objective, regularly yielding amazing outcomes. The datasets used in this trial. Firstly, a collection of 24000 X-ray images includes 6000 images for confirmed Covid-19 disease,6000 confirmed common bacterial pneumonia and 6000 images of normal conditions. The information was gathered and expanded from the accessible X-Ray pictures on open clinical stores. The outcomes recommend that Deep Learning with X-Ray imaging may separate noteworthy biomarkers identified with the Covid-19 sickness, while the best precision, affectability, and particularity acquired is 97.83%, 96.81%, and 98.56% individually.

title: Achievements and highlights for Infection, Disease and Health

title: Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images
abstract: The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. However, there is still lack of studies on effectively quantifying the lung infection caused by COVID-19. As a basic but challenging task of the diagnostic framework, segmentation plays a crucial role in accurate quantification of COVID-19 infection measured by computed tomography (CT) images. To this end, we proposed a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions. Specifically, we use the Aggregated Residual Transformations to learn a robust and expressive feature representation and apply the soft attention mechanism to improve the capability of the model to distinguish a variety of symptoms of the COVID-19. With a public CT image dataset, we validate the efficacy of the proposed algorithm in comparison with other competing methods. Experimental results demonstrate the outstanding performance of our algorithm for automated segmentation of COVID-19 Chest CT images. Our study provides a promising deep leaning-based segmentation tool to lay a foundation to quantitative diagnosis of COVID-19 lung infection in CT images.

title: A snapshot of SARS-CoV-2 genome availability up to 30th March, 2020 and its implications
abstract: The SARS-CoV-2 pandemic has been growing exponentially, affecting nearly 900 thousand people and causing enormous distress to economies and societies worldwide. A plethora of analyses based on viral sequences has already been published, in scientific journals as well as through non-peer reviewed channels, to investigate SARS-CoV-2 genetic heterogeneity and spatiotemporal dissemination. We examined full genome sequences currently available to assess the presence of sufficient information for reliable phylogenetic and phylogeographic studies in countries with the highest toll of confirmed cases. Although number of-available full-genomes is growing daily, and the full dataset contains sufficient phylogenetic information that would allow reliable inference of phylogenetic relationships, country-specific SARS-CoV-2 datasets still present severe limitations. Studies assessing within country spread or transmission clusters should be considered preliminary at best, or hypothesis generating. Hence the need for continuing concerted efforts to increase number and quality of the sequences required for robust tracing of the epidemic.  Although genome sequences of SARS-CoV-2 are growing daily and contain sufficient phylogenetic information, country-specific data still present severe limitations and should be interpreted with caution.

title: Coronavirus 2019-nCoV: A brief perspective from the front line
abstract: A novel coronavirus, designated as 2019-nCoV, hit the central Chinese city of Wuhan in late December 2019, and subsequently spread rapidly to all provinces of China and multiple countries. As of 0:00 am February 9, 2020, a total of 37,287 cases have been confirmed infection of 2019-nCoV in China mainland, and 302 cases have also been cumulatively reported from 24 countries. According to the latest data, a total of 813 deaths occurred in China mainland, with the mortality reaching approximately 2.2%. At present, there is no vaccine or specific drugs for the human coronavirus. Therefore, it is critical to understand the nature of the virus and its clinical characteristics, in order to respond to the 2019-nCoV outbreak. Thus, the present study briefly but comprehensively summarizes the not much but timely reports on the 2019-nCoV.

title: Noncanonical junctions in subgenomic RNAs of SARS-CoV-2 lead to variant open reading frames
abstract: SARS-CoV-2, a positive-sense RNA virus in the family Coronaviridae, has caused the current worldwide pandemic, known as coronavirus disease 2019 or COVID-19. The definition of SARS-CoV-2 open reading frames is a key step in delineating targets for vaccination and treatment for COVID-19. Here, we report an integrative analysis of three independent direct RNA sequencing datasets of the SARS-CoV-2 transcriptome. We find strong evidence for variant open reading frames (ORFs) encoded by SARS-CoV-2 RNA. A variant transcript for the matrix protein (M) lacking its N-terminal transmembrane domain, initiated by a TTG start codon, is produced by a strong transcriptional regulatory sequence (TRS)-mediated junction within the M ORF and represents up to 19% of all M ORFs. Sporadic non-canonical junctions in the spike (S) ORF lead to N-terminal truncations that remove the N-terminal and receptor-binding domains from up to 25% of S ORFs. Surprisingly, nearly all ORFs from ORF1a identified in these transcriptome sequences were variant. These ORFs contain the first 200-800 amino acids of ORF1a and may represent a mechanism to regulate the relative abundance of ORF1a nonstructural proteins. We show there is strong transcriptome and junctional support for variant ORF1a ORFs in independent direct RNA sequencing and short-read RNA sequencing datasets, and further show that up to 1/3 of these ORFs are expected to have C-terminal fusions with downstream genes. Finally, we show that currently unannotated ORFs are abundant in the SARS-CoV-2 transcriptome. Together, these analyses help to elucidate the diverse coding potential of SARS-CoV-2.

title: COVID-19 Infection Forecasting based on Deep Learning in Iran
abstract: Since December 2019 coronavirus disease (COVID-19) is outbreak from China and infected more than 4,666,000 people and caused thousands of deaths. Unfortunately, the infection numbers and deaths are still increasing rapidly which has put the world on the catastrophic abyss edge. Application of artificial intelligence and spatiotemporal distribution techniques can play a key role to infection forecasting in national and province levels in many countries. As methodology, the presented study employs long short-term memory-based deep for time series forecasting, the confirmed cases in both national and province levels, in Iran. The data were collected from February 19, to March 22, 2020 in provincial level and from February 19, to May 13, 2020 in national level by nationally recognised sources. For justification, we use the recurrent neural network, seasonal autoregressive integrated moving average, Holt winter's exponential smoothing, and moving averages approaches. Furthermore, the mean absolute error, mean squared error, and mean absolute percentage error metrics are used as evaluation factors with associate the trend analysis. The results of our experiments show that the LSTM model is performed better than the other methods on the collected COVID-19 dataset in Iran

title: Integrated, multi-cohort analysis identifies conserved transcriptional signatures across multiple respiratory viruses
abstract: Respiratory viral infections are a significant burden to healthcare worldwide. Many whole genome expression profiles have identified different respiratory viral infection signatures, but these have not translated to clinical practice. Here, we performed two integrated, multi-cohort analyses of publicly available transcriptional data of viral infections. First, we identified a common host signature across different respiratory viral infections that could distinguish (a) individuals with viral infections from healthy controls and from those with bacterial infections, and (b) symptomatic from asymptomatic subjects prior to symptom onset in challenge studies. Second, we identified an influenza-specific host response signature that (a) could distinguish influenza-infected samples from those with bacterial and other respiratory viral infections, (b) was a diagnostic and prognostic marker in influenza-pneumonia patients and influenza challenge studies, and (c) was predictive of response to influenza vaccine. Our results have applications in the diagnosis, prognosis, and identification of drug targets in viral infections.

title: Nucleocapsid gene analysis from an imported case of Middle East respiratory syndrome coronavirus, Malaysia
abstract: To describe the complete nucleocapsid (N) gene region of Middle East respiratory syndrome coronavirus (MERS-CoV) from imported case in Malaysia and the relations with human- and camel-derived MERS-CoV.  Combination of throat and nasal swab specimens was subjected to viral RNA extraction. For screening, the extracted RNA was subjected to real-time RT-PCR targeting upstream of E gene, open reading frame 1b and open reading frame 1a. For confirmation, the RNA was subjected to RT-PCR targeting partial part of the RNA-dependent RNA polymerase and nucleocapsid, followed by amplification of complete N gene region. Nucleotide sequencing of the first Malaysian case of MERS-CoV was performed following the confirmation with real-time RT-PCR detection.  Initial analysis of partial RNA-dependent RNA polymerase and N gene revealed that the nucleotides had high similarity to Jeddah_1_2013 strain. Analysis of complete N gene region (1 242 nucleotides) from the case showed high similarity and yet distinct to the nucleotide sequences of camel-derived MERS-CoV.  From the finding, there are possibilities that the patient acquired the infection from zoonotic transmission from dromedary camels.

title: A potential role for AHR in SARS-CoV-2 pathology
abstract: Coronavirus infection is associated to life-threatening respiratory failure. The aryl hydrocarbon receptor (AHR) was recently identified as a host factor for Zika and dengue viruses; AHR antagonists decrease viral titers and ameliorate ZIKV-induced pathology in vivo. Here we report that AHR is activated during coronavirus infection, impacting anti-viral immunity and lung basal cells associated to tissue repair. Hence, AHR antagonists are candidate therapeutics for the management of coronavirus-infected patients.

title: Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set
abstract: At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter.  In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository.  We started this ongoing data collection on January 28, 2020, leveraging Twitter's streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter's search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020.  Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events.  It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic-and undoubtedly more.

title: Increased generalized anxiety, depression and distress during the COVID-19 pandemic: a cross-sectional study in Germany.
abstract: Since the first cases of the novel coronavirus disease SARS-CoV-2 were reported in December 2019 in China, the virus has spread in most countries. The aim of the present study was to assess initial data on the mental health burden of the German public during the COVID-19 pandemic.  A cross-sectional study was conducted in Germany and collected complete datasets from 15 704 German residents aged 18 years and over. Besides demographics, generalized anxiety (GAD-7), depression (PHQ-2) and psychological distress (DT) were assessed. Furthermore, COVID-19-related fear, trust in governmental actions to face COVID-19 and the subjective level of information regarding COVID-19 were covered.  Significantly increased symptoms were highly prevalent in all dimensions: generalized anxiety (44.9%), depression (14.3%), psychological distress (65.2%) and COVID-19-related fear (59%). Females and younger people reported higher mental burden. Trust in governmental actions to face COVID-19 and the subjective level of information regarding COVID-19 are negatively associated with mental health burden. However, the subjective level of information regarding COVID-19 is positively associated with increased COVID-19-related fear.  The provision of appropriate psychological interventions for those in need and the provision of transparency and comprehensible information are crucial during the current pandemic.

title: Google Dataset Search by the Numbers
abstract: Scientists, governments, and companies increasingly publish datasets on the Web. Google's Dataset Search extracts dataset metadata -- expressed using schema.org and similar vocabularies -- from Web pages in order to make datasets discoverable. Since we started the work on Dataset Search in 2016, the number of datasets described in schema.org has grown from about 500K to almost 30M. Thus, this corpus has become a valuable snapshot of data on the Web. To the best of our knowledge, this corpus is the largest and most diverse of its kind. We analyze this corpus and discuss where the datasets originate from, what topics they cover, which form they take, and what people searching for datasets are interested in. Based on this analysis, we identify gaps and possible future work to help make data more discoverable.

title: Health Belief Model-based Deep Learning Classifiers for Classifying COVID-19 Social Media Content to Examine Public Behaviors towards Physical Distancing.
abstract: Public health authorities (PHAs) have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions towards such interventions are to be identified so that PHAs can effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, to understand health behaviors of people.  This study is aimed at developing and evaluating deep learning-based text classification models for classifying social media content posted during the COVID-19 outbreak, using the key four constructs of HBM. We specifically focus on content related to the physical distancing interventions put forth by PHAs. We intend to test the model with a real-world case study.  The dataset for this study was prepared by analyzing Facebook comments which were posted by the public in response to the COVID-19 posts of three PHAs: Ministry of Health of Singapore (MOH), Centers for Disease Control and Prevention (CDC) and Public Health England (PHE). The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated dataset of 16,752 comments, gated recurrent unit (GRU) based recurrent neural network (RNN) models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used for evaluating the model while specificity, sensitivity and balanced accuracy were the test metrics used for evaluating the classification results in the MOH case study.  The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91 and 0.94 for the constructs perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the testing case study with MOH FB comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits while for perceived susceptibility and perceived barriers, it was 79.6% and 81.5%. The classification models were able to accurately predict the trends in the prevalence of the constructs for the examined days in the case study.  The deep learning-based text classifiers developed in this study help in getting an understanding of the public perceptions towards physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize health behaviors of public through the lens of social media. In future studies, we intend to extend the model for studying public perceptions on other important interventions of PHAs.

title: Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
abstract: Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

title: Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT Scan Images
abstract: COVID-19 has become global threaten. CT acts as an important method of diagnosis. However, human-based interpretation of CT imaging is time consuming. More than that, substantial inter-observer-variation cannot be ignored. We aim at developing a diagnostic tool for artificial intelligence (AI)-based classification of CT images for recognizing COVID-19 and other common infectious diseases of the lung.  In this study, images were retrospectively collected and prospectively analyzed using machine learning. CT scan images of the lung that show or do not show COVID-19 were used to train and validate a classification framework based on convolutional neural network. Five conditions including COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary tuberculosis, and normal lung were evaluated. Training and validation set of images were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were collected from Zhongshan Hospital Xiamen University and the fifth Hospital of Wuhan.  Accuracy, sensitivity, and specificity of the AI framework were reported. For test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively.  The performance of the proposed AI framework has excellent performance of recognizing COVID-19 and other common infectious diseases of the lung, which also has balanced sensitivity and specificity.

title: COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images
abstract: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework; namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images.  Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case.  Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively.  This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work.

title: Index of articles published

title: Ten recommendations for supporting open pathogen genomic analysis in public health.
abstract: Increasingly, public-health agencies are using pathogen genomic sequence data to support surveillance and epidemiological investigations. As access to whole-genome sequencing has grown, greater amounts of molecular data have helped improve the ability to detect and track outbreaks of diseases such as COVID-19, investigate transmission chains and explore large-scale population dynamics, such as the spread of antibiotic resistance. However, the wide adoption of whole-genome sequencing also poses new challenges for public-health agencies that must adapt to support a new set of expertise, which means that the capacity to perform genomic data assembly and analysis has not expanded as widely as the adoption of sequencing itself. In this Perspective, we make recommendations for developing an accessible, unified informatic ecosystem to support pathogen genomic analysis in public-health agencies across income settings. We hope that the creation of this ecosystem will allow agencies to effectively and efficiently share data, workflows and analyses and thereby increase the reproducibility, accessibility and auditability of pathogen genomic analysis while also supporting agency autonomy.

title: Interactive Extractive Search over Biomedical Corpora
abstract: We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries. In contrast to previous attempts to dependency-based search, we introduce a light-weight query language that does not require the user to know the details of the underlying linguistic representations, and instead to query the corpus by providing an example sentence coupled with simple markup. Search is performed at an interactive speed due to efficient linguistic graph-indexing and retrieval engine. This allows for rapid exploration, development and refinement of user queries. We demonstrate the system using example workflows over two corpora: the PubMed corpus including 14,446,243 PubMed abstracts and the CORD-19 dataset, a collection of over 45,000 research papers focused on COVID-19 research. The system is publicly available at

title: Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
abstract: Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNNs) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and adapt our previously developed CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 95.12% (with a sensitivity of 97.91%, a specificity of 91.87%, and a precision of 93.36%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.

title: Pericyte-specific vascular expression of SARS-CoV-2 receptor ACE2 – implications for microvascular inflammation and hypercoagulopathy in COVID-19 patients
abstract: Accumulating clinical observations suggest pathogenesis beyond viral pneumonia and its secondary consequences in COVID-19 patients. In particular, many patients develop profound hyperinflammation and hypercoagulopathy with disseminated thrombogenesis and thromboembolism, which we observe also in a Swedish COVID-19 intensive care patient cohort. To understand these vascular manifestations, it is important to establish the potential vascular entry point(s) of the SARS-CoV-2 virus, i.e. which vascular cell types express the SARS-CoV-2 receptor ACE2. We present data that ACE2 is specifically and highly expressed in microvascular pericytes, but absent from endothelial cells, perivascular macrophages and fibroblasts. Mice with pericyte ablation show increased expression and release of Von Willebrand Factor from microvascular endothelial cells, suggesting that pericytes orchestrate thrombogenic responses in neighboring endothelial cells. Identifying pericytes rather than endothelial cells as the ACE2-expressing cells in the vasculature may explain why hypertension, diabetes and obesity are risk factors for severe COVID-19 patients, as these conditions are characterized by an impaired endothelial barrier function, allowing SARS-CoV-2 to reach and infect the pericytes that are normally shielded from the blood behind an intact endothelial barrier. This novel COVID-19-pericyte hypothesis is testable, offers explanations for some of the most enigmatic and lethal aspects of COVID-19 and calls for further investigations into the possible benefits of preventive anticoagulant therapy.

title: Survey data of COVID-19-related Knowledge, Risk Perceptions and Precautionary Behavior among Nigerians
abstract: In response to the global call for strategic information to understand the novel coronavirus, the dataset presented in this paper is an examination of COVID-19-related knowledge, risk perceptions and precautionary health behavior among Nigerians. The data were generated during the COVID-19 lockdown in the country through a survey distributed via an online questionnaire, assessing socio-demographic information (7 items), knowledge (5 items), information sources (1 item), risk perception (9 items), expected end of lockdown (1 item), and COVID-19 precautionary health behavior (10 items), from 28th March to 4th April, 2020, gathering a total of 1,357 responses. A combination of purposive and snowball techniques helped to select the respondents via Whatsapp and Facebook from 180 cities/towns in the 6 geopolitical zones of Nigeria. The survey data were analyzed using descriptive and inferential statistics. The entire dataset is stored in a Microsoft Excel Worksheet (xlsx) and the questionnaire is attached as a supplementary file. The data will assist in curbing the Coronavirus pandemic by offering evidence for strategic and targeted interventions as well as health policy formulations and implementation.

title: What we know so far: COVID-19 current clinical knowledge and research
abstract: In December 2019, health authorities in Wuhan, China, identified a cluster of pneumonia cases of unknown aetiology linked to the city's South China Seafood Market Subsequent investigations revealed a novel coronavirus, SARS-CoV-2, as the causative agent now at the heart of a major outbreak The rising case numbers have been accompanied by unprecedented public health action, including the wholesale isolation of Wuhan Alongside this has been a robust scientific response, including early publication of the pathogen genome, and rapid development of highly specific diagnostics This article will review the new knowledge of SARS-CoV-2 COVID-19 acute respiratory disease, and summarise its clinical features , Copyright Notice (C) 2020 Royal College of Physicians

title: Thyroid disease in the time of COVID-19
abstract: The novel coronavirus disease COVID-19 is produced by SARS-CoV-2. WHO has declared COVID-19 as a public health emergency, with the most susceptible populations (requiring ventilation) being the elderly, pregnant women and people with associated co-morbidities including heart failure, uncontrolled diabetes, chronic obstructive pulmonary disease, asthma and cancer. However, such general guidance does not provide information regarding COVID-19 risks in patients with suffering from pre-existing thyroid problems, and furthermore, we do not know whether patients with COVID-19 (symptomatic or without symptoms), who have not previously had thyroid issues develop endocrine thyroid dysfunction after infection. The European Society for Endocrinology recently published a statement on COVID-19 and endocrine diseases (Endocrine, 2020); however, thyroid diseases were not mentioned specifically. We have therefore reviewed the current literature on thyroid diseases (excluding cancer) and COVID-19, including data from the previous coronavirus pandemic caused by the SARS-associated coronavirus (SARS-CoV), a member of the same family Coronaviridae leading to severe acute respiratory syndrome (SARS). At the moment there are no data suggesting that thyroid patients are at higher risk of COVID-19, but this requites further research and data analysis.

title: FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19
abstract: In this paper, we present a first multilingual cross-domain dataset of 5182 fact-checked news articles for COVID-19, collected from 04/01/2020 to 15/05/2020. We have collected the fact-checked articles from 92 different fact-checking websites after obtaining references from Poynter and Snopes. We have manually annotated articles into 11 different categories of the fact-checked news according to their content. The dataset is in 40 languages from 105 countries. We have built a classifier to detect fake news and present results for the automatic fake news detection and its class. Our model achieves an F1 score of 0.76 to detect the false class and other fact check articles. The FakeCovid dataset is available at Github.

title: Hypothesis for potential pathogenesis of SARS-CoV-2 infection–a review of immune changes in patients with viral pneumonia
abstract: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with droplets and contact as the main means of transmission. Since the first case appeared in Wuhan, China, in December 2019, the outbreak has gradually spread nationwide. Up to now, according to official data released by the Chinese health commission, the number of newly diagnosed patients has been declining, and the epidemic is gradually being controlled. Although most patients have mild symptoms and good prognosis after infection, some patients developed severe and die from multiple organ complications. The pathogenesis of SARS-CoV-2 infection in humans remains unclear. Immune function is a strong defense against invasive pathogens and there is currently no specific antiviral drug against the virus. This article reviews the immunological changes of coronaviruses like SARS, MERS and other viral pneumonia similar to SARS-CoV-2. Combined with the published literature, the potential pathogenesis of COVID-19 is inferred, and the treatment recommendations for giving high-doses intravenous immunoglobulin and low-molecular-weight heparin anticoagulant therapy to severe type patients are proposed.

title: An Interactive Online Dashboard for Tracking COVID-19 in U.S. Counties, Cities, and States in Real Time
abstract: To create an online resource that informs the public of COVID-19 outbreaks in their area.  This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard.  The web resource, called the COVID-19 Watcher, can be accessed at https://covid19watcher.research.cchmc.org/. It displays COVID-19 data from every county and 188 metropolitan areas in the U.S. Features include rankings of the worst affected areas and auto-generating plots that depict temporal changes in testing capacity, cases, and deaths.  The Centers for Disease Control and Prevention (CDC) do not publish COVID-19 data for local municipalities, so it is critical that academic resources fill this void so the public can stay informed. The data used have limitations and likely underestimate the scale of the outbreak.  The COVID-19 Watcher can provide the public with real-time updates of outbreaks in their area.

title: [Tracheal intubation in patients with severe and critical COVID-19: analysis of 18 cases].
abstract: To analyze the clinical characteristics of patients with severe or critical coronavirus disease 2019 (COVID-19) receiving tracheal intubation.  We analyzed clinical characteristics of 18 severely or critically ill patients with COVID-19 undergoing tracheal intubation. The general demographic and clinical data of the patients including their age, gender, pre- intubation state of consciousness and the ventilation mode were recorded. The anesthesiologists performing the tracheal intubation procedure evaluated and recorded the tracheal intubation conditions of the patients. The changes in the vital signs of the patients before anesthesia induction and after intubation were recorded.  The average ages of these patients were 70.39±8.02 years. Fifteen patients (83.33%) received non- invasive ventilation before tracheal intubation, and 13 patients (72.22%) were conscious before tracheal intubation. After induction of anesthesia, the blood pressure and heart rate of the patients decreased significantly (P < 0.05). Most of the patients (94.44%) were in excellent or good conditions for tracheal intubation, and the first-attempt success rate of tracheal intubation was 100%. Five patients died within 3 weeks following the intubation. Tracheotomy was performed in one patient. Twelve patients were still on endotracheal mechanical ventilation in the intensive care unit, and one of them received ECMO treatment due to poor oxygenation. A total of 16 experienced anesthesiologists participated in tracheal intubation, all with third-level protection during the operation, and no medical staff infection has been detected so far.  For patients with severe and critical COVID-19 and indications of tracheal intubation, we recommend early intubation with invasive respiratory support to improve the treatment efficacy and reduce the mortality. Anesthetic agents should be used carefully during tracheal intubation to ensure patients' safety. The medical staff should have a high-level protection during the intubation to maximally ensure their safety.

title: Forthcoming Articles

title: Association of Cardiovascular Manifestations with In-hospital Outcomes in Patients with COVID-19: A Hospital Staff Data
abstract: The outbreaks of coronavirus disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain a huge threat to the public health worldwide. Clinical data is limited up to now regarding the risk factors in favor of severe conversion of non-severe case with COVID-19.  This study analyzed a hospital staff data to figure out general clinical features of COVID-19 in terms of the association of cardiovascular manifestations (CVMs) with in-hospital outcomes of COVID-19 cases.  Retrospective, single-center case series of 41 consecutive hospitalized health staff with confirmed COVID-19 were collected at the Central Hospital of Wuhan in Wuhan, China, from January 15 to January 24, 2020. Epidemiological, demographic, clinical, laboratory, radiological, treatment data and in-hospital adverse events were collected and analyzed. Final date of follow-up was March 3, 2020. A comparative study was applied between cases with CVMs and those without CVMs.  Of all, clinicians and clinical nurses accounted for 80.5%, while 87.8% of all had history of patient contact. The population was presented with a mean age of 39.1 +- 9.2 and less comorbidities than community population. The three most frequent symptoms of COVID-19 cases analyzed were fever (82.9%), myalgia or fatigue (80.5%) and cough (63.4%). While, the three most frequent initial symptoms were myalgia or fatigue (80.5%), fever (73.2%) and cough (41.5%). There were 95.1% cases featured as non-severe course of disease according to the official standard in China. Patients with CVMs and those without CVMs accounted for 58.5% and 41.5%, respectively. Compared with cases without CVMs, patients with CVMs were presented with lower baseline lymphocyte count (0.99 +- 0.43 and 1.55 +- 0.61, P<0.001), more who had at least once positive nucleic acid detection of throat swab during admission (50.0% and 11.8%, P=0.011), and more received oxygen support (79.2% and 23.5%, P<0.001). The rate of in-hospital adverse events was significantly higher in patients with CVMs group (75.0% and 23.5%, P=0.001). Multivariable logistic regression model indicated that, coexisting with CVMs in COVID-19 patients was not independently associated with in-hospital adverse events.  Most of hospital staff with COVID-19 had history of patient contact, featured non-severe course of disease. Cases with CVMs suffered from more in-hospital adverse events than those without CVMs. But concomitant CVMs were not independently associated with in-hospital adverse events in COVID-19 patients.

title: A living systematic review protocol for COVID-19 clinical trial registrations
abstract: Since the coronavirus disease 2019 (COVID-19) outbreak was identified in December 2019 in Wuhan, China, a strong response from the research community has been observed with the proliferation of independent clinical trials assessing diagnostic methods, therapeutic and prophylactic strategies. While there is no intervention for the prevention or treatment of COVID-19 with proven clinical efficacy to date, tools to distil the current research landscape by intervention, level of evidence and those studies likely powered to address future research questions is essential. This living systematic review aims to provide an open, accessible and frequently updated resource summarising the characteristics of COVID-19 clinical trial registrations. Weekly search updates of the WHO International Clinical Trials Registry Platform (ICTRP) and source registries will be conducted. Data extraction by two independent reviewers of trial characteristic variables including categorisation of trial design, geographic location, intervention type and targets, level of evidence and intervention adaptability to low resource settings will be completed. Descriptive and thematic synthesis will be conducted. A searchable and interactive visualisation of the results database will be created, and made openly available online. Weekly results from the continued search updates will be published and made available on the Infectious Diseases Data Observatory (IDDO) website ( COVID-19 website). This living systematic review will provide a useful resource of COVID-19 clinical trial registrations for researchers in a rapidly evolving context. In the future, this sustained review will allow prioritisation of research targets for individual patient data meta-analysis.

title: COVID-19 Epidemic Dynamics and Population Projections from Early Days of Case Reporting in a 40 million population from Southern India
abstract: India reported its first COVID19 case on 30 January 2020. Since then the epidemic has taken different trajectories across different geographical locations in the country. This study explores the population aggregated trajectories of COVID19 susceptible, infected and recovered or dead cases in the south Indian state of Telangana with a population of approximately 40 million. Information on cases reported from March 2 to April 4 was collated from government records. The susceptible-infected-removed (SIR) model for the spread of an infectious disease was used. Transmission parameters were extracted from existing literature that has emerged over past weeks from other regions with similar population densities as Telangana. Optimisation algorithms were used to get basic reproduction rate for different phases of nonpharmaceutical interventions rolled by the government. Peak accumulation is projected towards end of July with 36% of the population being infected by August 2020 if the population lockdown or social distancing mechanism is not continued. The number of deaths assuming no intervention is projected to be 488000 (95% CI: (329400, 646600)). A draconian enforcement of population lockdown combined with hand and face hygiene adherence would reduce the transmission by at least 99.7% whereas partial social distancing and hygiene would reduce it by 51.2%. Transmission parameters reported should be interpreted with caution as they are population aggregated and do not consider unique characteristics of susceptibility among micro-clusters and vulnerable individuals. More data will need to be collected to optimize transmission parameters and evaluate the full complexity, to simulate real world scenarios in the models.

title: The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2
abstract: Face mask usage by the healthy population in the community to reduce risk of transmission of respiratory viruses remains controversial. We assessed the effect of community-wide mask usage to control coronavirus disease 2019 (COVID-19) in Hong Kong Special Administrative Region (HKSAR).  Patients presenting with respiratory symptoms at outpatient clinics or hospital wards were screened for COVID-19 per protocol. Epidemiological analysis was performed for confirmed cases, especially persons acquiring COVID-19 during mask-off and mask-on settings. The incidence of COVID-19 per-million-population in HKSAR with community-wide masking was compared to that of non-mask-wearing countries which are comparable with HKSAR in terms of population density, healthcare system, BCG vaccination and social distancing measures but not community-wide masking. Compliance of face mask usage in the HKSAR community was monitored.  Within first 100 days (31 December 2019 to 8 April 2020), 961 COVID-19 patients were diagnosed in HKSAR. The COVID-19 incidence in HKSAR (129.0 per-million-population) was significantly lower (p<0.001) than that of Spain (2983.2), Italy (2250.8), Germany (1241.5), France (1151.6), U.S. (1102.8), U.K. (831.5), Singapore (259.8), and South Korea (200.5). The compliance of face mask usage by HKSAR general public was 96.6% (range: 95.7% to 97.2%). We observed 11 COVID-19 clusters in recreational ‘mask-off’ settings compared to only 3 in workplace ‘mask-on’ settings (p = 0.036 by Chi square test of goodness-of-fit).  Community-wide mask wearing may contribute to the control of COVID-19 by reducing virus shedding in saliva and respiratory droplets from individuals with subclinical or mild COVID-19.

title: Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?
abstract: Computational models for the simulation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic evolution would be extremely useful to support authorities in designing healthcare policies and lockdown measures to contain its impact on public health and economy. In Italy, the devised forecasts have been mostly based on a pure data-driven approach, by fitting and extrapolating open data on the epidemic evolution collected by the Italian Civil Protection Center. In this respect, SIR epidemiological models, which start from the description of the nonlinear interactions between population compartments, would be a much more desirable approach to understand and predict the collective emergent response. The present contribution addresses the fundamental question whether a SIR epidemiological model, suitably enriched with asymptomatic and dead individual compartments, could be able to provide reliable predictions on the epidemic evolution. To this aim, a machine learning approach based on particle swarm optimization (PSO) is proposed to automatically identify the model parameters based on a training set of data of progressive increasing size, considering Lombardy in Italy as a case study. The analysis of the scatter in the forecasts shows that model predictions are quite sensitive to the size of the dataset used for training, and that further data are still required to achieve convergent -- and therefore reliable -- predictions.

title: Data analysis of COVID-2019 epidemic using machine learning methods: a case study of India
abstract: At this time, COVID-2019 is spreading its foot in the form of a huge epidemic for the world. This epidemic is spreading its foot very fast in India too. One of the World Health Organization states that COVID-2019 is a serious disease that spreads from one person to another at very fast speed through contact routes and respiratory drops. On this day, India and the world should rise to an effective step to analyze this disease and eliminate the effects of this epidemic. In this paper presented, the growing database of COVID-2019 has been analyzed from March 1, 2020, to April 11, 2020, and the next one is predicted for the number of patients suffering from the rising COVID-2019. Different regression analysis models have been utilized for data analysis of COVID-2019 of India based on data stored by Kaggle in between 1 March 2020 to 11 April 2020. In this study, we have been utilized six regression analysis based models namely quadratic, third degree, fourth degree, fifth degree, sixth degree, and exponential polynomial respectively for the COVID-2019 dataset. We have calculated the root mean square of these six regression analysis models. In these six models, the root mean square error of sixth degree polynomial is very less in compared other like quadratic, third degree, fourth degree, fifth degree, and exponential polynomial. Therefore the sixth degree polynomial regression model is very good models for forecasting the next 6 days for COVID-2019 data analysis in India. In this study, we have found that the sixth degree polynomial regression models will help Indian doctors and the Government in preparing their plans in the next 7 days. Based on further regression analysis study, this model can be tuned for forecasting over long term intervals.

title: Evolutionary characterization of the emerging porcine epidemic diarrhea virus worldwide and 2014 epidemic in Taiwan
abstract: Since 2010, a new variant of PEDV belonging to Genogroup 2 has been transmitting in China and further spreading to the Unites States and other Asian countries including Taiwan. In order to characterize in detail the temporal and geographic relationships among PEDV strains, the present study systematically evaluated the evolutionary patterns and phylogenetic resolution in each gene of the whole PEDV genome in order to determine which regions provided the maximal interpretative power. The result was further applied to identify the origin of PEDV that caused the 2014 epidemic in Taiwan. Thirty-four full genome sequences were downloaded from GenBank and divided into three non-mutually exclusive groups, namely, worldwide, Genogroup 2 and China, to cover different ranges of secular and spatial trends. Each dataset was then divided into different alignments by different genes for likelihood mapping and phylogenetic analysis. Our study suggested that both nsp3 and S genes contained the highest phylogenetic signal with substitution rate and phylogenetic topology similar to those obtained from the complete genome. Furthermore, the proportion of nodes with high posterior support (posterior probability >0.8) was similar between nsp3 and S genes. The nsp3 gene sequences from three clinical samples of swine with PEDV infections were aligned with other strains available from GenBank and the results suggested that the virus responsible for the 2014 PEDV outbreak in Taiwan clustered together with Clade I from the US within Genogroup 2. In conclusion, the current study identified the nsp3 gene as an alternative marker for a rapid and unequivocal classification of the circulating PEDV strains which provides complementary information to the S gene in identifying the emergence of epidemic strain resulting from recombination.

title: Epidemiological Identification of A Novel Pathogen in Real Time: Analysis of the Atypical Pneumonia Outbreak in Wuhan, China, 2019–2020
abstract: Virological tests have now shown conclusively that a novel coronavirus is causing the 2019–2020 atypical pneumonia outbreak in Wuhan, China. We demonstrate that non-virological descriptive characteristics could have determined that the outbreak is caused by a novel pathogen in advance of virological testing. Characteristics of the ongoing outbreak were collected in real time from two medical social media sites. These were compared against characteristics of eleven pathogens that have previously caused cases of atypical pneumonia. The probability that the current outbreak is due to “Disease X” (i.e., previously unknown etiology) as opposed to one of the known pathogens was inferred, and this estimate was updated as the outbreak continued. The probability (expressed as a percentage) that Disease X is driving the outbreak was assessed as over 29% on 31 December 2019, one week before virus identification. After some specific pathogens were ruled out by laboratory tests on 5 January 2020, the inferred probability of Disease X was over 49%. We showed quantitatively that the emerging outbreak of atypical pneumonia cases is consistent with causation by a novel pathogen. The proposed approach, which uses only routinely observed non-virological data, can aid ongoing risk assessments in advance of virological test results becoming available.

title: Evolution of genomes, host shifts and the geographic spread of SARS‐CoV and related coronaviruses
abstract: Severe acute respiratory syndrome (SARS) is a novel human illness caused by a previously unrecognized coronavirus (CoV) termed SARS‐CoV. There are conflicting reports on the animal reservoir of SARS‐CoV. Many of the groups that argue carnivores are the original reservoir of SARS‐CoV use a phylogeny to support their argument. However, the phylogenies in these studies often lack outgroup and rooting criteria necessary to determine the origins of SARS‐CoV. Recently, SARS‐CoV has been isolated from various species of Chiroptera from China (e.g., Rhinolophus sinicus) thus leading to reconsideration of the original reservoir of SARS‐CoV. We evaluated the hypothesis that SARS‐CoV isolated from Chiroptera are the original zoonotic source for SARS‐CoV by sampling SARS‐CoV and non‐SARS‐CoV from diverse hosts including Chiroptera, as well as carnivores, artiodactyls, rodents, birds and humans. Regardless of alignment parameters, optimality criteria, or isolate sampling, the resulting phylogenies clearly show that the SARS‐CoV was transmitted to small carnivores well after the epidemic of SARS in humans that began in late 2002. The SARS‐CoV isolates from small carnivores in Shenzhen markets form a terminal clade that emerged recently from within the radiation of human SARS‐CoV. There is evidence of subsequent exchange of SARS‐CoV between humans and carnivores. In addition SARS‐CoV was transmitted independently from humans to farmed pigs (Sus scrofa). The position of SARS‐CoV isolates from Chiroptera are basal to the SARS‐CoV clade isolated from humans and carnivores. Although sequence data indicate that Chiroptera are a good candidate for the original reservoir of SARS‐CoV, the structural biology of the spike protein of SARS‐CoV isolated from Chiroptera suggests that these viruses are not able to interact with the human variant of the receptor of SARS‐CoV, angiotensin‐converting enzyme 2 (ACE2). In SARS‐CoV we study, both visually and statistically, labile genomic fragments and, putative key mutations of the spike protein that may be associated with host shifts. We display host shifts and candidate mutations on trees projected in virtual globes depicting the spread of SARS‐CoV. These results suggest that more sampling of coronaviruses from diverse hosts, especially Chiroptera, carnivores and primates, will be required to understand the genomic and biochemical evolution of coronaviruses, including SARS‐CoV.

title: Estimation of risk factors for COVID-19 mortality - preliminary results
abstract: Since late December 2019 a new epidemic outbreak has emerged from Whuhan, China. Rapidly the new coronavirus has spread worldwide. China CDC has reported results of a descriptive exploratory analysis of all cases diagnosed until the 11th February 2020, presenting the epidemiologic curves and geo-temporal spread of COVID-19 along with case fatality rate according to some baseline characteristics, such as age, gender and several well-established high prevalence comorbidities. Despite this, we intend to increase even further the predictive value of that manuscript by presenting the odds ratio for mortality due to COVID-19 adjusted for the presence of those comorbidities and baseline characteristics such as age and gender. Besides, we present a way to determine the risk of each particular patient, given his characteristics. We found that age is the variable that presents higher risk of COVID-19 mortality, where 60 or older patients have an OR = 18.8161 (CI95%[7.1997; 41.5517]). Regarding comorbidities, cardiovascular disease appears to be the riskiest (OR=12.8328 CI95%[10.2736; 15.8643], along with chronic respiratory disease (OR=7.7925 CI95%[5.5446; 10.4319]). Males are more likely to die from COVID-19 (OR=1.8518 (CI95%[1.5996; 2.1270]). Some limitations such as the lack of information about the correct prevalence of gender per age or about comorbidities per age and gender or the assumption of independence between risk factors are expected to have a small impact on results. A final point of paramount importance is that the equation presented here can be used to determine the probability of dying from COVID-19 for a particular patient, given its age interval, gender and comorbidities associated.

title: Development and validation of the COVID-19 severity index (CSI): a prognostic tool for early respiratory decompensation
abstract: The goal of this study was to create a predictive model of early hospital respiratory decompensation among patients with COVID-19.  Observational, retrospective cohort study.  Nine-hospital health system within the Northeastern United States.  Adult patients ([≥] 18 years) admitted from the emergency department who tested positive for SARS-CoV-2 (COVID-19) up to 24 hours after initial presentation. Patients meeting criteria for critical respiratory illness within 4 hours of arrival were excluded. Main outcome and performance measures: We used a composite endpoint of respiratory critical illness as defined by oxygen requirement beyond low-flow nasal cannula (e.g., non-rebreather mask, high-flow nasal cannula, bi-level positive pressure ventilation), intubation, or death within the first 24 hours of hospitalization. We developed predictive models using patient demographic and clinical data collected during those first 4 hours. Eight hospitals were used for development and internal validation (n=932) and 1 hospital for model external validation (n=240). Predictive variables were identified using an ensemble approach that included univariate regression, random forest, logistic regression with LASSO, Chi-square testing, gradient boosting information gain, and gradient boosting Shapley additive explanation (SHAP) values prior to manual curation. We generated two predictive models, a quick COVID-19 severity index (qCSI) that uses only exam and vital sign measurements, and a COVID-19 severity index (CSI) machine learning model. Using area under receiver operating characteristic (AU-ROC), precision-recall curves (AU-PRC) and calibration metrics, we compare the qCSI and CSI to three illness scoring systems: Elixhauser mortality score, qSOFA, and CURB-65. We present performance of qCSI and CSI on an external validation cohort.  During the study period from March 1, 2020 to April 27, 2020, 1,792 patients were admitted with COVID-19. Six-hundred and twenty patients were excluded based on age or critical illness within the first 4 hours, yielding 1172 patients in the final cohort. Of these patients, 144 (12.3%) met the composite endpoint within the first 24 hours. The qCSI (AU-ROC: 0.90 [0.85-0.96]) comprised of nasal cannula flow rate, respiratory rate, and minimum documented pulse oximetry outperformed the baseline models (qSOFA: 0.76 [0.69-0.85]; Elixhauser: 0.70 [0.62-0.80]; CURB-65: AU-ROC 0.66 [0.58-0.77]) and was validated on an external cohort (AU-ROC: 0.82). The machine learning-based CSI had superior performance on the training cohort (AU-ROC: 0.91 [0.86-0.97]), but was unlikely to provide practical improvements in clinical settings.  A significant proportion of admitted COVID-19 patients decompensate within 24 hours of hospital presentation and these events are accurately predicted using respiratory exam findings within a simple scoring system.

title: Neuartiges Coronavirus (SARS-CoV-2)

title: Asthma and COVID-19 in children - a systematic review and call for data
abstract: Whether asthma constitutes a risk factor for COVID-19 is unclear.  We performed a systematic literature search in three stages: First, we reviewed PubMed, EMBASE and CINAHL for systematic reviews of SARS-CoC-2 and COVID-19 in pediatric populations, and reviewed their primary articles; next, we searched PubMed for studies on COVID-19 or SARS-CoV-2 and asthma/wheeze, and evaluated whether the resulting studies included pediatric populations; lastly, we repeated the second search in BioRxiv.org and MedRxiv.org to find pre-prints that may have information on pediatric asthma.  In the first search, eight systematic reviews were found, of which five were done in pediatric population; after reviewing 67 primary studies we found no data on pediatric asthma as a comorbidity for COVID-19. In the second search, we found 25 results in PubMed, of which five reported asthma in adults, but none included data on children. In the third search, 14 pre-prints in MedRxiv were identified with data on asthma, but again none with pediatric data. We found only one report by the U.S. CDC stating that 40/345 (~11.5%) children with data on chronic conditions had “chronic lung diseases including asthma”.  There is scarcely any data on whether childhood asthma (or other pediatric respiratory diseases) constitute risk factors for SARS-CoV-2 infection or COVID-19 severity. Studies are needed that go beyond counting the number of cases in the pediatric age range.

title: Data sharing for novel coronavirus (COVID-19)

title: Likelihood of survival of coronavirus disease 2019

title: P1460 – P1884

title: Multilevel Integrated Model with a Novel Systems Approach (MIMANSA) for Simulating the Spread of COVID-19
abstract: Abstract COVID-19 has affected people's lives all over the world. It has created a perplexing situation about what actions one should and should not take. Mathematical modeling of biological systems is challenging and gives a different perspective, especially in decision making under multiple complex scenarios. Public health officials grapple with multiple issues such as recommending a lockdown, contact tracing, promoting the mask usage, social distancing, and frequent handwashing, as well as keeping the families of patients in isolation for the incubation period. It is even more challenging to find the optimal combination of all of the above without the use of a suitable mathematical model. There are many different approaches to modeling the spread of SARS-CoV-2, the virus that causes COVID-19. Some models are easy to use, while others need extensive use of high-end computers. However, models to assist public health official's decision making are hard to find. In this paper, we discuss a novel systems approach to building a model for simulating the spread of COVID-19. The model, MIMANSA, divides an individual's in-person social interactions into three areas, namely home, workplace, and public places. While tracking the in-person interactions, the model follows the virus spread. Internally, the model labels healthy people who turn into silent carriers, virus-infected patients, or healthy carriers. It tracks down to the smallest level of a single day interaction. As and when a new silent carrier is created, the model automatically expands and builds a network of virus spread. All single-day blocks are integrated to get the final result. MIMANSA is novel due to its ability to build a virus spread network as a multilevel, integrated model, and in the end, enable one to make complex decisions with ease. MIMANSA is trained and validated using the data from the www.COVID-19India.org website. It does not use any arbitrary constants. All its parameters have a physical significance and are measurable. Once trained, the parameter estimation is complete, and the model is ready to run multiple scenarios. MIMANSA has four control mechanisms that a user can use. It helps one simulate the what-if scenarios. The first one is to control the exposure level to the virus depending on the number of hours spent with a silent carrier. The second provides control over the infection rate or the probability of a healthy person getting infected in the presence of a silent carrier. The third one allows the user to control lockdown effectiveness percentage, and the fourth one gives control over quarantine percentage. Inside the model, MIMANSA differentiates between virus-infected patients, silent carriers, and healthy carriers. MIMANSA has the capability to consider variations in virus activity levels of every asymptomatic patient, varying the exposure to the virus, and varying the infection rate depending on the person's immunity. MIMANSA can simulate scenarios to study the impact of many different conditions simultaneously. MIMANSA assists public health officials in complex decision making, enables scientists in projecting the SARS-CoV-2 virus spread, and aids hospital administrators in management. MIMANSA will play a significant role in finding the balance between the effect of strict lockdown on the economy vs. the marginally high number of COVID-19 patients with a bit relaxed lockdown.

title: Artificial Intelligence and COVID-19: A Multidisciplinary Approach
abstract: The COVID-19 pandemic is taking a colossal toll in human suffering and lives. A significant amount of new scientific research and data sharing is underway due to the pandemic which is still rapidly spreading. There is now a growing amount of coronavirus related datasets as well as published papers that must be leveraged along with artificial intelligence (AI) to fight this pandemic by driving news approaches to drug discovery, vaccine development, and public awareness. AI can be used to mine this avalanche of new data and papers to extract new insights by cross-referencing papers and searching for patterns that AI algorithms could help discover new possible treatments or help in vaccine development. Drug discovery is not a trivial task and AI technologies like deep learning can help accelerate this process by helping predict which existing drugs, or brand-new drug-like molecules could treat COVID-19. AI techniques can also help disseminate vital information across the globe and reduce the spread of false information about COVID-19. The positive power and potential of AI must be harnessed in the fight to slow the spread of COVID-19 in order to save lives and limit the economic havoc due to this horrific disease.

title: A protocol for adding knowledge to Wikidata, a case report
abstract: Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a “commons”. Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modelled with entity schemas represented by Shape Expressions. As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable, is demonstrated by integrating data from NCBI Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, Human Coronavirus NL63, Human coronavirus 229E, Human coronavirus HKU1, Human coronavirus OC4).

title: COVID-19: Public Compliance with and Public Support for Stay-at-Home Mitigation Strategies
abstract: Governments worldwide have recommended unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. As pressure mounts to scale back these measures, understanding public compliance with and priorities for COVID-19 mitigation is critical. The main aim of this study was to assess public compliance with and support for government-imposed stay-at-home orders in nations and cities with different COVID-19 infection and death rates.  In this cross-sectional study, questionnaires were administered to nationally representative respondents from April 2-8, 2020.  Regions with different disease prevalence included two nations [the United States (US, high) and Australia (AU, low)] and two cities [New York (NY, high) and Los Angeles (LA, low)].  For adults 18 years or older residing in specified regions, eligible respondents were empaneled until representative quotas were reached for age, gender, and either race and ethnicity (US, NY, LA) or ancestry (AU), matching the 2010 US or 2016 AU census. Of 8718 eligible potential respondents, 5573 (response rate, 63.9%) completed surveys ). The median age was 47 years (range, 18-89); 3039 (54.5%) were female.  The prevalence of COVID-19 in each region (cumulative infections, deaths) as of April 8, 2020: US (458610, 15659), AU (5956, 45), NY (81803, 4571), LA (7530, 198).  Public compliance with and attitudes regarding government-imposed stay-at-home orders were evaluated and compared between regions.  Of 5573 total respondents, 4560 (81.8%) reported compliance with recommended quarantine or stay-at-home policies (range of samples, 75.5%-88.2%). Despite significant disruptions of social and work life, health, and behavior, 5022 respondents (90.1%) supported government-imposed stay-at-home orders (range of samples, 88.9%-93.1%). Of these, 90.8% believe orders should last at least three more weeks or until public health or government officials recommend, with such support spanning the political spectrum.  Public compliance with stringent quarantine and stay-at-home policies was very high, in both highly-affected (US, NY) and minimally-affected regions (AU, LA). Despite extensive disruption of respondents′ lives, the vast majority supported continuation of long-term government-imposed stay-at-home orders. These findings have important implications for policymakers grappling with the decision as to when to lift restrictions.

title: CovidAID: COVID-19 Detection Using Chest X-Ray
abstract: The exponential increase in COVID-19 patients is overwhelming healthcare systems across the world. With limited testing kits, it is impossible for every patient with respiratory illness to be tested using conventional techniques (RT-PCR). The tests also have long turn-around time, and limited sensitivity. Detecting possible COVID-19 infections on Chest X-Ray may help quarantine high risk patients while test results are awaited. X-Ray machines are already available in most healthcare systems, and with most modern X-Ray systems already digitized, there is no transportation time involved for the samples either. In this work we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing. This may be useful in an inpatient setting where the present systems are struggling to decide whether to keep the patient in the ward along with other patients or isolate them in COVID-19 areas. It would also help in identifying patients with high likelihood of COVID with a false negative RT-PCR who would need repeat testing. Further, we propose the use of modern AI techniques to detect the COVID-19 patients using X-Ray images in an automated manner, particularly in settings where radiologists are not available, and help make the proposed testing technology scalable. We present CovidAID: COVID-19 AI Detector, a novel deep neural network based model to triage patients for appropriate testing. On the publicly available covid-chestxray-dataset [2], our model gives 90.5% accuracy with 100% sensitivity (recall) for the COVID-19 infection. We significantly improve upon the results of Covid-Net  on the same dataset.

title: Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms
abstract: COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China and spread across the globe with unprecedented effect and has now become the greatest crisis of the modern era. The COVID-19 has proved much more pervasive demands for diagnosis that has driven researchers to develop more intelligent, highly responsive and efficient detection methods. In this work, we focus on proposing AI tools that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images dataset. The result of the experiments shows that the utilized models can provide accuracy up to 98 % via pre-trained network and 94.1 % accuracy by using the modified CNN.

title: PCR sequencing of the spike genes of geographically and chronologically distinct human coronaviruses 229E
abstract: A reverse transcription nested PCR (RT-PCR) sequencing methodology was developed and used to generate sequence data from the spike genes of three geographically and chronologically distinct human coronaviruses 229E. These three coronaviruses were isolated originally from the USA in the 1960s (human coronavirus 229E strain ATCC VR-74), the UK in the 1990s (human coronavirus 229E LRI 281) and Ghana (human coronavirus 229E A162). Upon translation and alignment with the published spike protein sequence of human coronavirus 229E ‘LP’ (isolated in the UK in the 1970s), it was found that variation within the translated protein sequences was rather limited. In particular, minimal variation was observed between the translated spike protein sequence of human coronaviruses 229E LP and ATCC VR-74 (1/1012 amino acid differences), whilst most variation was observed between the translated spike protein sequence of human coronaviruses 229E LP and A162 (47/1012 amino acid changes). Further, the translated spike protein sequence of human coronavirus 229E A162 showed three clusters of amino acid changes, situated within the 5′ half of the translated spike protein sequence.

title: Ancestral origin, antigenic resemblance and epidemiological insights of novel coronavirus (SARS-CoV-2): Global burden and Bangladesh perspective
abstract: SARS-CoV-2, a new coronavirus strain responsible for COVID-19 has emerged in Wuhan City, China and still continuing its worldwide pandemic nature. Considering the severity of the disease, a number of studies are underway, and full genomic sequences have already been released in the last few weeks to enable the understanding of the evolutionary origin and molecular characteristics of this virus. Bioinformatics analysis, satellite derived imaging data and epidemiological attributes were employed to investigate origin, immunogenic resemblance and global threat of newly pandemic SARS-CoV-2 including Bangladesh perspective. Based on currently available genomic information, a phylogeny study was employed focusing four types of representative viral proteins (spike, membrane, envelope and nucleoprotein) of SARS-CoV-2, HCoV-229E, HCoV-OC43, SARS-CoV, HCoV-NL63, HKU1, MERS-CoV, HKU4, HKU5 and BufCoV-HKU26. The findings clearly demonstrated that SARS-CoV-2 exhibited evolutionary convergent relation with previously reported SARS-CoV. It was also found that SARS-CoV-2 proteins were highly similar and identical to SARS-CoV proteins, though proteins from other coronaviruses showed lower level of similarity and identical patterns. The cross-checked conservancy analysis of SARS-CoV-2 antigenic epitopes showed significant conservancy with antigenic epitopes derived from SARS-CoV. The study also prioritized the temperature comparison through satellite imaging alongside compiling and analyzing the epidemiological outbreak information on the 2019 novel coronavirus based on several open datasets on COVID-19 (SARS-CoV-2) and discussed possible threats to Bangladesh.

title: Database resources of the National Center for Biotechnology Information
abstract: The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank(®) nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. Additional NCBI resources focus on literature (PubMed Central (PMC), Bookshelf and PubReader), health (ClinVar, dbGaP, dbMHC, the Genetic Testing Registry, HIV-1/Human Protein Interaction Database and MedGen), genomes (BioProject, Assembly, Genome, BioSample, dbSNP, dbVar, Epigenomics, the Map Viewer, Nucleotide, Probe, RefSeq, Sequence Read Archive, the Taxonomy Browser and the Trace Archive), genes (Gene, Gene Expression Omnibus (GEO), HomoloGene, PopSet and UniGene), proteins (Protein, the Conserved Domain Database (CDD), COBALT, Conserved Domain Architecture Retrieval Tool (CDART), the Molecular Modeling Database (MMDB) and Protein Clusters) and chemicals (Biosystems and the PubChem suite of small molecule databases). The Entrez system provides search and retrieval operations for most of these databases. Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized datasets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

title: Estimation of time-varying reproduction numbers underlying epidemiological processes: a new statistical tool for the COVID-19 pandemic
abstract: The coronavirus pandemic has rapidly evolved into an unprecedented crisis. The susceptible-infectious-removed (SIR) model and its variants have been used for modeling the pandemic. However, time-independent parameters in the classical models may not capture the dynamic transmission and removal processes, governed by virus containment strategies taken at various phases of the epidemic. Moreover, very few models account for possible inaccuracies of the reported cases. We propose a Poisson model with time-dependent transmission and removal rates to account for possible random errors in reporting and estimate a time-dependent disease reproduction number, which may be used to assess the effectiveness of virus control strategies. We apply our method to study the pandemic in several severely impacted countries, and analyze and forecast the evolving spread of the coronavirus. We have developed an interactive web application to facilitate readers' use of our method.

title: Covid-19 clinical data analysis using Ball Mapper
abstract: In this note we provide a result of analysis of blood test data from patients with SARS-Cov-2 using Ball Mapper Algorithm. We observe that patients with the virus and in particularly patients who end up in Intensive Care Unit have quite narrow values of those parameters. Please note that this is a preliminary work and it need to be validated on much larger dataset which we are trying to acquire at the moment.

title: An "Infodemic": Leveraging High-Volume Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak
abstract: Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the CDC activated its Emergency Operations Center and the WHO released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment has evolved in the early stages of any outbreak, including the COVID-19 epidemic, has not been described.  To quantify and understand early changes in Twitter activity, content, and sentiment about the COVID-19 epidemic.  Observational study.  Twitter platform.  All Twitter users who created or sent a message from January 14th to 28th, 2020.  We extracted tweets matching hashtags related to COVID-19 and measured frequency of keywords related to infection prevention practices, vaccination, and racial prejudice. We performed a sentiment analysis to identify emotional valence and predominant emotions. We conducted topic modeling to identify and explore discussion topics over time.  We evaluated 126,049 tweets from 53,196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Nearly half (49.5%) of all tweets expressed fear and nearly 30% expressed surprise. The frequency of racially charged tweets closely paralleled the number of newly diagnosed cases of COVID-19. The economic and political impact of the COVID-19 was the most commonly discussed topic, while public health risk and prevention were among the least discussed.  Tweets with negative sentiment and emotion parallel the incidence of cases for the COVID-19 outbreak. Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and target public health messages based on user interest and emotion.

title: Time Series Prediction of COVID-19 by Mutation Rate Analysis using Recurrent Neural Network-based LSTM Model
abstract: SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of May 18, already more than 4.8 million people have been infected and 316k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, The United States, and the rest of the World. It has been found that a huge amount of Thymine (T) and Adenine (A) are mutated to other nucleotides for all regions, but codons are not frequently mutating like nucleotides. A recurrent neural network-based Long Short Term Memory (LSTM) model has been applied to predict the future mutation rate of this virus. The LSTM model gives Root Mean Square Error (RMSE) of 0.06 in testing and 0.04 in training, which is an optimized value. Using this train and testing process, the nucleotide mutation rate of 400(th) patient in future time has been predicted. About 0.1% increment in mutation rate is found for mutating of nucleotides from T to C and G, C to G and G to T. While a decrement of 0.1% is seen for mutating of T to A, and A to C. It is found that this model can be used to predict day basis mutation rates if more patient data is available in updated time.

title: Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays
abstract: We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestation of COVID-19 with chest X-rays. This disease is caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, also known as the novel Coronavirus (2019-nCoV). A custom convolutional neural network and a selection of ImageNet pretrained models are trained and evaluated at patient-level on publicly available CXR collections to learn modality-specific feature representations. The learned knowledge is transferred and fine-tuned to improve performance and generalization in the related task of classifying CXRs as normal, showing bacterial pneumonia, or COVID-19-viral abnormalities. The best performing models are iteratively pruned to reduce complexity and improve memory efficiency. The predictions of the best-performing pruned models are combined through different ensemble strategies to improve classification performance. Empirical evaluations demonstrate that the weighted average of the best-performing pruned models significantly improves performance resulting in an accuracy of 99.01% and area under the curve of 0.9972 in detecting COVID-19 findings on CXRs. The combined use of modality-specific knowledge transfer, iterative model pruning, and ensemble learning resulted in improved predictions. We expect that this model can be quickly adopted for COVID-19 screening using chest radiographs.

title: Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter
abstract: The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including"updates about confirmed cases,""COVID-19 related death,""cases outside China (worldwide),""COVID-19 outbreak in South Korea,""early signs of the outbreak in New York,""Diamond Princess cruise,""economic impact,""Preventive measures,""authorities,"and"supply chain."Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.

title: Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans
abstract: Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT scans faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT scans. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on a COVID-19 infection dataset demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art

title: COVID-19 in Brazil: advantages of a socialized unified health system and preparation to contain cases
abstract: The outbreak of new coronavirus disease 2019 (COVID-19) reported for the first time in Wuhan, China in late December 2019 have rapidly spread to other countries and it was declared on January 30, 2020 as a public health emergency of international concern (PHEIC) by the World Health Organization. Before the first COVID-19 cases were reported in Brazil, several measures have been implemented including the adjustment of legal framework to carry out isolation and quarantine. As the cases increased significantly, new measures, mainly to reduce mortality and severe cases, have also been implemented. Rapid and robust preparedness actions have been undertaken in Brazil while first cases have not yet been identified in Latin-American. The outcome of this early preparation should be analyzed in future studies.

title: CoV Genome Tracker: tracing genomic footprints of Covid-19 pandemic
abstract: Genome sequences constitute the primary evidence on the origin and spread of the 2019-2020 Covid-19 pandemic. Rapid comparative analysis of coronavirus SARS-CoV-2 genomes is critical for disease control, outbreak forecasting, and developing clinical interventions. CoV Genome Tracker is a web portal dedicated to trace Covid-19 outbreaks in real time using a haplotype network, an accurate and scalable representation of genomic changes in a rapidly evolving population. We resolve the direction of mutations by using a bat-associated genome as outgroup. At a broader evolutionary time scale, a companion browser provides gene-by-gene and codon-by-codon evolutionary rates to facilitate the search for molecular targets of clinical interventions. Availability and Implementation CoV Genome Tracker is publicly available at http://cov.genometracker.org and updated weekly with the data downloaded from GISAID (http://gisaid.org). The website is implemented with a custom JavaScript script based on jQuery (https://jquery.com) and D3-force (https://github.com/d3/d3-force). Contact weigang@genectr.hunter.cuny.edu, City University of New York, Hunter College Supplementary Information All supporting scripts developed in JavaScript, Python, BASH, and PERL programming languages are available as Open Source at the GitHub repository https://github.com/weigangq/cov-browser.

title: Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US
abstract: During a disease outbreak, timely non-medical interventions are critical in preventing the disease from growing into an epidemic and ultimately a pandemic. However, taking quick measures requires the capability to detect the early warning signs of the outbreak. This work collects Twitter posts surrounding the 2020 COVID-19 pandemic expressing the most common symptoms of COVID-19 including cough and fever, geolocated to the United States. Through examining the variation in Twitter activities at the state level, we observed a temporal lag between the rises in the number of symptom reporting tweets and officially reported positive cases which varies between 5 to 19 days.

title: Assessing the Global Tendency of COVID-19 Outbreak
abstract: COVID-19 is now widely spreading around the world as a global pandemic. In this report, we estimate the global tendency of COVID-19 and analyze the associated global epidemic risk, given that the status quo is continued without further measures being taken. The results show that the global R_0, excluding China, is estimated to be 2.49 (95% CI: 2.15 - 2.92). The United States, Germany, Italy and Spain have peak values over 100,000. According to dynamical model and cluster analysis, we category the globe into four type regional epicenters of the outbreak: Southeast Asia extending southward to Oceania, the Middle East, Western Europe and North America. Among them, Western Europe will become the major center of the outbreak. The peak values in Germany, Italy and Spain are estimated to be 105,903, 127,283 and 152,539, respectively. The United States is the country with the most serious outbreak trend. Based on the current control measures by Mar. 27, 2020, the peak value in the United States will reach 400,892. Above all, if the current control measures are maintained, the cumulative number of patients worldwide will be 1,442,523 (95% CI: 1,052,577 - 8,981,440). We also estimated the diagnosis rate, recovery rate and infection degree of each country or region, and use clustering algorithm to retrieve countries or regions with similar epidemic characteristics. Different suggestions are proposed for countries or regions in different clusters.

title: Significant Applications of Big Data in COVID-19 Pandemic

title: Complete Genome Sequence of a Novel Feline Astrovirus from a Domestic Cat in Hong Kong
abstract: We report the first complete genome sequence of a feline astrovirus (FAstV), FAstV2 strain 1637F, identified from a domestic cat. The genome is 6,779 nucleotides (nt) in length and consists of three overlapping open reading frames (ORF1a-ORF1b-ORF2). Sequence analysis suggests that FAstV2 represents a new FAstV genotype that is closely related to human astroviruses.

title: Genetic evolution analysis of 2019 novel coronavirus and coronavirus from other species
abstract: The Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a Public Health Emergency of International Concern. However, so far, there are still controversies about the source of the virus and its intermediate host. Here, we found the novel coronavirus was closely related to coronaviruses derived from five wild animals, including Paguma larvata, Paradoxurus hermaphroditus, Civet, Aselliscus stoliczkanus and Rhinolophus sinicus, and was in the same branch of the phylogenetic tree. However, genome and ORF1a homology show that the virus is not the same coronavirus as the coronavirus derived from these five animals, whereas the virus has the highest homology with Bat coronavirus isolate RaTG13.

title: deepMINE - Natural Language Processing based Automatic Literature Mining and Research Summarization for Early-Stage Comprehension in Pandemic Situations specifically for COVID-19
abstract: The recent pandemic created due to Novel Coronavirus (nCOV-2019) from Wuhan, China demanding a large scale of a general health emergency. This demands novel research on the vaccine to fight against this pandemic situation, re-purposing of the existing drugs, phylogenetic analysis to identify the origin and determine the similarity with other known viruses, etc. The very preliminary task from the research community is to analyze the wide verities of existing related research articles, which is very much time-consuming in such situations where each minute counts for saving hundreds of human lives. The entire manual processing is even lower down the efficiency in mining the information. We have developed a complete automatic literature mining system that delivers efficient and fast mining from existing biomedical literature databases. With the help of modern-day deep learning algorithms, our system also delivers a summarization of important research articles that provides ease and fast comprehension of critical research articles. The system is currently scanning nearly 1,46,115,136 English words from 29,315 research articles in not greater than 1.5 seconds with multiple search keywords. Our research article presents the criticality of literature mining, especially in pandemic situations with the implementation and online deployment of the system.

title: Transmission dynamics and evolutionary history of 2019‐nCoV
abstract: To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019‐nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogenetic, transmission network, and likelihood‐mapping analyses of the genome sequences were performed. On the basis of the likelihood‐mapping analysis, the increasing tree‐like signals (from 0% to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We identified three phylogenetic clusters using the Bayesian inference framework and three transmission clusters using transmission network analysis, with only one cluster identified by both methods using the above genome sequences of 2019‐nCoV strains. The estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10(−3) to 1.8266 × 10(−3) substitutions per site per year. On the basis of our study, undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real‐time.

title: Using imaging to combat a pandemic: rationale for developing the UK National COVID-19 Chest Imaging Database
abstract: The National COVID-19 Chest Imaging Database (NCCID) is a repository of chest X-Ray, CT and MRI images and clinical data from COVID-19 patients across the UK, to support research and development of AI technology that may proffer insights into the disease.

title: Effect of mutations in the mouse hepatitis virus 3'(+)42 protein binding element on RNA replication.
abstract: The mouse hepatitis virus (MHV) genome's 3' untranslated region contains cis-acting sequences necessary for replication. Studies of MHV and other coronaviruses have indicated a role for RNA secondary and tertiary elements in replication. Previous work in our laboratory has identified four proteins which form ribonucleoprotein complexes with the 3'-terminal 42 nucleotides [3'(+)42] of the MHV genome. Defective interfering (DI) RNA replication assays have demonstrated a role for the 3'(+)42 host protein binding element in the MHV life cycle. Using gel mobility shift RNase T1 protection assays and secondary structure modeling, we have characterized a possible role for RNA secondary structure in host protein binding to the 3'-terminal 42-nucleotide element. Additionally we have identified a role for the 3'-terminal 42-nucleotide host protein binding element in RNA replication and transcription using DI RNA replication assays and targeted recombination and by directly constructing mutants in this protein binding element using a recently described MHV reverse genetic system. DI RNA replication assays demonstrated that mutations in the 3'(+)42 host protein binding element had a deleterious effect on the accumulation of DI RNA. When the identical mutations were directly inserted into the MHV genome, most mutant genomes were viable but formed smaller plaques than the wild-type parent virus. One mutant was not viable. This mutant directed the synthesis of genome-sized negative-sense RNA approximately as efficiently as the wild type did but had a defect in subgenomic mRNA synthesis. These results point to a potential role for sequences at the extreme 3' end of the MHV genome in subgenomic RNA synthesis.

title: Current efforts and challenges facing responses to 2019-nCoV in Africa
abstract: The novel coronavirus is a pandemic that has started to creep into Africa thus making the virus a truly global, health security threat. The number of new 2019-nCoV cases has been rising in Africa, though currently lower than the cases reported outside the region. African countries have activated their Emergency Operations Centres to coordinate responses and preparedness activities to the pandemic. A series of measures such as restricting travel, case detection and contact tracing, mandatory quarantine, guidance and information to the public among other efforts are being implemented across Africa. However, the presence of porous borders, the double burden of communicable and non-communicable diseases, poverty, poor health literacy, infodemic and family clustering, and most of all, weak health systems, may make containment challenging. It is important for African countries to continue to intensify efforts and address the challenges to effectively respond to the uncertainty the pandemic poses.

title: TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic
abstract: Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning, NLP and information retrieval methods. With respect to the recent outbreak of COVID-19, online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact. Understanding such discourse, its evolution and interdependencies with real-world events or (mis)information can foster valuable insights. On the other hand, such corpora are crucial facilitators for computational methods addressing tasks such as sentiment analysis, event detection or entity recognition. However, obtaining, archiving and semantically annotating large amounts of tweets is costly. In this paper, we describe TweetsCOV19, a publicly available knowledge base of currently more than 8 million tweets, spanning the period Oct'19-Apr'20. Metadata about the tweets as well as extracted entities, hashtags, user mentions, sentiments, and URLs are exposed using established RDF/S vocabularies, providing an unprecedented knowledge base for a range of knowledge discovery tasks. Next to a description of the dataset and its extraction and annotation process, we present an initial analysis, use cases and usage of the corpus.

title: Collective response to the media coverage of COVID-19 Pandemic on Reddit and Wikipedia
abstract: The exposure and consumption of information during epidemic outbreaks may alter risk perception, trigger behavioural changes, and ultimately affect the evolution of the disease. It is thus of the uttermost importance to map information dissemination by mainstream media outlets and public response. However, our understanding of this exposure-response dynamic during COVID-19 pandemic is still limited. In this paper, we provide a characterization of media coverage and online collective attention to COVID-19 pandemic in four countries: Italy, United Kingdom, United States, and Canada. For this purpose, we collect an heterogeneous dataset including 227,768 online news articles and 13,448 Youtube videos published by mainstream media, 107,898 users posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views to COVID-19 related Wikipedia pages. Our results show that public attention, quantified as users activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage and declines rapidly, while news exposure and COVID-19 incidence remain high. Furthermore, by using an unsupervised, dynamical topic modeling approach, we show that while the attention dedicated to different topics by media and online users are in good accordance, interesting deviations emerge in their temporal patterns. Overall, our findings offer an additional key to interpret public perception/response to the current global health emergency and raise questions about the effects of attention saturation on collective awareness, risk perception and thus on tendencies towards behavioural changes.

title: A Survey of Social Web Mining Applications for Disease Outbreak Detection
abstract: Social Web Media is one of the most important sources of big data to extract and acquire new knowledge. Social Networks have become an important environment where users provide information of their preferences and relationships. This information can be used to measure the influence of ideas and the society opinions in real time, being very useful on several fields and research areas such as marketing campaigns, financial prediction or public healthcare among others. Recently, the research on artificial intelligence techniques applied to develop technologies allowing monitoring web data sources for detecting public health events has emerged as a new relevant discipline called Epidemic Intelligence. Epidemic Intelligence Systems are nowadays widely used by public health organizations like monitoring mechanisms for early detection of disease outbreaks to reduce the impact of epidemics. This paper presents a survey on current data mining applications and web systems based on web data for public healthcare over the last years. It tries to take special attention to machine learning and data mining techniques and how they have been applied to these web data to extract collective knowledge from Twitter.

title: Open data in a deeply connected world

title: Pangolin homology associated with 2019-nCoV
abstract: To explore potential intermediate host of a novel coronavirus is vital to rapidly control continuous COVID-19 spread. We found genomic and evolutionary evidences of the occurrence of 2019-nCoV-like coronavirus (named as Pangolin-CoV) from dead Malayan Pangolins. Pangolin-CoV is 91.02% and 90.55% identical at the whole genome level to 2019-nCoV and BatCoV RaTG13, respectively. Pangolin-CoV is the lowest common ancestor of 2019-nCoV and RaTG13. The S1 protein of Pangolin-CoV is much more closely related to 2019-nCoV than RaTG13. Five key amino-acid residues involved in the interaction with human ACE2 are completely consistent between Pangolin-CoV and 2019-nCoV but four amino-acid mutations occur in RaTG13. It indicates Pangolin-CoV has similar pathogenic potential to 2019-nCoV, and would be helpful to trace the origin and probable intermediate host of 2019-nCoV.

title: Coronavirus (COVID-19): A Review of Clinical Features, Diagnosis, and Treatment
abstract: Coronavirus (COVID-19) is an enveloped RNA virus that is diversely found in humans and wildlife. A total of six species have been identified to cause disease in humans. They are known to infect the neurological, respiratory, enteric, and hepatic systems. The past few decades have seen endemic outbreaks in the form of Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome related coronavirus (SARS-CoV). Yet again, we see the emergence of another outbreak due to a new strain called the SARS-CoV-2 virus. The most recent outbreak initially presented as pneumonia of unknown etiology in a cluster of patients in Wuhan, China. The epicenter of infection was linked to seafood and exotic animal wholesale markets in the city. SARS-CoV-2 is highly contagious and has resulted in a rapid pandemic of COVID-19. As the number of cases continues to rise, it is clear that these viruses pose a threat to public health. This review will introduce a general overview of coronavirus and describe the clinical features, evaluation, and treatment of COVID-19 patients. It will also provide a means to raise awareness among primary and secondary healthcare providers during the current pandemic. Furthermore, our review focuses on the most up-to-date clinical information for the effective management, prevention, and counseling of patients worldwide.

title: Early in the epidemic: impact of preprints on global discourse about COVID-19 transmissibility

title: Online Information Search During COVID-19
abstract: Public information search data from sources such as Google Trends affords researchers a perspective on what society does not know, or what society wants to find out prompted by, or in response to, developments in societal communication and news media events. In times of crisis, online public information search thus offers a window into the most urgent concerns of, and demands by society. By extension, such data offers a window into upcoming, and currently pressing demands on businesses, policymakers and researchers. This research note aims to illustrate some of these themes with a visual-based overview of the current COVID-19 / Coronavirus crisis.

title: Strong effect of socioeconomic levels on the spread and treatment of the 2019 novel coronavirus (COVID-19) in China
abstract: Global response to the COVID-19 epidemic presents strengths and weaknesses in national and regional social governance capacities to address public health challenges. The emergence, detection, spread, treatment and containment of infectious diseases shows the considerable political and economic impacts in a highly interconnected world. We aimed to estimate the effects of socioeconomic levels on the spread and treatment of COVID-19 in China.  We obtained daily COVID-19 cases at a city level in China. We used migration data from the major cities in Hubei Province, and macroeconomic data at city and province levels. We obtained social management measures in response to COVID-19 outbreak. We assessed the association between measures, migration and COVID-19 spread, and the association between socioeconomic levels and COVID-19 treatment capacity.  On January 1, 2020, COVID-19 spread that affected by management measures and migration started across China. After Wuhan lockdown, the case number reached peak in 12 days, and COVID-19 outbreak was basically contained in China in four weeks due to intensive measures. Guangdong, Jiangsu and Zhejiang Provinces showed the most excellent COVID-19 treatment capacities. Socioeconomic levels in these provinces ranked top in China. Guangdong achieved the largest decline in severe case rate by 22.1%. Jiangsu had the lowest average rate of severe cases (1.7%) and zero death. Among the regions with top case number, Zhejiang showed the highest rate of cured cases on confirmed cases (96.3%), the lowest average rate of severe cases (7.7%), and one death. The COVID-19 treatment capacities were strongly affected by regional economics and measures on control, detection and treatment.  Socioeconomic levels had strong effect on the spread and treatment of COVID-19 in China. Further investigations are needed on the effectiveness of Chinese measures and the effects of socioeconomic levels on COVID-19 treatment outside China. Fund Guangdong Natural Science Foundation

title: How related is SARS-CoV-2 to other coronaviruses?

title: Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II): protocol for an observational study using linked Scottish national data
abstract: Following the emergence of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019 and the ensuing COVID-19 pandemic, population-level surveillance and rapid assessment of the effectiveness of existing or new therapeutic or preventive interventions are required to ensure that interventions are targeted to those at highest risk of serious illness or death from COVID-19. We aim to repurpose and expand an existing pandemic reporting platform to determine the attack rate of SARS-CoV-2, the uptake and effectiveness of any new pandemic vaccine (once available) and any protective effect conferred by existing or new antimicrobial drugs and other therapies.  A prospective observational cohort will be used to monitor daily/weekly the progress of the COVID-19 epidemic and to evaluate the effectiveness of therapeutic interventions in approximately 5.4 million individuals registered in general practices across Scotland. A national linked dataset of patient-level primary care data, out-of-hours, hospitalisation, mortality and laboratory data will be assembled. The primary outcomes will measure association between: (A) laboratory confirmed SARS-CoV-2 infection, morbidity and mortality, and demographic, socioeconomic and clinical population characteristics; and (B) healthcare burden of COVID-19 and demographic, socioeconomic and clinical population characteristics. The secondary outcomes will estimate: (A) the uptake (for vaccines only); (B) effectiveness; and (C) safety of new or existing therapies, vaccines and antimicrobials against SARS-CoV-2 infection. The association between population characteristics and primary outcomes will be assessed via multivariate logistic regression models. The effectiveness of therapies, vaccines and antimicrobials will be assessed from time-dependent Cox models or Poisson regression models. Self-controlled study designs will be explored to estimate the risk of therapeutic and prophylactic-related adverse events.  We obtained approval from the National Research Ethics Service Committee, Southeast Scotland 02. The study findings will be presented at international conferences and published in peer-reviewed journals.

title: Unraveling the web of viroinformatics: computational tools and databases in virus research.
abstract: The beginning of the second century of research in the field of virology (the first virus was discovered in 1898) was marked by its amalgamation with bioinformatics, resulting in the birth of a new domain--viroinformatics. The availability of more than 100 Web servers and databases embracing all or specific viruses (for example, dengue virus, influenza virus, hepatitis virus, human immunodeficiency virus [HIV], hemorrhagic fever virus [HFV], human papillomavirus [HPV], West Nile virus, etc.) as well as distinct applications (comparative/diversity analysis, viral recombination, small interfering RNA [siRNA]/short hairpin RNA [shRNA]/microRNA [miRNA] studies, RNA folding, protein-protein interaction, structural analysis, and phylotyping and genotyping) will definitely aid the development of effective drugs and vaccines. However, information about their access and utility is not available at any single source or on any single platform. Therefore, a compendium of various computational tools and resources dedicated specifically to virology is presented in this article.

title: Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases
abstract: The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.

title: Analysis of SARS-CoV-2 Antibodies in COVID-19 Convalescent Plasma using a Coronavirus Antigen Microarray
abstract: The current practice for diagnosis of COVID-19, based on SARS-CoV-2 PCR testing of pharyngeal or respiratory specimens in a symptomatic patient at high epidemiologic risk, likely underestimates the true prevalence of infection. Serologic methods can more accurately estimate the disease burden by detecting infections missed by the limited testing performed to date. Here, we describe the validation of a coronavirus antigen microarray containing immunologically significant antigens from SARS-CoV-2, in addition to SARS-CoV, MERS-CoV, common human coronavirus strains, and other common respiratory viruses. A comparison of antibody profiles detected on the array from control sera collected prior to the SARS-CoV-2 pandemic versus convalescent blood specimens from virologically confirmed COVID-19 cases demonstrates complete discrimination of these two groups. This array can be used as a diagnostic tool, as an epidemiologic tool to more accurately estimate the disease burden of COVID-19, and as a research tool to correlate antibody responses with clinical outcomes.

title: Serial interval in determining the estimation of reproduction number of the novel coronavirus disease (COVID-19) during the early outbreak

title: Can orthodontic care be safely delivered during the COVID-19 pandemic? Recommendations from a literature review
abstract: Five electronic databases were searched: COVID-19 Open Research Dataset (CORD-19-2020); PubMed; MEDLINE; Scopus; and Google Scholar. Study selection Titles of articles and abstracts were identified during the electronic database searches and then screened for relevance. Publications up until the date of the literature search, 19th March 2020, were used. All articles with the appropriate topics pertaining to COVID-19, dentistry, orthodontics, and infection control were used irrespective of language. The author did not state whether they were selective about the study type or design of articles screened. References of these articles were also screened, via the 'snowballing technique', to obtain as much relevant literature as possible. Data extraction and synthesis Articles were reviewed by the cited author, and one research assistant. Data was extracted from each study by this author. The data obtained was combined and discussed narratively, in a qualitative manner. Due to the broad scope of studies included, it was not possible to conduct a meta-analysis.  This literature review describes ways to reduce COVID-19 transmission in orthodontic practice. Overview of the literature discusses how the virus may be transmitted in the orthodontic setting: by human-human contact; saliva; aerosols; and use of orthodontic instruments. The literature review illustrates the need for optimum infection control and strict cleaning, detailing surface disinfection and sterilisation protocols. It highlights the need for optimal hand hygiene, use of high standard personal protective equipment, controlling aerosol use, appropriate ventilation, and treating emergency cases only.  While there are no known cases of COVID-19 cross-transmission within the dental setting currently reported, utmost vigilance is required by orthodontic professionals to reduce risk of transmission. The review reinforces crucial measures required to reduce infection, as outlined in the Results section above. While the virus is still emerging, knowledge is limited and as such it is difficult to provide robust and complete recommendations for best practice. Further studies to inform future practice are required.

title: Flattening the curve before it flattens us: hospital critical care capacity limits and mortality from novel coronavirus (SARS-CoV2) cases in US counties
abstract: As of March 26, 2020, the United States had the highest number of confirmed cases of Novel Coronavirus (COVID-19) of any country in the world. Hospital critical care is perhaps the most important medical system choke point in terms of preventing deaths in a disaster scenario such as the current COVID-19 pandemic. We therefore brought together previously established disease modeling estimates of the growth of the COVID-19 epidemic in the US under various social distancing contact reduction assumptions, with local estimates of the potential critical care surge response across all US counties.  Estimates of spatio-temporal COVID-19 demand and medical system critical care supply were calculated for all continental US counties. These estimates were statistically summarized and mapped for US counties, regions and urban versus non-urban areas. Estimates of COVID-19 infections and patients needing critical care were calculated from March 24, 2020 to April 24, 2020 for three different estimated population levels - 0%, 25%, and 50% - of contact reduction (through actions such as social distancing). Multiple national public and private datasets were linked and harmonized in order to calculate county-level critical care bed counts that included currently available beds and those that could be made available under four surge response scenarios - very low, low, medium, and high - as well as excess deaths stemming from inaccessible critical care.  Surge response scenarios ranged from a very low total supply 77,588 critical care beds to a high total of 278,850 critical care beds. Over the four week study period, excess deaths from inaccessible critical care ranged from 24,688 in the very low response scenario to 13,268 in the high response scenario. Northeastern and urban counties were projected to be most affected by excess deaths due to critical care shortages, and counties in New York, Colorado, and Virginia were projected to exceed their critical care bed limits despite high levels of COVID-19 contact reduction. Over the four week study period, an estimated 12,203-19,594 excess deaths stemming from inaccessible critical care could be averted through greater preventive actions such as travel restrictions, publicly imposed contact precautions, greater availability of rapid testing for COVID-19, social distancing, self-isolation when sick, and similar interventions. An estimated 4,029-11,420 excess deaths stemming from inaccessible critical care could be averted through aggressive critical care surge response and preparations, including high clearance of ICU and non-ICU critical care beds and extraordinary measures like using a single ventilator for multiple patients.  Unless the epidemic curve of COVID-19 cases is flattened over an extended period of time, the US COVID-19 epidemic will cause a shortage of critical care beds and drive up otherwise preventable deaths. The findings here support value of preventive actions to flatten the epidemic curve, as well as the value of exercising extraordinary surge capacity measures to increase access to hospital critical care for severely ill COVID-19 patients.

title: Rapid Detection of COVID-19 Causative Virus (SARS-CoV-2) in Human Nasopharyngeal Swab Specimens Using Field-Effect Transistor-Based Biosensor
abstract: see text] Coronavirus disease 2019 (COVID-19) is a newly emerging human infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously called 2019-nCoV). Based on the rapid increase in the rate of human infection, the World Health Organization (WHO) has classified the COVID-19 outbreak as a pandemic. Because no specific drugs or vaccines for COVID-19 are yet available, early diagnosis and management are crucial for containing the outbreak. Here, we report a field-effect transistor (FET)-based biosensing device for detecting SARS-CoV-2 in clinical samples. The sensor was produced by coating graphene sheets of the FET with a specific antibody against SARS-CoV-2 spike protein. The performance of the sensor was determined using antigen protein, cultured virus, and nasopharyngeal swab specimens from COVID-19 patients. Our FET device could detect the SARS-CoV-2 spike protein at concentrations of 1 fg/mL in phosphate-buffered saline and 100 fg/mL clinical transport medium. In addition, the FET sensor successfully detected SARS-CoV-2 in culture medium (limit of detection [LOD]: 1.6 × 10(1) pfu/mL) and clinical samples (LOD: 2.42 × 10(2) copies/mL). Thus, we have successfully fabricated a promising FET biosensor for SARS-CoV-2; our device is a highly sensitive immunological diagnostic method for COVID-19 that requires no sample pretreatment or labeling.

title: A Machine Learning Solution Framework for Combatting COVID-19 in Smart Cities from Multiple Dimensions
abstract: The spread of COVID-19 across the world continue as efforts are been made from multi-dimension to curtail it is spread and provide treatment. The COVID-19 triggered partial and full lockdown across the globe in an effort to prevent it is spread. The COVID-19 causes serious fatalities with United States of America recording over 3,000 deaths within 24 hours, the highest in the world for a single day. In this paper, we propose a framework integrated with machine learning to curtail the spread of COVID-19 in smart cities. A novel mathematical model is created to show the spread of the COVID-19 in smart cities. The propose solution framework can generate, capture, store and analyze data using machine learning algorithms to detect, prevent the spread of COVID-19, forecast next epidemic, effective contact tracing, diagnose cases, monitor COVID-19 patient, COVID-19 vaccine development, track potential COVID-19 patients, COVID-19 drug discovery and provide better understand of the virus in smart cities. The study outlined case studies on the application of machine learning to help in the fight against COVID-19 in hospitals in smart cities across the world. The framework can provide guide for real world execution in smart cities. The propose framework has the potential for helping national healthcare systems in curtailing the COVID-19 pandemic in smart cities.

title: Studying the pathophysiology of coronavirus disease 2019 - a protocol for the Berlin prospective COVID-19 patient cohort (Pa- COVID-19)
abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide causing a global health emergency. Pa-COVID-19 aims to provide comprehensive data on clinical course, pathophysiology, immunology and outcome of COVID-19, in order to identify prognostic biomarkers, clinical scores, and therapeutic targets for improved clinical management and preventive interventions.  Pa-COVID-19 is a prospective observational cohort study of patients with confirmed SARS-CoV-2 infection treated at Charite - Universitaetsmedizin Berlin. We collect data on epidemiology, demography, medical history, symptoms, clinical course, pathogen testing and treatment. Systematic, serial blood sampling will allow deep molecular and immunological phenotyping, transcriptomic profiling, and comprehensive biobanking. Longitudinal data and sample collection during hospitalization will be supplemented by long-term follow-up.  Outcome measures include the WHO clinical ordinal scale on day 15 and clinical, functional and health-related quality of life assessments at discharge and during follow-up. We developed a scalable dataset to (i) suit national standards of care (ii) facilitate comprehensive data collection in medical care facilities with varying resources and (iii) allow for rapid implementation of interventional trials based on the standardized study design and data collection. We propose this scalable protocol as blueprint for harmonized data collection and deep phenotyping in COVID-19 in Germany.  We established a basic platform for harmonized, scalable data collection, pathophysiological analysis, and deep phenotyping of COVID-19, which enables rapid generation of evidence for improved medical care and identification of candidate therapeutic and preventive strategies. The electronic database accredited for interventional trials allows fast trial implementation for candidate therapeutic agents.

title: Facemask shortage and the novel coronavirus disease (COVID-19) outbreak: Reflections on public health measures
abstract: A novel coronavirus disease (COVID-19) outbreak due to the severe respiratory syndrome coronavirus (SARS-CoV-2) infection occurred in China in late December 2019. Facemask wearing with proper hand hygiene is considered an effective measure to prevent SARS-CoV-2 transmission, but facemask wearing has become a social concern due to the global facemask shortage. China is the major facemask producer in the world, contributing to 50% of global production. However, a universal facemask wearing policy would put an enormous burden on the facemask supply.  We performed a policy review concerning facemasks using government websites and mathematical modelling shortage analyses based on data obtained from the National Health Commission (NHC), the Ministry of Industry and Information Technology (MIIT), the Centre for Disease Control and Prevention (CDC), and General Administration of Customs (GAC) of the People's Republic of China. Three scenarios with respect to wearing facemasks were considered: (1) a universal facemask wearing policy implementation in all regions of mainland China; (2) a universal facemask wearing policy implementation only in the epicentre (Hubei province, China); and (3) no implementation of a universal facemask wearing policy.  Regardless of different universal facemask wearing policy scenarios, facemask shortage would occur but eventually end during our prediction period (from 20 Jan 2020 to 30 Jun 2020). The duration of the facemask shortage described in the scenarios of a country-wide universal facemask wearing policy, a universal facemask wearing policy in the epicentre, and no universal facemask wearing policy were 132, seven, and four days, respectively. During the prediction period, the largest daily facemask shortages were predicted to be 589·5, 49·3, and 37·5 million in each of the three scenarios, respectively. In any scenario, an N95 mask shortage was predicted to occur on 24 January 2020 with a daily facemask shortage of 2·2 million.  Implementing a universal facemask wearing policy in the whole of China could lead to severe facemask shortage. Without effective public communication, a universal facemask wearing policy could result in societal panic and subsequently, increase the nationwide and worldwide demand for facemasks. These increased demands could cause a facemask shortage for healthcare workers and reduce the effectiveness of outbreak control in the affected regions, eventually leading to a pandemic. To fight novel infectious disease outbreaks, such as COVID-19, governments should monitor domestic facemask supplies and give priority to healthcare workers. The risk of asymptomatic transmission and facemask shortages should be carefully evaluated before introducing a universal facemask wearing policy in high-risk regions. Public health measures aimed at improving hand hygiene and effective public communication should be considered along with the facemask policy.

title: Dynamic change process of target genes by RT-PCR testing of SARS-Cov-2 during the course of a Coronavirus Disease 2019 patient
abstract: We report the dynamic change process of target genes by RT-PCR testing of SARS-Cov-2 during the course of a COVID-19 patient: from successive negative results to successive single positive nucleocapsid gene, to two positive target genes (orf1ab and nucleocapsid) by RT-PCR testing of SARS-Cov-2, and describe the diagnosis, clinical course, and management of the case. In this case, negative results of RT-PCR testing was not excluded to diagnose a suspected COVID-19 patient, clinical signs and symptoms, other laboratory findings, and chest CT images should be taken into account for the absence of enough positive evidence. This case highlights the importance of successive sampling and testing SARS-Cov-2 by RT-PCR as well as the increased value of single positive target gene from pending to positive in two specimens to diagnose laboratory-confirmed COVID-19.

title: Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis
abstract: The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting 201 countries and territories around the globe. As of April 4, 2020, it has caused a pandemic outbreak with more than 11,16,643 confirmed infections and more than 59,170 reported deaths worldwide. The main focus of this paper is two-fold: (a) generating short term (real-time) forecasts of the future COVID-19 cases for multiple countries; (b) risk assessment (in terms of case fatality rate) of the novel COVID-19 for some profoundly affected countries by finding various important demographic characteristics of the countries along with some disease characteristics. To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. The predictions of the future outbreak for different countries will be useful for the effective allocation of health care resources and will act as an early-warning system for government policymakers. In the second problem, we applied an optimal regression tree algorithm to find essential causal variables that significantly affect the case fatality rates for different countries. This data-driven analysis will necessarily provide deep insights into the study of early risk assessments for 50 immensely affected countries.

title: Identification of a novel coronavirus causing severe pneumonia in human: a descriptive study
abstract: Human infections with zoonotic coronaviruses (CoVs), including severe acute respiratory syndrome (SARS)-CoV and Middle East respiratory syndrome (MERS)-CoV, have raised great public health concern globally. Here, we report a novel bat-origin CoV causing severe and fatal pneumonia in humans.  We collected clinical data and bronchoalveolar lavage (BAL) specimens from five patients with severe pneumonia from Jin Yin-tan Hospital of Wuhan, Hubei province, China. Nucleic acids of the BAL were extracted and subjected to next-generation sequencing. Virus isolation was carried out, and maximum-likelihood phylogenetic trees were constructed.  Five patients hospitalized from December 18 to December 29, 2019 presented with fever, cough, and dyspnea accompanied by complications of acute respiratory distress syndrome. Chest radiography revealed diffuse opacities and consolidation. One of these patients died. Sequence results revealed the presence of a previously unknown β-CoV strain in all five patients, with 99.8% to 99.9% nucleotide identities among the isolates. These isolates showed 79.0% nucleotide identity with the sequence of SARS-CoV (GenBank NC_004718) and 51.8% identity with the sequence of MERS-CoV (GenBank NC_019843). The virus is phylogenetically closest to a bat SARS-like CoV (SL-ZC45, GenBank MG772933) with 87.6% to 87.7% nucleotide identity, but is in a separate clade. Moreover, these viruses have a single intact open reading frame gene 8, as a further indicator of bat-origin CoVs. However, the amino acid sequence of the tentative receptor-binding domain resembles that of SARS-CoV, indicating that these viruses might use the same receptor.  A novel bat-borne CoV was identified that is associated with severe and fatal respiratory disease in humans.

title: Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in the United States
abstract: The novel coronavirus SARS-CoV-2 was first detected in the Pacific Northwest region of the United States in January 2020, with subsequent COVID-19 outbreaks detected in all 50 states by early March. To uncover the sources of SARS-CoV-2 introductions and patterns of spread within the United States, we sequenced nine viral genomes from early reported COVID-19 patients in Connecticut. Our phylogenetic analysis places the majority of these genomes with viruses sequenced from Washington state. By coupling our genomic data with domestic and international travel patterns, we show that early SARS-CoV-2 transmission in Connecticut was likely driven by domestic introductions. Moreover, the risk of domestic importation to Connecticut exceeded that of international importation by mid-March regardless of our estimated effects of federal travel restrictions. This study provides evidence of widespread sustained transmission of SARS-CoV-2 within the United States and highlights the critical need for local surveillance.

title: Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia
abstract: Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) – the causal agent in COVID-19 – affects olfaction directly by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing revealed that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing and immunostaining demonstrated ACE2 expression in support cells, stem cells, and perivascular cells; in contrast, neurons in both the olfactory epithelium and bulb did not express ACE2 message or protein. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.

title: Regional Planning for Extracorporeal Membrane Oxygenation Allocation During Coronavirus Disease 2019
abstract: Health systems confronting the coronavirus disease 2019 (COVID-19) pandemic must plan for surges in ICU demand and equitably distribute resources to maximize benefit for critically ill patients and the public during periods of resource scarcity. For example, morbidity and mortality could be mitigated by a proactive regional plan for the triage of mechanical ventilators. Extracorporeal membrane oxygenation (ECMO), a resource-intensive and potentially life-saving modality in severe respiratory failure, has generally not been included in proactive disaster preparedness until recently. This paper explores underlying assumptions and triage principles that could guide the integration of ECMO resources into existing disaster planning. Drawing from a collaborative framework developed by one US metropolitan area with multiple adult and pediatric extracorporeal life support centers, this paper aims to inform decision-making around ECMO use during a pandemic such as COVID-19. It also addresses the ethical and practical aspects of not continuing to offer ECMO during a disaster.

title: Exploring Epidemiological Behavior of Novel Coronavirus Outbreak through the Development and Analysis of COVID-19 Daily Dataset in Bangladesh
abstract: Globally, there is an obvious concern about the fact that the evolving 2019-nCoV coronavirus is a worldwide public health threat. The appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China at the end of 2019 triggered a major global epidemic, which is now a major community health issue. As of April 17, 2020, according to Institute of Epidemiology, Disease Control and Research (IEDCR) Bangladesh has reported 1838 confirmed cases in between 8 March to 17 April 2020, with > 4.08% of mortality rate and >3.15% of recovery rate. COVID-19 outbreak is evolving so rapidly in Bangladesh; therefore, the availability of epidemiological data and its sensible analysis are essential to direct strategies for situational awareness and intervention. This article presents an exploratory data analysis approach to collect and analyze COVID-19 data on epidemiological outbreaks based on first publicly available COVID-19 Daily Dataset of Bangladesh. Various publicly open data sources on the outbreak of COVID-19 provided by the IEDCR, World Health Organization (WHO), Directorate General of Health Services (DGHS), and Ministry of Health and Family Welfare (MHFW) of Bangladesh have been used in this research. A Visual Exploratory Data Analysis (V-EDA) techniques have been followed in this research to understand the epidemiological characteristics of COVID-19 outbreak in different districts of Bangladesh in between 8 March 2020 to 12 April 2020 and these findings were compared with those of other countries. In all, this is extremely important to promptly spread information to understand the risks of this pandemic and begin containment activities in the country.

title: Phase-adjusted estimation of the number of Coronavirus Disease 2019 cases in Wuhan, China
abstract: An outbreak of clusters of viral pneumonia due to a novel coronavirus (2019-nCoV/SARS-CoV-2) happened in Wuhan, Hubei Province in China in December 2019. Since the outbreak, several groups reported estimated R(0) of Coronavirus Disease 2019 (COVID-19) and generated valuable prediction for the early phase of this outbreak. After implementation of strict prevention and control measures in China, new estimation is needed. An infectious disease dynamics SEIR (Susceptible, Exposed, Infectious, and Removed) model was applied to estimate the epidemic trend in Wuhan, China under two assumptions of R(t). In the first assumption, R(t) was assumed to maintain over 1. The estimated number of infections would continue to increase throughout February without any indication of dropping with R(t) = 1.9, 2.6, or 3.1. The number of infections would reach 11,044, 70,258, and 227,989, respectively, by 29 February 2020. In the second assumption, R(t) was assumed to gradually decrease at different phases from high level of transmission (R(t) = 3.1, 2.6, and 1.9) to below 1 (R(t) = 0.9 or 0.5) owing to increasingly implemented public health intervention. Several phases were divided by the dates when various levels of prevention and control measures were taken in effect in Wuhan. The estimated number of infections would reach the peak in late February, which is 58,077–84,520 or 55,869–81,393. Whether or not the peak of the number of infections would occur in February 2020 may be an important index for evaluating the sufficiency of the current measures taken in China. Regardless of the occurrence of the peak, the currently strict measures in Wuhan should be continuously implemented and necessary strict public health measures should be applied in other locations in China with high number of COVID-19 cases, in order to reduce R(t) to an ideal level and control the infection.

title: CompRes: A Dataset for Narrative Structure in News
abstract: This paper addresses the task of automatically detecting narrative structures in raw texts. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to news articles, motivated by their growing social impact as well as their role in creating and shaping public opinion. We introduce CompRes -- the first dataset for narrative structure in news media. We describe the process in which the dataset was constructed: first, we designed a new narrative annotation scheme, better suited for news media, by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success); then, we used that scheme to annotate a set of 29 English news articles (containing 1,099 sentences) collected from news and partisan websites. We use the annotated dataset to train several supervised models to identify the different narrative elements, achieving an $F_1$ score of up to 0.7. We conclude by suggesting several promising directions for future work.

title: Mining Coronavirus (COVID-19) Posts in Social Media
abstract: World Health Organization (WHO) characterized the novel coronavirus (COVID-19) as a global pandemic on March 11th, 2020. Before this and in late January, more specifically on January 27th, while the majority of the infection cases were still reported in China and a few cruise ships, we began crawling social media user postings using the Twitter search API. Our goal was to leverage machine learning and linguistic tools to better understand the impact of the outbreak in China. Unlike our initial expectation to monitor a local outbreak, COVID-19 rapidly spread across the globe. In this short article we report the preliminary results of our study on automatically detecting the positive reports of COVID-19 from social media user postings using state-of-the-art machine learning models.

title: Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019‐nCoV
abstract: To estimate the basic reproduction number of the Wuhan novel coronavirus (2019‐nCoV).  Based on the susceptible‐exposed‐infected‐removed (SEIR) compartment model and the assumption that the infectious cases with symptoms occurred before 26 January, 2020 are resulted from free propagation without intervention, we estimate the basic reproduction number of 2019‐nCoV according to the reported confirmed cases and suspected cases, as well as the theoretical estimated number of infected cases by other research teams, together with some epidemiological determinants learned from the severe acute respiratory syndrome (SARS).  The basic reproduction number fall between 2.8 and 3.3 by using the real‐time reports on the number of 2019‐nCoV‐infected cases from People's Daily in China and fall between 3.2 and 3.9 on the basis of the predicted number of infected cases from international colleagues.  The early transmission ability of 2019‐nCoV is close to or slightly higher than SARS. It is a controllable disease with moderate to high transmissibility. Timely and effective control measures are needed to prevent the further transmissions.

title: Prediction of SARS-CoV-2 epitopes across 9360 HLA class I alleles
abstract: Elucidating antiviral CD8 T lymphocyte responses to SARS-CoV-2 may shed light on the heterogeneity of clinical outcomes and inform vaccine or therapeutic approaches. To facilitate the evaluation of antiviral CD8 T cell responses to SARS-CoV-2, we generated a publicly accessible database of epitopes predicted to bind any class I HLA protein across the entire SARS-CoV-2 proteome. While a subset of epitopes from earlier betacoronaviruses, such as SARS-CoV (SARS), have been validated experimentally, validation systems are often biased toward specific HLA haplotypes (notably HLA-A*02:01) that only account for a fraction of the haplotypes of individuals affected by the SARS-CoV-2 pandemic. To enable evaluation of epitopes across individuals with a variety of HLA haplotypes, we computed the predicted binding affinities between 9-mer peptides derived from the annotated SARS-CoV-2 peptidome across 9,360 MHC class I HLA-A, -B, and -C alleles. There were 6,748 unique combinations of peptides and HLA alleles (pMHCs) with a predicted binding affinity of less than 500nM, including 1,103 unique peptides and 1,022 HLA alleles, spanning 11 annotated superfamilies. These peptides were derived from all 11 proteins spanning the SARS-CoV-2 peptidome, including peptides that have previously been validated experimentally. We also show evidence that these previously validated epitopes may be relevant in other HLA contexts. This complete dataset is available publicly: gs://pici-covid19-data-resources/mhci/peptide_predictions.

title: COVID-19, a worldwide public health emergency
abstract: A new coronavirus outbreak emerged on the 31st of December 2019 in Wuhan, China, causing commotion among the medical community and the rest of the world. This new species of coronavirus has been termed 2019-nCoV and has caused a considerable number of cases of infection and deaths in China and, to a growing degree, beyond China, becoming a worldwide public health emergency. 2019-nCoV has high homology to other pathogenic coronaviruses, such as those originating from bat-related zoonosis (SARS-CoV), which caused approximately 646 deaths in China at the start of the decade. The mortality rate for 2019-nCoV is not as high (approximately 2–3%), but its rapid propagation has resulted in the activation of protocols to stop its spread. This pathogen has the potential to become a pandemic. It is therefore vital to follow the personal care recommendations issued by the World Health Organization.

title: A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates
abstract: An important unknown during the COVID-19 pandemic has been the infection-fatality rate (IFR). This differs from the case-fatality rate (CFR) as an estimate of the number of deaths as a proportion of the total number of cases, including those who are mild and asymptomatic. While the CFR is extremely valuable for experts, IFR is increasingly being called for by policy-makers and the lay public as an estimate of the overall mortality from COVID-19.  Pubmed and Medrxiv were searched using a set of terms and Boolean operators on 25/04/2020. Articles were screened for inclusion by both authors. Meta-analysis was performed in Stata 15.1 using the metan command, based on IFR and confidence intervals extracted from each study. Google/Google Scholar was used to assess the grey literature relating to government reports.  After exclusions, there were 13 estimates of IFR included in the final meta-analysis, from a wide range of countries, published between February and April 2020. The meta-analysis demonstrated a point-estimate of IFR of 0.75% (0.49-1.01%) with significant heterogeneity (p<0.001).  Based on a systematic review and meta-analysis of published evidence on COVID-19 until the end of April, 2020, the IFR of the disease across populations is 0.75% (0.49-1.01%). However, due to very high heterogeneity in the meta-analysis, it is difficult to know if this represents the "true" point estimate. It is likely that different places will experience different IFRs. More research looking at age-stratified IFR is urgently needed to inform policy-making on this front.

title: Geospatial Spread of the COVID-19 Pandemic in Mexico
abstract: COVID-19 is an infectious respiratory disease that the World Health Organization has declared a pandemic. Although a global phenomenon, there is the need to react locally with swift and informed actions, as some of the essential plans are highly dependent on factors such as culture, geography, laws, and customs. This paper presents our approach to mapping the geospatial spread of COVID-19 in Mexico at the state and municipal level, the highest allowed possible resolution in the publicly available dataset provided by the Health Ministry. To visualize the magnitude of the infection, we offer a map for the confirmed positive, pending, and deceases cases, while to support mobility, we provide a geospatial visualization of $R_t$, the basic reproduction number. This document describes the structure of the dataset, the software tools employed, and a description of the functionality of the maps, which definition we make publicly available.

title: More effective strategies are required to strengthen public awareness of COVID-19: Evidence from Google Trends
abstract: The outbreak of coronavirus disease 2019 (COVID-19) has posed stress on the health and well-being of both Chinese people and the public worldwide. Global public interest in this new issue largely reflects people’s attention to COVID-19 and their willingness to take precautionary actions. This study aimed to examine global public awareness of COVID-19 using Google Trends.  Using Google Trends, we retrieved public query data for terms of “2019-nCoV + SARS-CoV-2 + novel coronavirus + new coronavirus + COVID-19 + Corona Virus Disease 2019” between the 31(st) December 2019 and the 24(th) February 2020 in six major English-speaking countries, including the USA, the UK, Canada, Ireland, Australia, and New Zealand. Dynamic series analysis demonstrates the overall change trend of relative search volume (RSV) for the topic on COVID-19. We compared the top-ranking related queries and sub-regions distribution of RSV about COVID-19 across different countries. The correlation between daily search volumes on the topic related to COVID-19 and the daily number of people infected with SARS-CoV-2 was analyzed.  The overall search trend of RSV regarding COVID-19 increased during the early period of observing time and reached the first apex on 31(st) January 2020. A shorter response time and a longer duration of public attention to COVID-19 was observed in public from the USA, the UK, Australia, and Canada, than that in Ireland and New Zealand. A slightly positive correlation between daily RSV about COVID-19 and the daily number of confirmed cases was observed (P < 0.05). People across countries presented a various interest to the RSV on COVID-19, and public awareness of COVID-19 was different in various sub-regions within countries.  The results suggest that public response time to COVID-19 was different across countries, and the overall duration of public attention was short. The current study reminds us that governments should strengthen the publicity of COVID-19 nationally, strengthen the public's vigilance and sensitivity to COVID-19, inform public the importance of protecting themselves with enough precautionary measures, and finally control the spread of COVID-19 globally.

title: What Are People Asking About COVID-19? A Question Classification Dataset
abstract: We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question classes. The most common questions in our dataset asked about transmission, prevention, and societal effects of COVID, and we found that many questions that appeared in multiple sources were not answered by any FAQ websites of reputable organizations such as the CDC and FDA. We post our dataset publicly at https://github.com/JerryWei03/COVID-Q . For classifying questions into 15 categories, a BERT baseline scored 58.1% accuracy when trained on 20 examples per class, and for classifying questions into 89 question classes, the baseline achieved 54.6% accuracy. We hope COVID-Q can be helpful either for direct use in developing applied systems or as a domain-specific resource for model evaluation.

title: LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets
abstract: The study of cell-population heterogeneity in a range of biological systems, from viruses to bacterial isolates to tumor samples, has been transformed by recent advances in sequencing throughput. While the high-coverage afforded can be used, in principle, to identify very rare variants in a population, existing ad hoc approaches frequently fail to distinguish true variants from sequencing errors. We report a method (LoFreq) that models sequencing run-specific error rates to accurately call variants occurring in <0.05% of a population. Using simulated and real datasets (viral, bacterial and human), we show that LoFreq has near-perfect specificity, with significantly improved sensitivity compared with existing methods and can efficiently analyze deep Illumina sequencing datasets without resorting to approximations or heuristics. We also present experimental validation for LoFreq on two different platforms (Fluidigm and Sequenom) and its application to call rare somatic variants from exome sequencing datasets for gastric cancer. Source code and executables for LoFreq are freely available at http://sourceforge.net/projects/lofreq/.

title: POSTER PRESENTATION

title: Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis
abstract: Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. It has had a dramatic impact on society and world economies in only a few months. COVID-19 presents numerous challenges to all aspects of healthcare, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications to healthcare. Machine learning is a subset of AI that employs deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than humans. In this manuscript, we explore the potential for a simple and widely available test as a chest x-ray (CXR) to be utilized with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision. Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value. Finally, we developed and described a publicly available website to demonstrate how this technology can be made readily available in the future.

title: A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2
abstract: In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. For evaluating our network, we have tested it on 11302 images to report the actual accuracy achievable in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%.

title: COVID-19 forecasting based on an improved interior search algorithm and multi-layer feed forward neural network
abstract: COVID-19 is a novel coronavirus that was emerged in December 2019 within Wuhan, China. As the crisis of its serious increasing dynamic outbreak in all parts of the globe, the forecast maps and analysis of confirmed cases (CS) becomes a vital great changeling task. In this study, a new forecasting model is presented to analyze and forecast the CS of COVID-19 for the coming days based on the reported data since 22 Jan 2020. The proposed forecasting model, named ISACL-MFNN, integrates an improved interior search algorithm (ISA) based on chaotic learning (CL) strategy into a multi-layer feed-forward neural network (MFNN). The ISACL incorporates the CL strategy to enhance the performance of ISA and avoid the trapping in the local optima. By this methodology, it is intended to train the neural network by tuning its parameters to optimal values and thus achieving high-accuracy level regarding forecasted results. The ISACL-MFNN model is investigated on the official data of the COVID-19 reported by the World Health Organization (WHO) to analyze the confirmed cases for the upcoming days. The performance regarding the proposed forecasting model is validated and assessed by introducing some indices including the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) and the comparisons with other optimization algorithms are presented. The proposed model is investigated in the most affected countries (i.e., USA, Italy, and Spain). The experimental simulations illustrate that the proposed ISACL-MFNN provides promising performance rather than the other algorithms while forecasting task for the candidate countries.

title: Emergence of genomic diversity and recurrent mutations in SARS-CoV-2
abstract: SARS-CoV-2 is a SARS-like coronavirus of likely zoonotic origin first identified in December 2019 in Wuhan, the capital of China's Hubei province. The virus has since spread globally, resulting in the currently ongoing COVID-19 pandemic. The first whole genome sequence was published on January 52,020, and thousands of genomes have been sequenced since this date. This resource allows unprecedented insights into the past demography of SARS-CoV-2 but also monitoring of how the virus is adapting to its novel human host, providing information to direct drug and vaccine design. We curated a dataset of 7666 public genome assemblies and analysed the emergence of genomic diversity over time. Our results are in line with previous estimates and point to all sequences sharing a common ancestor towards the end of 2019, supporting this as the period when SARS-CoV-2 jumped into its human host. Due to extensive transmission, the genetic diversity of the virus in several countries recapitulates a large fraction of its worldwide genetic diversity. We identify regions of the SARS-CoV-2 genome that have remained largely invariant to date, and others that have already accumulated diversity. By focusing on mutations which have emerged independently multiple times (homoplasies), we identify 198 filtered recurrent mutations in the SARS-CoV-2 genome. Nearly 80% of the recurrent mutations produced non-synonymous changes at the protein level, suggesting possible ongoing adaptation of SARS-CoV-2. Three sites in Orf1ab in the regions encoding Nsp6, Nsp11, Nsp13, and one in the Spike protein are characterised by a particularly large number of recurrent mutations (>15 events) which may signpost convergent evolution and are of particular interest in the context of adaptation of SARS-CoV-2 to the human host. We additionally provide an interactive user-friendly web-application to query the alignment of the 7666 SARS-CoV-2 genomes.

title: Novel coronavirus in a 15-day-old neonate with clinical signs of sepsis, a case report
abstract: Novel coronavirus or coronavirus disease (COVID-19) can affect all age groups. The clinical course of the disease in children and infants is milder than in adults. It should be noted that, although typical symptoms may be present in children, non-specific symptoms could be noted in the neonate. The disease is rare in the neonate, so, its suspicion in this group can help to make a quick diagnose.  A 15-day-old neonate was admitted with fever, lethargy, cutaneous mottling, and respiratory distress without cough. His mother had symptoms of Novel coronavirus. So Reverse-Transcription Polymerase Chain Reaction (RT-PCR) assay was done for the neonate and showed to be positive. The newborn was isolated and subjected to supportive care. Antibiotic and antiviral treatment was initiated. Eventually, the baby was discharged in good general condition.  When a newborn presents with non-specific symptoms of infection with an added history of COVID-19 in his/her parents, it indicates the need for PCR testing for Novel coronavirus.

title: Virus Databases☆
abstract: Databases are critical to all scientific endeavours. They manage our email, funding, and scientific literature, and provide access to the ever-growing mountains of scientific data. In molecular virology, all researchers are familiar with performing BLAST searches of the DNA and protein sequence databases; however, in some respects these are simple databases tailored to this specific task. This collection of virus databases illustrates a greater diversity of purpose, but reveals they often function to support only one or a few virus families grouped by a common theme. The common characteristic is usually genome size as small RNA viruses are typically sequenced in vastly greater numbers and are much less complex than the large DNA viruses; they have variability at the level of SNPs rather than gene presence/absence. Here, we present the basics of database organization and aim to compare and contrast these features and the manner by which the databases are used by the scientific community.

title: Crowdsourcing a crisis response for COVID-19 in oncology
abstract: Crowdsourcing efforts are currently underway to collect and analyze data from patients with cancer who are affected by the COVID-19 pandemic. These community-led initiatives will fill key knowledge gaps to tackle crucial clinical questions on the complexities of infection with the causative coronavirus SARS-Cov-2 in the large, heterogeneous group of vulnerable patients with cancer.

title: Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device
abstract: Coronavirus Disease 2019 (COVID-19) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. For such a large-scale epidemic, early detection and isolation of potential virus carriers is essential to curb the spread of the epidemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the epidemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health condition of people wearing masks through analysis of the respiratory characteristics. The device mainly consists of a FLIR one thermal camera and an Android phone. This may help identify those potential patients of COVID-19 under practical scenarios such as pre-inspection in schools and hospitals. In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture.We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status on respiratory with the accuracy of 83.7\% on the real-world dataset. The abnormal respiratory data and part of normal respiratory data are collected from Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School. Other normal respiratory data are obtained from healthy people around our researchers. This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic.

title: Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound
abstract: Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DLbased solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, videolevel, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

title: An updated analysis of turning point, duration and attack rate of COVID-19 outbreaks in major Western countries with data of daily new cases
abstract: As coronavirus spreads around the world, the study of its effects is of great practical significance. We collated data on daily new cases of the COVID-19 outbreaks in the six Western countries of the Group of Seven and the dates of governments' interventions. We studied the periods before and after the dates of major governments' interventions integrally based on a segmented Poisson model. The relevant results are published in the paper of "Predicting turning point, duration and attack rate of COVID - 19 outbreaks in major Western countries" . Our method can be used to update prediction daily as COVID-19 outbreaks evolve. In this article, we illustrate an updated analysis with our method to facilitate reproducibility. Both datasets used and updated are provided.

title: Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods
abstract: This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images.  From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID-19 pneumonia patients, non-COVID-19 pneumonia patients, and the healthy samples. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre-trained models were compared with the VGG16-based model. Single type or no data augmentation methods were also evaluated. Splitting of training/validation/test sets was used when building and evaluating the CADx system. Three-category accuracy was evaluated for test set with 125 CXR images.  The three-category accuracy of the CAD system was 83.6% between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy. Sensitivity for COVID-19 pneumonia was more than 90%. The combination of conventional method and mixup was more useful than single type or no data augmentation method.  This study was able to create an accurate CADx system for the 3-category classification. Source code of our CADx system is available as open source for COVID-19 research.

title: A structured open dataset of government interventions in response to COVID-19
abstract: In response to the COVID-19 pandemic, governments have implemented a wide range of nonpharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset on a weekly basis until the end of December 2020.

title: Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans
abstract: COVID-19 pandemic has spread all over the world for months. As its transmissibility and high pathogenicity seriously threaten people's lives, the accurate and fast detection of the COVID-19 infection is crucial. Although many recent studies have shown that deep learning based solutions can help detect COVID-19 based on chest CT scans, there lacks a consistent and systematic comparison and evaluation on these techniques. In this paper, we first build a clean and segmented CT dataset called Clean-CC-CCII by fixing the errors and removing some noises in a large CT scan dataset CC-CCII with three classes: novel coronavirus pneumonia (NCP), common pneumonia (CP), and normal controls (Normal). After cleaning, our dataset consists of a total of 340,190 slices of 3,993 scans from 2,698 patients. Then we benchmark and compare the performance of a series of state-of-the-art (SOTA) 3D and 2D convolutional neural networks (CNNs). The results show that 3D CNNs outperform 2D CNNs in general. With extensive effort of hyperparameter tuning, we find that the 3D CNN model DenseNet3D121 achieves the highest accuracy of 88.63% (F1-score is 88.14% and AUC is 0.940), and another 3D CNN model ResNet3D34 achieves the best AUC of 0.959 (accuracy is 87.83% and F1-score is 86.04%). We further demonstrate that the mixup data augmentation technique can largely improve the model performance. At last, we design an automated deep learning methodology to generate a lightweight deep learning model MNas3DNet41 that achieves an accuracy of 87.14%, F1-score of 87.25%, and AUC of 0.957, which are on par with the best models made by AI experts. The automated deep learning design is a promising methodology that can help health-care professionals develop effective deep learning models using their private data sets. Our Clean-CC-CCII dataset and source code are available at: https://github.com/arthursdays/HKBU_HPML_COVID-19.

title: Anosmia and dysgeusia in patients with mild SARS-CoV-2 infection
abstract: In this cohort of 42 patients with mild COVID19 we describe the unique clinical feature of acute anosmia and dysgeusia in more than third of patients. Median onset of these features was 3.3 days after onset of illness (range 0-7) with rapid recovery in most patient.

title: A review of initial data on pregnancy during the COVID-19 outbreak: implications for assisted reproductive treatments
abstract: The current outbreak of the novel 2019 coronavirus disease (COVID-19) started in China in December 2019 and has since spread to several other countries. On March 25, 2020, a total of 375,498 cases had been confirmed globally with 2,201 cases in Brazil, showing the urgency of reacting to this international public health emergency. While in most cases, mild symptoms are observed, in some cases the infection leads to serious pulmonary disease. As a result, the possible consequences of the COVID-19 outbreak for pregnant women and its potential effects on the management of assisted reproductive treatments, demand attention. In this review, we summarize the latest research progress related to COVID-19 epidemiology and the reported data of pregnant women, and discuss the current evidence of COVID-19 infections during pregnancy and its potential consequences for assisted reproductive treatments. Reported data suggest that symptoms in pregnant women are similar to those in other people, and that there is no evidence for higher maternal or fetal risks. However, considering the initial data and lack of comprehensive knowledge on the pathogenesis of SARS-CoV-2 during pregnancy, human reproduction societies have recommended postponing the embryo transfers and do not initiate new treatment cycles. New evidence must be considered carefully in order to adjust these recommendations accordingly at any time and to guide assisted reproductive treatments.

title: The Coronavirus Pandemic: What Does the Evidence Show?
abstract: Coronavirus disease 2019 (COVID-19) is a newly emerged disease that has become a global public health concern as it rapidly spread around the world. The etiologic agent responsible for this disease has been named as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses as it shows similar genomic features to that of SARS-CoV which caused a pandemic in 2002. This disease first appeared in Hubei province of China and it follows human-to-human transmission but the path this virus took to set up human infection remains a mystery. By 17 April 2020, globally there have been 2,074,529 confirmed cases with 139,378 deaths because of COVID-19. SARS-CoV-2 shows several similarities with SARS?CoV, and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) with its clinical presentations. This can vary from asymptomatic infection to severe disease and mortality. Real-time reverse-transcription polymerase chain reaction (rRT-PCR) screening is considered as the standard laboratory test for the diagnosis of COVID-19. There is no proven antiviral agent against SARS-CoV-2 so the treatment for COVID-19 is symptomatic, aiming for the management of the symptoms and prevention of the complications. The outbreak of COVID-19 has led to the implementation of extraordinary public health measures throughout the world. Numerous antiviral compounds used to treat other infections are being clinically researched to find possible treatment. Similarly, the traditional public health outbreak response strategy of isolation, quarantine, social distancing and community containment has been implemented in multiple countries and has played an important role in the prevention of new outbreaks. This review aims to enhance our understanding of COVID 19.  Coronavirus disease 2019; COVID-19; SARS-CoV-2; novel coronavirus 2019; severe acute respiratory syndrome-2.

title: Traditional Chinese Medicine in the Treatment of Patients Infected with 2019-New Coronavirus (SARS-CoV-2): A Review and Perspective
abstract: Currently, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2, formerly known as 2019-nCoV, the causative pathogen of Coronavirus Disease 2019 (COVID-19)) has rapidly spread across China and around the world, causing an outbreak of acute infectious pneumonia. No specific anti-virus drugs or vaccines are available for the treatment of this sudden and lethal disease. The supportive care and non-specific treatment to ameliorate the symptoms of the patient are the only options currently. At the top of these conventional therapies, greater than 85% of SARS-CoV-2 infected patients in China are receiving Traditional Chinese Medicine (TCM) treatment. In this article, relevant published literatures are thoroughly reviewed and current applications of TCM in the treatment of COVID-19 patients are analyzed. Due to the homology in epidemiology, genomics, and pathogenesis of the SARS-CoV-2 and SARS-CoV, and the widely use of TCM in the treatment of SARS-CoV, the clinical evidence showing the beneficial effect of TCM in the treatment of patients with SARS coronaviral infections are discussed. Current experiment studies that provide an insight into the mechanism underlying the therapeutic effect of TCM, and those studies identified novel naturally occurring compounds with anti-coronaviral activity are also introduced.

title: VQuAnDa: Verbalization QUestion ANswering DAtaset
abstract: Answering (QA) systems over Knowledge Graphs (KGs) aim to provide a concise answer to a given natural language question. Despite the significant evolution of QA methods over the past years, there are still some core lines of work, which are lagging behind. This is especially true for methods and datasets that support the verbalization of answers in natural language. Specifically, to the best of our knowledge, none of the existing Question Answering datasets provide any verbalization data for the question-query pairs. Hence, we aim to fill this gap by providing the first QA dataset VQuAnDa that includes the verbalization of each answer. We base VQuAnDa on a commonly used large-scale QA dataset – LC-QuAD, in order to support compatibility and continuity of previous work. We complement the dataset with baseline scores for measuring future training and evaluation work, by using a set of standard sequence to sequence models and sharing the results of the experiments. This resource empowers researchers to train and evaluate a variety of models to generate answer verbalizations.

title: The role of asymptomatic SARS-CoV-2 infections: rapid living systematic review and meta-analysis
abstract: There is substantial disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a population. The disagreement results, in part, from the interpretation of studies that report a proportion of asymptomatic people with SARS-CoV-2 detected at a single point.  1. Amongst people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? 2. Amongst people with SARS-CoV-2 infection who are asymptomatic when diagnosed, what proportion will develop symptoms later? 3. What proportion of SARS-CoV-2 transmission is accounted for by people who are either asymptomatic throughout infection, or pre-symptomatic?  Rapid living systematic review (protocol https://osf.io/9ewys/). We searched Pubmed, Embase, bioRxiv and medRxiv using a living evidence database of SARS-CoV-2 literature on 25.03.2020. We included studies of people with SARS-CoV-2 diagnosed by reverse transcriptase PCR (RT-PCR) that documented follow-up and symptom status at the beginning and end of follow-up and modelling studies. Study selection, data extraction and bias assessment were done by one reviewer and verified by a second, with disagreement resolved by discussion or a third reviewer. We used a common-effect model to synthesise proportions from comparable studies.  We screened 89 studies and included 11. We estimated an upper bound for the proportion of asymptomatic SARS-CoV-2 infections of 29% (95% confidence interval 23 to 37%) in eight studies. Selection bias and likely publication bias affected the family case investigation studies. One statistical modelling study estimated the true proportion of asymptomatic infections at 18% (95% credibility interval 16 to 20%). Estimates of the proportions of pre-symptomatic individual in four studies were too heterogeneous to combine. In modelling studies, 40-60% of all SARS-CoV-2 infections are the result of transmission from pre-symptomatic individuals, with a smaller contribution from asymptomatic individuals.  An intermediate contribution of pre-symptomatic and asymptomatic infections to overall SARS-CoV-2 transmission means that combination prevention, with enhanced hand and respiratory hygiene, testing tracing and isolation strategies and social distancing, will continue to be needed. The findings of this systematic review of publications early in the pandemic suggests that most SARS-CoV-2 infections are not asymptomatic throughout the course of infection.

title: Deep learning COVID-19 detection bias: accuracy through artificial intelligence
abstract: Detection of COVID-19 cases’ accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered. Yet, there is no publicly available data on tests that could be missing infections. Complicating matters and furthering anxiety are specific instances of false-negative tests.  We developed a deep learning model to improve accuracy of reported cases and to precisely predict the disease from chest X-ray scans. Our model relied on convolutional neural networks (CNNs) to detect structural abnormalities and disease categorization that were keys to uncovering hidden patterns. To do so, a transfer learning approach was deployed to perform detections from the chest anterior-posterior radiographs of patients. We used publicly available datasets to achieve this.  Our results offer very high accuracy (96.3%) and loss (0.151 binary cross-entropy) using the public dataset consisting of patients from different countries worldwide. As the confusion matrix indicates, our model is able to accurately identify true negatives (74) and true positives (32); this deep learning model identified three cases of false-positive and one false-negative finding from the healthy patient scans.  Our COVID-19 detection model minimizes manual interaction dependent on radiologists as it automates identification of structural abnormalities in patient’s CXRs, and our deep learning model is likely to detect true positives and true negatives and weed out false positive and false negatives with > 96.3% accuracy.

title: Predicting the receptor-binding domain usage of the coronavirus based on kmer frequency on spike protein

title: Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
abstract: We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University. Our main objective is to provide predictions of the number of infected people for different countries in the next 14 days. The predictive analysis is done using time-series data transformed on a logarithmic scale. We use two well-known methods for prediction: polynomial regression and neural network. As the number of training data for each country is limited, we use a single-layer neural network called the extreme learning machine (ELM) to avoid over-fitting. Due to the non-stationary nature of the time-series, a sliding window approach is used to provide a more accurate prediction.

title: Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware
abstract: As the COVID-19 pandemic emerges in early 2020, a number of campaigns have started capitalizing the topic. Although a few media reports mentioned the existence of coronavirus-themed mobile malware, the research community lacks the understanding of the landscape of the coronavirus-themed mobile malware, and there is no publicly accessible dataset that could be utilized to boost the related research. In this paper, we present the first systematic study of coronavirus-themed mobile malware. We first make efforts to create a daily growing COVID-19 themed mobile app dataset, which contains $2,016$ COVID-19 themed apps and $277$ malware samples by the time of May 26, 2020. We then present an analysis of these apps from multiple perspectives including popularity and trends, installation methods, malicious behaviors and malicious campaigns. We observe that the growth of the number of COVID-19 themed apps is highly related to the number of confirmed cases of COVID-19 in the world. Most of them were released through distribution channels beyond app markets. A majority of the malicious apps (over 53%) are camouflaged as official apps using the same app identifiers and some of them use confusing similar app icons with the official ones to mislead users. Their main purposes are either stealing users' private information or making profit by using the tricks like phishing and extortion. Furthermore, we find that only 40\% of the COVID-19 malware creators are habitual developers who are active for a long time, while 60\% of them are new emerging ones in this pandemic and only released COVID-19 themed malware. The malicious developers are mainly located in US, mostly targeting countries including English countries, Arabic countries, Europe and China. To facilitate future research, we have publicly released all the well-labelled COVID-19 themed apps (and malware) to the research community.

title: Database resources of the National Center for Biotechnology Information
abstract: The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank(®) nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. The Entrez system provides search and retrieval operations for most of these data from 39 distinct databases. The E-utilities serve as the programming interface for the Entrez system. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. New resources released in the past year include PubMed Data Management, RefSeq Functional Elements, genome data download, variation services API, Magic-BLAST, QuickBLASTp, and Identical Protein Groups. Resources that were updated in the past year include the genome data viewer, a human genome resources page, Gene, virus variation, OSIRIS, and PubChem. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

title: A new twenty-first century science for effective epidemic response
abstract: With rapidly changing ecology, urbanization, climate change, increased travel and fragile public health systems, epidemics will become more frequent, more complex and harder to prevent and contain. Here we argue that our concept of epidemics must evolve from crisis response during discrete outbreaks to an integrated cycle of preparation, response and recovery. This is an opportunity to combine knowledge and skills from all over the world—especially at-risk and affected communities. Many disciplines need to be integrated, including not only epidemiology but also social sciences, research and development, diplomacy, logistics and crisis management. This requires a new approach to training tomorrow’s leaders in epidemic prevention and response.

title: An Evaluation of Publicly Available Deep Learning Based Commercial Information Retrieval Systems to search Biomedical Articles related to COVID-19
abstract: The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, primarily through the use of text mining and search tools. This has led to both corpora for biomedical articles related to COVID-19 (such as the CORD-19 corpus (Wang et al., 2020)) as well as search engines to query such data. While most research in search engines is performed in the academic field of information retrieval (IR), most academic search engines--though rigorously evaluated--are sparsely utilized, while major commercial web search engines (e.g., Google, Bing) dominate. This relates to COVID-19 because it can be expected that commercial search engines deployed for the pandemic will gain much higher traction than those produced in academic labs, and thus leads to questions about the empirical performance of these search tools. This paper seeks to empirically evaluate two such commercial search engines for COVID-19, produced by Google and Amazon, in comparison to the more academic prototypes evaluated in the context of the TREC-COVID track (Roberts et al., 2020). To ensure a fair comparison, we limit the number of documents in the retrieved runs and also annotate additional documents. We find that the top-performing system from TREC-COVID on bpref metric performed the best among the different systems evaluated in this study on all the metrics.

title: Modelling the global spread of diseases: A review of current practice and capability
abstract: Mathematical models can aid in the understanding of the risks associated with the global spread of infectious diseases. To assess the current state of mathematical models for the global spread of infectious diseases, we reviewed the literature highlighting common approaches and good practice, and identifying research gaps. We followed a scoping study method and extracted information from 78 records on: modelling approaches; input data (epidemiological, population, and travel) for model parameterization; model validation data. We found that most epidemiological data come from published journal articles, population data come from a wide range of sources, and travel data mainly come from statistics or surveys, or commercial datasets. The use of commercial datasets may benefit the modeller, however makes critical appraisal of their model by other researchers more difficult. We found a minority of records (26) validated their model. We posit that this may be a result of pandemics, or far-reaching epidemics, being relatively rare events compared with other modelled physical phenomena (e.g. climate change). The sparsity of such events, and changes in outbreak recording, may make identifying suitable validation data difficult. We appreciate the challenge of modelling emerging infections given the lack of data for both model parameterisation and validation, and inherent complexity of the approaches used. However, we believe that open access datasets should be used wherever possible to aid model reproducibility and transparency. Further, modellers should validate their models where possible, or explicitly state why validation was not possible.

title: Methodological challenges of analysing COVID-19 data during the pandemic

title: Declining Public Health Protections within Autocratic Regimes: Impact on Global Public Health Security, Infectious Disease Outbreaks, Epidemics, and Pandemics
abstract: Public health emergencies of international concern, in the form of infectious disease outbreaks, epidemics, and pandemics, represent an increasing risk to the worldʼs population. Management requires coordinated responses, across many disciplines and nations, and the capacity to muster proper national and global public health education, infrastructure, and prevention measures. Unfortunately, increasing numbers of nations are ruled by autocratic regimes which have characteristically failed to adopt investments in public health infrastructure, education, and prevention measures to keep pace with population growth and density. Autocratic leaders have a direct impact on health security, a direct negative impact on health, and create adverse political and economic conditions that only complicate the crisis further. This is most evident in autocratic regimes where health protections have been seriously and purposely curtailed. All autocratic regimes define public health along economic and political imperatives that are similar across borders and cultures. Autocratic regimes are seriously handicapped by sociopathic narcissistic leaders who are incapable of understanding the health consequences of infectious diseases or the impact on their population. A cross section of autocratic nations currently experiencing the impact of COVID-19 (coronavirus disease 2019) are reviewed to demonstrate the manner where self-serving regimes fail to manage health crises and place the rest of the world at increasing risk. It is time to re-address the pre-SARS (severe acute respiratory syndrome) global agendas calling for stronger strategic capacity, legal authority, support, and institutional status under World Health Organization (WHO) leadership granted by an International Health Regulations Treaty. Treaties remain the most successful means the world has in preventing, preparing for, and controlling epidemics in an increasingly globalized world. “Honesty is worth a lot more than hope…” The Economist, February 17, 2020.

title: Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
abstract: As of February 20, 2020, the 2019 novel coronavirus (renamed to COVID-19) spread to 30 countries with 2130 deaths and more than 75500 confirmed cases. COVID-19 is being compared to the infamous SARS coronavirus, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Though COVID-19 has a death rate of 2.8% as of 20 February, the 75752 confirmed cases in a few weeks (December 8, 2019 to February 20, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 genomes. The proposed method combines supervised machine learning with digital signal processing for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp. Our results support a hypothesis of a bat origin and classify COVID-19 as Sarbecovirus, within Betacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

title: The Extent of Transmission of Novel Coronavirus in Wuhan, China, 2020
abstract: A cluster of pneumonia cases linked to a novel coronavirus (2019-nCoV) was reported by China in late December 2019. Reported case incidence has now reached the hundreds, but this is likely an underestimate. As of 24 January 2020, with reports of thirteen exportation events, we estimate the cumulative incidence in China at 5502 cases (95% confidence interval: 3027, 9057). The most plausible number of infections is in the order of thousands, rather than hundreds, and there is a strong indication that untraced exposures other than the one in the epidemiologically linked seafood market in Wuhan have occurred.

title: S8 – S47 and O50 – O440

title: TUBERCULOUS SARCOIDOSIS: DOES IT EXIST?

title: CODA-19: Reliably Annotating Research Aspects on 10,000+ CORD-19 Abstracts Using a Non-Expert Crowd
abstract: This paper introduces CODA-19, a human-annotated dataset that codes the Background,   and Other sections of 10,966 English abstracts in the COVID-19 Open Research Dataset. CODA-19 was created by 248 crowd workers from Amazon Mechanical Turk within 10 days, achieving a label quality comparable to that of experts. Each abstract was annotated by nine different workers, and the final labels were obtained by majority vote. The inter-annotator agreement (Cohen's kappa) between the crowd and the biomedical expert (0.741) is comparable to inter-expert agreement (0.788). CODA-19's labels have an accuracy of 82.2% when compared to the biomedical expert's labels, while the accuracy between experts was 85.0%. Reliable human annotations help scientists to understand the rapidly accelerating coronavirus literature and also serve as the battery of AI/NLP research, but obtaining expert annotations can be slow. We demonstrated that a non-expert crowd can be rapidly employed at scale to join the fight against COVID-19.

title: Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?
abstract: To evaluate the diagnostic value of computed tomography (CT) and real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR) for COVID-19 pneumonia.  This retrospective study included all patients with COVID-19 pneumonia suspicion, who were examined by both CT and rRT-PCR at initial presentation. The sensitivities of both tests were then compared. For patients with a final confirmed diagnosis, clinical and laboratory data, in addition to CT imaging findings were evaluated.  A total of 36 patients were finally diagnosed with COVID-19 pneumonia. Thirty-five patients had abnormal CT findings at presentation, whereas one patient had a normal CT. Using rRT-PCR, 30 patients were tested positive, with 6 cases initially missed. Amongst these 6 patients, 3 became positive in the second rRT-PCR assay(after 2 days, 2 days and 3 days respectively), and the other 3 became positive only in the third round of rRT-PCR tests(after 5 days, 6 days and 8 days respectively). At presentation, CT sensitivity was therefore 97.2%, whereas the sensitivity of initial rRT-PCR was only 83.3%.  rRT-PCR may produce initial false negative results. We suggest that patients with typical CT findings but negative rRT-PCR results should be isolated, and rRT-PCR should be repeated to avoid misdiagnosis.

title: Otorhinolaryngologists and Coronavirus Disease 2019 (COVID-19)

title: Single cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse immune cell subtypes including T-bet+ B cells
abstract: Traditional laboratory model organisms represent a small fraction of the diversity of multicellular life, and findings in any given experimental model often do not translate to other species. Immunology research in non-traditional model organisms can be advantageous or even necessary (e.g. for host-pathogen interaction studies), but presents multiple challenges, many stemming from an incomplete understanding of potentially species-specific immune cell types, frequencies and phenotypes. Identifying and characterizing immune cells in such organisms is frequently limited by the availability of species-reactive immunophenotyping reagents for flow cytometry, and insufficient prior knowledge of cell type-defining markers. Here, we demonstrate the utility of single cell RNA sequencing (scRNA-Seq) to characterize immune cells for which traditional experimental tools are limited. Specifically, we used scRNA-Seq to comprehensively define the cellular diversity of equine peripheral blood mononuclear cells (PBMCs) from healthy horses across different breeds, ages, and sexes. We identified 30 cell type clusters partitioned into five major populations: Monocytes/Dendritic Cells, B cells, CD3+PRF1+ lymphocytes, CD3+PRF1- lymphocytes, and Basophils. Comparative analyses revealed many cell populations analogous to human PBMC, including transcriptionally heterogeneous monocytes and distinct dendritic cell subsets (cDC1, cDC2, plasmacytoid DC). Unexpectedly, we found that a majority of the equine peripheral B cell compartment is comprised of T-bet+ B cells; an immune cell subpopulation typically associated with chronic infection and inflammation in human and mouse. Taken together, our results demonstrate the potential of scRNA-Seq for cellular analyses in non-traditional model organisms, and form the basis for an immune cell atlas of horse peripheral blood.

title: A Bibliometric Analysis of COVID-19 Research Activity: A Call for Increased Output
abstract: The novel coronavirus disease 2019 (COVID-19) has impacted many countries across all inhabited continents, and is now considered a global pandemic, due to its high rate of infectivity. Research related to this disease is pivotal for assessing pathogenic characteristics and formulating therapeutic strategies. The aim of this paper is to explore the activity and trends of COVID-19 research since its outbreak in December 2019.  We explored the PubMed database and the World Health Organization (WHO) database for publications pertaining to COVID-19 since December 2019 up until March 18, 2020. Only relevant observational and interventional studies were included in our study. Data on COVID-19 incidence were extracted from the WHO situation reports. Research output was assessed with respect to gross domestic product (GDP) and population of each country.  Only 564 publications met our inclusion criteria. These articles came from 39 different countries, constituting 24% of all affected countries. China produced the greatest number of publications with 377 publications (67%). With respect to continental research activity, Asian countries had the highest research activity with 434 original publications (77%). In terms of publications per million persons (PPMPs), Singapore had the highest number of publications with 1.069 PPMPs. In terms of publications per billion-dollar GDP, Mauritius ranked first with 0.075.  COVID-19 is a major disease that has impacted international public health on a global level. Observational studies and therapeutic trials pertaining to COVID-19 are essential for assessing pathogenic characteristics and developing novel treatment options.

title: Comparison of risk factors for viral and nonviral asthma exacerbations

title: A U-turn motif-containing stem-loop in the coronavirus 5' untranslated region plays a functional role in replication.
abstract: The 5' untranslated region (UTR) of the mouse hepatitis virus (MHV) genome contains cis-acting sequences necessary for transcription and replication. A consensus secondary structural model of the 5' 140 nucleotides of the 5' UTRs of nine coronaviruses (CoVs) derived from all three major CoV groups is presented and characterized by three major stem-loops, SL1, SL2, and SL4. NMR spectroscopy provides structural support for SL1 and SL2 in three group 2 CoVs, including MHV, BCoV, and HCoV-OC43. SL2 is conserved in all CoVs, typically containing a pentaloop (C47-U48-U49-G50-U51 in MHV) stacked on a 5 base-pair stem, with some sequences containing an additional U 3' to U51; SL2 therefore possesses sequence features consistent with a U-turn-like conformation. The imino protons of U48 in the wild-type RNA, and G48 in the U48G SL2 mutant RNA, are significantly protected from exchange with solvent, consistent with a hydrogen bonding interaction critical to the hairpin loop architecture. SL2 is required for MHV replication; MHV genomes containing point substitutions predicted to perturb the SL2 structure (U48C, U48A) were not viable, while those that maintain the structure (U48G and U49A) were viable. The U48C MHV mutant supports both positive- and negative-sense genome-sized RNA synthesis, but fails to direct the synthesis of positive- or negative-sense subgenomic RNAs. These data support the existence of the SL2 in our models, and further suggest a critical role in coronavirus replication.

title: Large-Scale Phylogenetic Analysis of Emerging Infectious Diseases
abstract: Microorganisms that cause infectious diseases present critical issues of national security, public health, and economic welfare. For example, in recent years, highly pathogenic strains of avian influenza have emerged in Asia, spread through Eastern Europe, and threaten to become pandemic. As demonstrated by the coordinated response to Severe Acute Respiratory Syndrome (SARS) and influenza, agents of infectious disease are being addressed via large-scale genomic sequencing. The goal of genomic sequencing projects are to rapidly put large amounts of data in the public domain to accelerate research on disease surveillance, treatment, and prevention. However, our ability to derive information from large comparative genomic datasets lags far behind acquisition. Here we review the computational challenges of comparative genomic analyses, specifically sequence alignment and reconstruction of phylogenetic trees. We present novel analytical results on two important infectious diseases, Severe Acute Respiratory Syndrome (SARS) and influenza. SARS and influenza have similarities and important differences both as biological and comparative genomic analysis problems. Influenza viruses (Orthymxyoviridae) are RNA based. Current evidence indicates that influenza viruses originate in aquatic birds from wild populations. Influenza has been studied for decades via well-coordinated international efforts. These efforts center on surveillance via antibody characterization of the hemagglutinin (HA) and neuraminidase (N) proteins of the circulating strains to inform vaccine design. However, we still do not have a clear understanding of (1) various transmission pathways such as the role of intermediate hosts like swine and domestic birds and (2) the key mutation and genomic recombination events that underlie periodic pandemics of influenza. In the past 30 years, sequence data from HA and N loci has become an important data type. In the past year, full genomic data has become prominent. These data present exciting opportunities to address unanswered questions in influenza pandemics. SARS is caused by a previously unrecognized lineage of coronavirus, SARS-CoV, which like influenza has an RNA based genome. Although SARS-CoV is widely believed to have originated in animals, there remains disagreement over the candidate animal source that lead to the original outbreak of SARS. In contrast to the long history of the study of influenza, SARS was only recognized in late 2002 and the virus that causes SARS has been documented primarily by genomic sequencing. In the past, most studies of influenza were performed on a limited number of isolates and genes suited to a particular problem. Major goals in science today are to understand emerging diseases in broad geographic, environmental, societal, biological, and genomic contexts. Synthesizing diverse information brought together by various researchers is important to find out what can be done to prevent future outbreaks [JON03]. Thus comprehensive means to organize and analyze large amounts of diverse information are critical. For example, the relationships of isolates and patterns of genomic change observed in large datasets might not be consistent with hypotheses formed on partial data. Moreover when researchers rely on partial datasets, they restrict the range of possible discoveries. Phylogenetics is well suited to the complex task of understanding emerging infectious disease. Phylogenetic analyses can test many hypotheses by comparing diverse isolates collected from various hosts, environments, and points in time and organizing these data into various evolutionary scenarios. The products of a phylogenetic analysis are a graphical tree of ancestor–descendent relationships and an inferred summary of mutations, recombination events, host shifts, geographic, and temporal spread of the viruses. However, this synthesis comes at a price. The cost of computation of phylogenetic analysis expands combinatorially as the number of isolates considered increases. Thus, large datasets like those currently produced are commonly considered intractable. We address this problem with synergistic development of heuristics tree search strategies and parallel computing.

title: Data stream dataset of SARS-CoV-2 genome
abstract: As of May 25, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 348,000 deaths and more than 5,550,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical stream representation. Thus, the dataset provides four kinds of data stream representation (DSR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the DSR of 1557 instances of SARS-CoV-2 virus, 11540 other instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).

title: Hepatitis B Virus Genotype G forms core-like particles with unique structural properties
abstract: We have determined the structure of the core capsid of an unusual variant of hepatitis B virus, genotype G (HBV/G) at 14 Å resolution, using cryo-electron microscopy. The structure reveals surface features not present in the prototype HBV/A genotype. HBV/G is novel in that it has a unique 36- bp insertion downstream of the core gene start codon. This results in a twelve amino acid insertion at the N-terminal end of the core protein, and two stop codons in the precore region that prevent the expression of HBeAg. HBV/G replication in patients is associated with co-infection with another genotype of HBV, suggesting that HBV/G may have reduced replication efficiency in vivo. We localized the N-terminal insertion in HBV/G and show that it forms two additional masses on the core surface adjacent to each of the dimer-spikes and have modelled the structure of the additional residues within this density. We show that the position of the insertion would not interfere with translocation of nucleic acids through the pores to the core interior compartment. However, the insertion may partially obscure several residues on the core surface that are known to play a role in envelopment and secretion of virions, or that could affect structural rearrangements that may trigger envelopment after DNA second-strand synthesis.

title: Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim
abstract: In silico bacterial, viral, and human truth datasets were generated to evaluate available metagenomics algorithms. Sequenced datasets include background organisms, creating ambiguity in the true source organism for each read. Bacterial and viral datasets were created with even and staggered coverage to evaluate organism identification, read mapping, and gene identification capabilities of available algorithms. These truth datasets are provided as a resource for the development and refinement of metagenomic algorithms. Algorithm performance on these truth datasets can inform decision makers on strengths and weaknesses of available algorithms and how the results may be best leveraged for bacterial and viral organism identification and characterization. Source organisms were selected to mirror communities described in the Human Microbiome Project as well as the emerging pathogens listed by the National Institute of Allergy and Infectious Diseases. The six in silico datasets were used to evaluate the performance of six leading metagenomics algorithms: MetaScope, Kraken, LMAT, MetaPhlAn, MetaCV, and MetaPhyler.  Algorithms were evaluated on runtime, true positive organisms identified to the genus and species levels, false positive organisms identified to genus and species level, read mapping, relative abundance estimation, and gene calling. No algorithm out performed the others in all categories, and the algorithm or algorithms of choice strongly depends on analysis goals. MetaPhlAn excels for bacteria and LMAT for viruses. The algorithms were ranked by overall performance using a normalized weighted sum of the above metrics, and MetaScope emerged as the overall winner, followed by Kraken and LMAT.  Simulated FASTQ datasets with well-characterized truth data about microbial community composition reveal numerous insights about the relative strengths and weaknesses of the metagenomics algorithms evaluated. The simulated datasets are available to download from the Sequence Read Archive (SRP062063).

title: Knowledge-Based Tweet Classification for Disease Sentiment Monitoring
abstract: Disease monitoring and tracking is of tremendous value, not only for containing the spread of contagious diseases but also for avoiding unnecessary public concerns and even panic. In this chapter, we present a near real-time sentiment analysis service of public health-related tweets. Traditionally, it is impossible for humans to effectively measure the degree of public health concerns due to limited resources and significant time delays. To solve this problem, we have developed a computational intelligence approach for Epidemic Sentiment Monitoring System (ESMOS) to automatically analyze the disease sentiments and gauge the Measure of Concern (MOC) expressed by Twitter users. More specifically, we present a knowledge-based approach that employs a disease ontology to detect the outbreak of diseases and to analyze the linguistic expressions that convey subjective expressions and sentiment polarity of emotions, feelings, opinions, personal attitudes, etc. with a sentiment classifier. The two-step sentiment classification method utilizes the subjective vocabulary corpus (MPQA), sentiment strength corpus (AFINN), as well as emoticons and profanity words that are often used in social media postings. It first automatically classifies the tweets into personal and non-personal classes, eliminating many tweets such as non-personal “retweets” of news articles from further consideration. In the second stage, the personal tweets are classified into Negative and non-Negative sentiments. In addition, we present a model to quantify the public’s Measure of Concern (MOC) about a disease, based on sentiment classification results. The trends of the public MOC are visualized on a timeline. Correlation analyses between MOC timeline and disease-related sentiment category timelines show that the peaks of the MOC are weakly correlated with the peaks of the News timeline without any appreciable time delay or lead. Our sentiment analysis method and the MOC trend analyses can be generalized to other topical domains, such as mental health monitoring and crisis management. We present the ESMOS prototype for public health-related disease monitoring, for public concern trending and for mapping analyses.

title: KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species
abstract: Generating new ideas and scientific hypotheses is often the result of extensive literature and database reviews, overlaid with scientists’ own novel data and a creative process of making connections that were not made before. We have developed a comprehensive approach to guide this technically challenging data integration task and to make knowledge discovery and hypotheses generation easier for plant and crop researchers. KnetMiner can digest large volumes of scientific literature and biological research to find and visualise links between the genetic and biological properties of complex traits and diseases. Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size QTL. We have developed KnetMiner knowledge graphs and applications for a range of species including plants, crops and pathogens. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. KnetMiner is available at http://knetminer.org.

title: Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea
abstract: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations.  Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak.  Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources.  The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word "Coronavirus" communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers' attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852).  Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

title: Measuring multimorbidity beyond counting diseases: systematic review of community and population studies and guide to index choice.
abstract: To identify and summarise existing indices for measuring multimorbidity beyond disease counts, to establish which indices include mental health comorbidities or outcomes, and to develop recommendations based on applicability, performance, and usage.   Seven medical research databases (Medline, Web of Science Core Collection, Cochrane Library, Embase, PsycINFO, Scopus, and CINAHL Plus) from inception to October 2018 and bibliographies and citations of relevant papers. Searches were limited to English language publications.  Original articles describing a new multimorbidity index including more information than disease counts and not focusing on comorbidity associated with one specific disease. Studies were of adults based in the community or at population level.  Among 7128 search results, 5560 unique titles were identified. After screening against eligibility criteria the review finally included 35 papers. As index components, 25 indices used conditions (weighted or in combination with other parameters), five used diagnostic categories, four used drug use, and one used physiological measures. Predicted outcomes included mortality (18 indices), healthcare use or costs (13), hospital admission (13), and health related quality of life (7). 29 indices considered some aspect of mental health, with most including it as a comorbidity. 12 indices are recommended for use.  multimorbidity indices are available, with differing components and outcomes. Researchers and clinicians should examine existing indices for suitability before creating new ones.

title: Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning
abstract: The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID-19 is the ability to detect the infected patients early enough, and put them under special care. Detecting this disease from radiography and radiology images is perhaps one of the fastest way to diagnose the patients. Some of the early studies showed specific abnormalities in the chest radiograms of patients infected with COVID-19. Inspired by earlier works, we study the application of deep learning models to detect COVID-19 patients from their chest radiography images. We first prepare a dataset of 5,000 Chest X-rays from the publicly available datasets. Images exhibiting COVID-19 disease presence were identified by board-certified radiologist. Transfer learning on a subset of 2,000 radiograms was used to train four popular convolutional neural networks, including ResNet18, ResNet50, SqueezeNet, and DenseNet-121, to identify COVID-19 disease in the analyzed chest X-ray images. We evaluated these models on the remaining 3,000 images, and most of these networks achieved a sensitivity rate of 97\%($\pm$ 5\%), while having a specificity rate of around 90\%. While the achieved performance is very encouraging, further analysis is required on a larger set of COVID-19 images, to have a more reliable estimation of accuracy rates. Besides sensitivity and specificity rates, we also present the receiver operating characteristic (ROC), area under the curve (AUC), and confusion matrix of each model. The dataset, model implementations (in PyTorch), and evaluations, are all made publicly available for research community, here: https://github.com/shervinmin/DeepCovid.git

title: How many are at increased risk of severe COVID-19 disease? Rapid global, regional and national estimates for 2020
abstract: The risk of severe COVID-19 disease is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 illness, and how this varies between countries may inform the design of possible strategies to shield those at highest risk.  We estimated the number of individuals at increased risk of severe COVID-19 disease by age (5-year age groups), sex and country (n=188) based on prevalence data from the Global Burden of Disease (GBD) study for 2017 and United Nations population estimates for 2020. We also calculated the number of individuals without an underlying condition that could be considered at-risk because of their age, using thresholds from 50-70 years. The list of underlying conditions relevant to COVID-19 disease was determined by mapping conditions listed in GBD to the guidelines published by WHO and public health agencies in the UK and US. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity.  We estimate that 1.7 (1.0 - 2.4) billion individuals (22% [15-28%] of the global population) are at increased risk of severe COVID-19 disease. The share of the population at increased risk ranges from 16% in Africa to 31% in Europe. Chronic kidney disease (CKD), cardiovascular disease (CVD), diabetes and chronic respiratory disease (CRD) were the most prevalent conditions in males and females aged 50+ years. African countries with a high prevalence of HIV/AIDS and Island countries with a high prevalence of diabetes, also had a high share of the population at increased risk. The prevalence of multimorbidity (>1 underlying conditions) was three times higher in Europe than in Africa (10% vs 3%).  Based on current guidelines and prevalence data from GBD, we estimate that one in five individuals worldwide has a condition that is on the list of those at increased risk of severe COVID-19 disease. However, for many of these individuals the underlying condition will be undiagnosed or not severe enough to be captured in health systems, and in some cases the increase in risk may be quite modest. There is an urgent need for robust analyses of the risks associated with different underlying conditions so that countries can identify the highest risk groups and develop targeted shielding policies to mitigate the effects of the COVID-19 pandemic.

title: On the Generation of Medical Dialogues for COVID-19
abstract: Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets -- CovidDialog -- (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size, which bear high risk of overfitting, we leverage transfer learning to mitigate data deficiency. Specifically, we take the pretrained models of Transformer, GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune them on our CovidDialog tasks. We perform both automatic and human evaluation of responses generated by these models. The results show that the generated responses are promising in being doctor-like, relevant to the conversation history, and clinically informative. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue.

title: Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron
abstract: Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical, modeling can provide satisfactory models, it can also fail to comprehend the intricacies contained within the data. In this paper, authors use a publicly available dataset, containing information on infected, recovered, and deceased patients in 406 locations over 51 days (22nd January 2020 to 12th March 2020). This dataset, intended to be a time-series dataset, is transformed into a regression dataset and used in training a multilayer perceptron (MLP) artificial neural network (ANN). The aim of training is to achieve a worldwide model of the maximal number of patients across all locations in each time unit. Hyperparameters of the MLP are varied using a grid search algorithm, with a total of 5376 hyperparameter combinations. Using those combinations, a total of 48384 ANNs are trained (16128 for each patient group—deceased, recovered, and infected), and each model is evaluated using the coefficient of determination (R2). Cross-validation is performed using K-fold algorithm with 5-folds. Best models achieved consists of 4 hidden layers with 4 neurons in each of those layers, and use a ReLU activation function, with R2 scores of 0.98599 for confirmed, 0.99429 for deceased, and 0.97941 for recovered patient models. When cross-validation is performed, these scores drop to 0.94 for confirmed, 0.781 for recovered, and 0.986 for deceased patient models, showing high robustness of the deceased patient model, good robustness for confirmed, and low robustness for recovered patient model.

title: Pandemic related Health literacy - A Systematic Review of literature in COVID-19, SARS and MERS pandemics
abstract: Health literacy plays an essential role in ones ability to acquire and understand critical medical information in the COVID-19 infodemic and other pandemics.  To summarize the assessment, levels and determinants of pandemic related health literacy and its associated clinical outcomes.  Medline, Embase, PsychINFO, CINAHL, arXiv, bioRxiv, medRxiv, and Social Science Research Network. The start date was unrestricted and current as of 22 April 2020.  Studies which evaluated health literacy related to novel coronavirus disease 2019 (COVID-19), Severe Acute Respiratory Syndrome (SARS) or Middle East Respiratory Syndrome (MERS) Data extraction Data on the characteristics of study designs, instruments, participants and level of health literacy were collected. Items used in instruments were grouped under the themes of knowledge, attitudes and practices. Determinants of health literacy were grouped into five domains (socio-demographic, medical, psychological/psychiatric, health systems related and others).  Of 2,065 articles screened, 70 articles were included. 21, 17 and 32 studies evaluated health literacy related to COVID-19, SARS and MERS, respectively. The rates of low pandemic health literacy ranged from 4.3 to 57.9% among medical-related populations and 4.0% to 82.5% among non-medical populations. Knowledge about symptoms and transmission of infection; worry about infection and, practices related to mask usage and hand hygiene was most frequently evaluated. Socio-demographic determinants of health literacy were most studied, where higher education level, older age and female gender were associated with better health literacy. No studies evaluated outcomes associated with health literacy.  Non-English articles were excluded.  The level of pandemic related health literacy is sub-optimal. Healthcare administrators need to be aware of health literacy determinants when formulating policies in pandemics.

title: Emergence of a Novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2: Biology and Therapeutic Options
abstract: The new decade of the 21(st) century (2020) started with the emergence of a novel coronavirus known as SARS-CoV-2 that caused an epidemic of coronavirus disease (COVID-19) in Wuhan, China. It is the third highly pathogenic and transmissible coronavirus after severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) emerged in humans. The source of origin, transmission to humans, and mechanisms associated with the pathogenicity of SARS-CoV-2 are not yet clear, however, its resemblance to SARS-CoV and several other bat coronaviruses was recently confirmed through genome sequencing-related studies. The development of therapeutic strategies is necessary in order to prevent further epidemics and cure infections. In this review, we summarize current information about the emergence, origin, diversity, and epidemiology of three pathogenic coronaviruses with a specific focus on the current outbreak in Wuhan, China. Furthermore, we discuss the clinical features and potential therapeutic options that may be effective against SARS-CoV-2.

title: Drug repurposing: a better approach for infectious disease drug discovery?
abstract: The advent of publically available databases containing system-wide phenotypic data of the host response to both drugs and pathogens, in conjunction with bioinformatics and computational methods now allows for in silico predictions of FDA-approved drugs as treatments against infection diseases. This systems biology approach captures the complexity of both the pathogen and drug host response in the form of expression patterns or molecular interaction networks without having to understand the underlying mechanisms of action. These drug repurposing techniques have been successful in identifying new drug candidates for several types of cancers and were recently use to identify potential therapeutics against influenza, the newly discovered Middle Eastern Respiratory Syndrome coronavirus and several parasitic diseases. These new approaches have the potential to significantly reduce both the time and cost for infectious diseases drug discovery.

title: Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCoV, in the nasal tissue
abstract: A novel coronavirus (2019-nCoV) was first identified in Wuhan, Hubei Province, and then spreads to the other Provinces of China. WHO decides to determine a Public Health Emergency of International Concern (PHEIC) of 2019-nCoV. 2019-nCov was reported to share the same receptor, Angiotensin-converting enzyme 2 (ACE2), with SARS-Cov. Here based on the public single-cell RNA-Seq datasets, we analyzed the ACE2 RNA expression profile in the tissues at different locations of the respiratory tract. The result indicates that the ACE2 expression appears in nasal epithelial cells. We found that the size of this population of ACE2-expressing nasal epithelial cells is comparable with the size of the population of ACE2-expression type II alveolar cells (AT2) in the Asian sample reported by Yu Zhao et al. We further detected 2019-nCoV by polymerase chain reaction (PCR) from the nasal-swab and throat-swab of seven suspected cases. We found that 2019-nCoV tends to have a higher concentration in the nasal-swab comparing to the throat-swab, which could attribute to the ACE2-expressing nasal epithelial cells. We hope this study could be informative for virus-prevention strategy development, especially the treatment of nasal mucus.

title: GEval: A Modular and Extensible Evaluation Framework for Graph Embedding Techniques
abstract: While RDF data are graph shaped by nature, most traditional Machine Learning (ML) algorithms expect data in a vector form. To transform graph elements to vectors, several graph embedding approaches have been proposed. Comparing these approaches is interesting for 1) developers of new embedding techniques to verify in which cases their proposal outperforms the state-of-art and 2) consumers of these techniques in choosing the best approach according to the task(s) the vectors will be used for. The comparison could be delayed (and made difficult) by the choice of tasks, the design of the evaluation, the selection of models, parameters, and needed datasets. We propose GEval, an evaluation framework to simplify the evaluation and the comparison of graph embedding techniques. The covered tasks range from ML tasks (Classification, Regression, Clustering), semantic tasks (entity relatedness, document similarity) to semantic analogies. However, GEval is designed to be (easily) extensible. In this article, we will describe the design and development of the proposed framework by detailing its overall structure, the already implemented tasks, and how to extend it. In conclusion, to demonstrate its operating approach, we consider the parameter tuning of the KGloVe algorithm as a use case.

title: Public Health Emergency Preparedness and Response Communications with Health Care Providers: A Literature Review
abstract: Health care providers (HCPs) play an important role in public health emergency preparedness and response (PHEPR) so need to be aware of public health threats and emergencies. To inform HCPs, public health issues PHEPR messages that provide guidelines and updates, and facilitate surveillance so HCPs will recognize and control communicable diseases, prevent excess deaths and mitigate suffering. Public health agencies need to know that the PHEPR messages sent to HCPs reach their target audience and are effective and informative. Public health agencies need to know that the PHEPR messages sent to HCPs reach their target audience and are effective and informative. We conducted a literature review to investigate the systems and tools used by public health to generate PHEPR communications to HCPs, and to identify specific characteristics of message delivery mechanisms and formats that may be associated with effective PHEPR communications.  A systematic review of peer- and non-peer-reviewed literature focused on the following questions: 1) What public health systems exist for communicating PHEPR messages from public health agencies to HCPs? 2) Have these systems been evaluated and, if yes, what criteria were used to evaluate these systems? 3) What have these evaluations discovered about characterizations of the most effective ways for public health agencies to communicate PHEPR messages to HCPs?  We identified 25 systems or tools for communicating PHEPR messages from public health agencies to HCPs. Few articles assessed PHEPR communication systems or messaging methods or outcomes. Only one study compared the effectiveness of the delivery format, device or message itself. We also discovered that the potential is high for HCPs to experience "message overload" given redundancy of PHEPR messaging in multiple formats and/or through different delivery systems.  We found that detailed descriptions of PHEPR messaging from public health to HCPs are scarce in the literature and, even when available are rarely evaluated in any systematic fashion. To meet present-day and future information needs for emergency preparedness, more attention needs to be given to evaluating the effectiveness of these systems in a scientifically rigorous manner.

title: What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
abstract: The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to a great extent. Besides direct physical and economic threats, the pandemic also indirectly impact people's mental health conditions, which can be overwhelming but difficult to measure. The problem may come from various reasons such as unemployment status, stay-at-home policy, fear for the virus, and so forth. In this work, we focus on applying natural language processing (NLP) techniques to analyze tweets in terms of mental health. We trained deep models that classify each tweet into the following emotions: anger, anticipation, disgust, fear, joy, sadness, surprise and trust. We build the EmoCT (Emotion-Covid19-Tweet) dataset for the training purpose by manually labeling 1,000 English tweets. Furthermore, we propose and compare two methods to find out the reasons that are causing sadness and fear.

title: GIS-based spatial modeling of COVID-19 incidence rate in the continental United States
abstract: During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been announced, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.

title: Fighting against the common enemy of COVID-19: a practice of building a community with a shared future for mankind
abstract: The outbreak of coronavirus disease 2019 (COVID-19) has caused more than 80 813 confirmed cases in all provinces of China, and 21 110 cases reported in 93 countries of six continents as of 7 March 2020 since middle December 2019. Due to biological nature of the novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with faster spreading and unknown transmission pattern, it makes us in a difficulty position to contain the disease transmission globally. To date, we have found it is one of the greatest challenges to human beings in fighting against COVID-19 in the history, because SARS-CoV-2 is different from SARS-CoV and MERS-CoV in terms of biological features and transmissibility, and also found the containment strategies including the non-pharmaceutical public health measures implemented in China are effective and successful. In order to prevent a potential pandemic-level outbreak of COVID-19, we, as a community of shared future for mankind, recommend for all international leaders to support preparedness in low and middle income countries especially, take strong global interventions by using old approaches or new tools, mobilize global resources to equip hospital facilities and supplies to protect noisome infections and to provide personal protective tools such as facemask to general population, and quickly initiate research projects on drug and vaccine development. We also recommend for the international community to develop better coordination, cooperation, and strong solidarity in the joint efforts of fighting against COVID-19 spreading recommended by the joint mission report of the WHO-China experts, against violating the International Health Regulation ( 2005), and against stigmatization, in order to eventually win the battle against our common enemy — COVID-19.

title: COVID-19 outbreak: current scenario of Pakistan
abstract: COVID-19 outbreak was first time experienced in the Wuhan City of China at the end of December 2019. Which spread rapidly in China and then worldwide in 209 countries of America, Europe, Australia and Asia including Pakistan. There are more than fifty thousand mortalities and one million plus people have been affected worldwide, while figure increases rapidly. Different steps have been taken worldwide for the control of COVID-19. Even with less resources Pakistan also taken rigorous measures like designed special hospitals, Laboratories for testing, quarantine facilities, awareness campaign and lock down to control the spread of virus. We highlighted the efforts of government to combat this deadly pneumonia.

title: Novel coronavirus SARS-CoV-2 and COVID-19. Practice recommendations for obstetric anaesthesia: what we have learned thus far
abstract: • Pregnancy does not seem to be associated with more severe COVID-19 infections. • Risk of antenatal vertical transmission of COVID-19 is low. • Epidural analgesia should be considered, provided platelet counts are not low. • Droplet precautions are indicated for all women with suspected COVID-19. • Airborne precautions recommended for general anaesthesia or higher risk thereof.

title: Review and methodological analysis of trials currently testing treatment and prevention options for the novel coronavirus disease (COVID-19) globally.
abstract: As novel coronavirus disease (COVID-19) cases continue to steeply rise globally within an unprecedented short period of time, solid evidence from large randomised controlled trials is still lacking. Currently, numerous trials testing potential treatment and preventative options are undertaken globally.  We summarised all currently registered clinical trials examining treatment and prevention options for COVID-19 pneumonia. Additionally, we evaluated the quality of the retrieved interventional studies.  The ClinicalTrials.gov, the Chinese Clinical Trial Registry and the European Union Clinical Trials Register were systematically searched.  Registered clinical trials examining treatment and/or prevention options for COVID-19 were included. No language, country or study design restrictions were applied. Withdrawn, cancelled studies and trials not reporting therapeutic or preventative strategies for COVID-19 were excluded.  No restrictions in terms of participants' age and medical background or type of intervention were enforced.  The registries were searched using the term "coronavirus" or "COVID-19" from their inception until 26th March 2020. Additional manual search of the registries was also performed. Eligible studies were summarised and tabulated. Interventional trials were methodologically analysed, excluding expanded access studies and trials testing Traditional Chinese Medicine.  In total, 309 trials evaluating therapeutic management options, 23 studies assessing preventive strategies and 3 studies examining both were retrieved. Interventional treatment studies were mostly randomised (n=150, 76%) and open-label (n=73, 37%) with a median number of planned inclusions of 90 (IQR 40-200). Major categories of interventions that are currently being investigated are discussed.  Numerous clinical trials have been registered since the onset of the COVID-19 pandemic. Summarised data on these trials will assist physicians and researchers to promote patient care and guide future research efforts for COVID-19 pandemic containment. However, up to the end of March, 2020, significant information concerning reported trials was lacking.

title: Risks of Novel Coronavirus Disease (COVID-19) in Pregnancy; a Narrative Review
abstract: The outbreak of the new Coronavirus in China in December 2019 and subsequently in various countries around the world has raised concerns about the possibility of vertical transmission of the virus from mother to fetus. The present study aimed to review published literature in this regard.  In this narrative review, were searched for all articles published in various databases including PubMed, Scopus, Embase, Science Direct, and Web of Science using MeSH-compliant keywords including COVID-19, Pregnancy, Vertical transmission, Coronavirus 2019, SARS-CoV-2 and 2019-nCoV from December 2019 to March 18, 2020 and reviewed them. All type of articles published about COVID-19 and vertical transmission in pregnancy were included.  A review of 13 final articles published in this area revealed that COVID-19 can cause fetal distress, miscarriage, respiratory distress and preterm delivery in pregnant women but does not infect newborns. There has been no report of vertical transmission in pregnancy, and it has been found that clinical symptoms of COVID-19 in pregnant women are not different from those of non-pregnant women.  Overall, due to lack of appropriate data about the effect of COVID-19 on pregnancy, it is necessary to monitor suspected pregnant women before and after delivery. For confirmed cases both the mother and the newborn child should be followed up comprehensively.

title: NSAIDs in patients with viral infections, including Covid-19: victims or perpetrators?
abstract: Taking anti-inflammatory drugs, including non-steroidal (NSAIDs), during Covid-19 infection, how much is risky? The French Minister of Health, who has raised an alarm on a possible risk deriving from the use of ibuprofen for the control of fever and other symptoms during the disease, opened the debate a few days ago. In this paper we examine available evidence from preclinical and clinical studies that had analysed the role of COX in the inflammatory process and the effects of NSAIDs in patients with infections. Most of the published studies that suggested not protective effects of NSAIDs were mainly performed in vitro or on animals. Therefore, their meaning in humans is to be considered with great caution. Based also on data suggesting protective effects of NSAIDs, we concluded that currently there is no evidence suggesting a correlation between NSAIDs and a worsening of infections. Further studies will be certainly needed to better define the role of NSAIDs and particularly COX2 inhibitors in patients with infections. In the meantime, we must wait for results of the revision started by the PRAC on May 2019 on the association ibuprofen/ketoprofen​​​​​​ and worsening of infections. Since nowadays no scientific evidence establishes a correlation between NSAIDS and worsening of COVID-19, patients should be advice against any NSAIDs self-medication when COVID-19 like symptoms are present.

title: Special Article: Risk Communication During COVID-19
abstract: During the unprecedented times caused by the novel coronavirus COVID-19, there is rapidly evolving information and guidance. However, a focus must also be on proper and effective risk communication. This is especially the case during pandemics that have high rates of infection, significant morbidity, lack of therapeutic measures, and rapid increases in cases, all of which apply to the current COVID-19 pandemic. A consequence of poor risk communication and heightened risk perception is hoarding behavior, which can lead to lack of medications and personal protective equipment. One potential way to ensure appropriate risk communication is utilizing social media channels, and ensuring an ongoing consistent media presence. Another important step is to include all stakeholders including members of the allergy community in broader public health messaging. As we continue to face unprecedented times in the allergy community, an understanding and appreciation of risk communication will be essential as we communicate with, and inform, our patients, and our colleagues, moving forward.

title: Transfer Learning Using Convolutional Neural Network Architectures for Brain Tumor Classification from MRI Images
abstract: Brain tumor classification is very important in medical applications to develop an effective treatment. In this paper, we use brain contrast-enhanced magnetic resonance images (CE-MRI) benchmark dataset to classify three types of brain tumor (glioma, meningioma and pituitary). Due to the small number of training dataset, our classification systems evaluate deep transfer learning for feature extraction using nine deep pre-trained convolutional Neural Networks (CNNs) architectures. The objective of this study is to increase the classification accuracy, speed the training time and avoid the overfitting. In this work, we trained our architectures involved minimal pre-processing for three different epoch number in order to study its impact on classification performance and consuming time. In addition, the paper benefits acceptable results with small number of epoch in limited time. Our interpretations confirm that transfer learning provides reliable results in the case of small dataset. The proposed system outperforms the state-of-the-art methods and achieve 98.71% classification accuracy.

title: Evidence of recombination in coronaviruses implicating pangolin origins of nCoV-2019
abstract: A novel coronavirus (nCoV-2019) was the cause of an outbreak of respiratory illness detected in Wuhan, Hubei Province, China in December of 2019. Genomic analyses of nCoV-2019 determined a 96% resemblance with a coronavirus isolated from a bat in 2013 (RaTG13); however, the receptor binding motif (RBM) of these two genomes share low sequence similarity. This divergence suggests a possible alternative source for the RBM coding sequence in nCoV-2019. We identified high sequence similarity in the RBM between nCoV-2019 and a coronavirus genome reconstructed from a viral metagenomic dataset from pangolins possibly indicating a more complex origin for nCoV-2019.

title: MosMedData: Chest CT Scans with COVID-19 Related Findings
abstract: This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia.  https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License.

title: The effect of variant interference on de novo assembly for viral deep sequencing
abstract: Viruses have high mutation rates and generally exist as a mixture of variants in biological samples. Next-generation sequencing (NGS) approaches have surpassed Sanger for generating long viral sequences, yet how variants affect NGS de novo assembly remains largely unexplored.  Our results from > 15,000 simulated experiments showed that presence of variants can turn an assembly of one genome into tens to thousands of contigs. This “variant interference” (VI) is highly consistent and reproducible by ten commonly-used de novo assemblers, and occurs over a range of genome length, read length, and GC content. The main driver of VI is pairwise identities between viral variants. These findings were further supported by in silico simulations, where selective removal of minor variant reads from clinical datasets allow the “rescue” of full viral genomes from fragmented contigs.  These results call for careful interpretation of contigs and contig numbers from de novo assembly in viral deep sequencing.

title: ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics
abstract: The digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact.  In this paper, we present an overview of Oak Ridge Biosurveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various biosurveillance-related tasks.  We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using claims data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the claims data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as "bridge regions" contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences.  These quantitative insights show how the claims data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance.

title: Deep Learning for Screening COVID-19 using Chest X-Ray Images
abstract: With the ever increasing demand for screening millions of prospective"novel coronavirus"or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, we propose a new concept called domain extension transfer learning (DETL). We employ DETL, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes viz. $normal$, $other\_disease$, $pneumonia$ and $Covid-19$. A 5-fold cross validation is performed to estimate the feasibility of using chest X-Rays to diagnose COVID-19. The initial results show promise, with the possibility of replication on bigger and more diverse data sets. The overall accuracy was measured as $95.3\% \pm 0.02$. In order to get an idea about the COVID-19 detection transparency, we employed the concept of Gradient Class Activation Map (Grad-CAM) for detecting the regions where the model paid more attention during the classification. This was found to strongly correlate with clinical findings, as validated by experts.

title: Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors
abstract: The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes and olfactory epithelial cells are likely under-appreciated targets of SARS-CoV-2 infection, correlating with reported loss of sense of taste and smell as early indicators of COVID-19 infection, including in otherwise asymptomatic patients. Airway club cells, ciliated cells and type II pneumocytes in the lung, and enterocytes of the gut also express ACE2. This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses.

title: The Evaluation of Sleep Disturbances for Chinese Frontline Medical Workers under the Outbreak of COVID-19
abstract: The outbreak of coronavirus disease 2019 in China remains to be a serious challenge for frontline medical workers (fMW). They are under high risk of being infected and high mental stress, which may lead to sleep disturbances, anxiety, and depression.  We conducted a cross-sectional study to evaluate sleep disturbances of fMW and made a comparison with non-fMW. The medical workers from multiple hospitals in Hubei Province, China, were volunteered to participate. An online questionnaire, including Pittsburgh Sleep Quality Index (PSQI), Athens Insomnia Scale (AIS), and Visual Analogue Scale (VAS), was used to evaluate sleep disturbances and mental status of fMW. Sleep disturbances were defined as PSQI>7 points or/and AIS>6 points. We compared the scores of PSQI, AIS, anxiety and depression VAS, and prevalence of sleep disturbances between fMW and non-fMW. Subgroup analysis for different gender in fMW was conducted.  A total of 1306 subjects (including 801 fMW and 505 non-fMW) were enrolled. Compared to non-fMW, fMW had significantly higher scores of PSQI (p＜0.0001), AIS (p＜0.0001), anxiety (p＜0.0001) and depression (p=0.0010), and higher prevalence of sleep disturbances with PSQI > 7 points (p＜0.0001) and AIS > 6 points (p＜0.0001). In subgroup analysis, compared to male fMW, female fMW had significantly higher scores of PSQI (p=0.022) and higher prevalence of sleep disturbances with PSQI > 7 points (p<0.0001).  fMW had higher prevalence of sleep disturbances and worse sleep quality than non-fMW. Female fMW were more vulnerable to having sleep disturbances than male fMW.

title: The impact of current and future control measures on the spread of COVID-19 in Germany
abstract: The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020. In view of most recent information on testing activity, we present here an update of our initial work. In this work, mathematical models have been developed to study the spread of COVID-19 among the population in Germany and to asses the impact of non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended here to account for undetected infections, as well as for stages of infections and age groups. The models are calibrated on data until April 5, data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases and reduced contact to risk groups.

title: Research on the Influence of Effective Distance Between Cities on the Cross-regional Transmission of COVID-19
abstract: The COVID-19 epidemic in China has been effectively controlled. It is of great significance to study the law of cross-regional spread of the epidemic, for the prevention and control of the COVID-19 in the future in China and other countries or regions. In this study, the cross-regional connection intensity between cities was characterized based on the probability and the effective distance of the shortest path tree, and the empirical analysis was carried out based on the high-frequency data such as the cases of COVID 19 outbreaks. It is concluded that the higher the intensity of inter-city connection, the larger scale the cross-regional spread of the epidemic.

title: Demographic, etiological, and histological pulmonary analysis of patients with acute respiratory failure: a study of 19 years of autopsies
abstract: Acute respiratory failure has been one of the most important causes of death in intensive care units, and certain aspects of its pulmonary pathology are currently unknown.  The objective was to describe the demographic data, etiology, and pulmonary histopathological findings of different diseases in the autopsies of patients with acute respiratory failure.  Autopsies of 4,710 patients with acute respiratory failure from 1990 to 2008 were reviewed, and the following data were obtained: age, sex, and major associated diseases. The pulmonary histopathology was categorized as diffuse alveolar damage, pulmonary edema, alveolar hemorrhage, and lymphoplasmacytic interstitial pneumonia. The odds ratio of the concordance between the major associated diseases and specific autopsy findings was calculated using logistic regression.  Bacterial bronchopneumonia was present in 33.9% of the cases and cancer in 28.1%. The pulmonary histopathology showed diffuse alveolar damage in 40.7% (1,917) of the cases. A multivariate analysis showed a significant and powerful association between diffuse alveolar damage and bronchopneumonia, HIV/AIDS, sepsis, and septic shock, between liver cirrhosis and pulmonary embolism, between pulmonary edema and acute myocardial infarction, between dilated cardiomyopathy and cancer, between alveolar hemorrhage and bronchopneumonia and pulmonary embolism, and between lymphoplasmacytic interstitial pneumonia and HIV/AIDS and liver cirrhosis.  Bronchopneumonia was the most common diagnosis in these cases. The most prevalent pulmonary histopathological pattern was diffuse alveolar damage, which was associated with different inflammatory conditions. Further studies are necessary to elucidate the complete pathophysiological mechanisms involved with each disease and the development of acute respiratory failure.

title: Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach
abstract: The objective of the study is to examine coronavirus disease (COVID-19) related discussions, concerns, and sentiments that emerged from tweets posted by Twitter users. We analyze 4 million Twitter messages related to the COVID-19 pandemic using a list of 25 hashtags such as"coronavirus,""COVID-19,""quarantine"from March 1 to April 21 in 2020. We use a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigram, bigrams, salient topics and themes, and sentiments in the collected Tweets. Popular unigrams include"virus,""lockdown,"and"quarantine."Popular bigrams include"COVID-19,""stay home,""corona virus,""social distancing,"and"new cases."We identify 13 discussion topics and categorize them into five different themes, such as"public health measures to slow the spread of COVID-19,""social stigma associated with COVID-19,""coronavirus news cases and deaths,""COVID-19 in the United States,"and"coronavirus cases in the rest of the world". Across all identified topics, the dominant sentiments for the spread of coronavirus are anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics. The public reveals a significant feeling of fear when they discuss the coronavirus new cases and deaths than other topics. The study shows that Twitter data and machine learning approaches can be leveraged for infodemiology study by studying the evolving public discussions and sentiments during the COVID-19. Real-time monitoring and assessment of the Twitter discussion and concerns can be promising for public health emergency responses and planning. Already emerged pandemic fear, stigma, and mental health concerns may continue to influence public trust when there occurs a second wave of COVID-19 or a new surge of the imminent pandemic.

title: Portable sequencing, genomic data, and scale in global emerging infectious disease surveillance
abstract: Emerging infectious diseases (EIDs) occur when pathogens unpredictably spread into new contexts. EID surveillance systems seek to rapidly identify EID outbreaks to contain spread and improve public health outcomes. Sequencing data has historically not been integrated into real‐time responses, but portable DNA sequencing technology has prompted optimism among epidemiologists. Specifically, attention has focused on the goal of a “sequencing singularity”: the integration of portable sequencers in a worldwide event‐based surveillance network with other digital data (Gardy & Loman,  2018, p. 9). The sequencing singularity vision is a powerful socio‐technical imaginary, shaping the discourse around the future of portable sequencing. Ethical and practical issues are bound by the vision in two ways: they are framed only as obstacles, and they are formulated only at the scales made visible by its implicit geography. This geography privileges two extremes of scale – the genomic and the global – and leaves intermediate scales comparatively unmapped. We explore how widespread portable sequencing could challenge this geography. Portable sequencers put the ability to produce genomic data in the hands of the individual. The explicit assertion of rights over data may therefore become a matter disputed more at an interpersonal scale than an international one. Portable sequencers also promise ubiquitous, indiscriminate sequencing of the total metagenomic content of samples, raising the question of what (or who) is under surveillance and inviting consideration of the human microbiome and more‐than‐human geographies. We call into question a conception of a globally integrated stream of sequencing data as composed mostly of “noise,” within which signals of pathogen “emergence” are “hidden,” considering it instead from the perspective of recent work into more‐than‐human geographies. Our work highlights a practical need for researchers to consider both the alternative possibilities they foreclose as well as the exciting opportunities they move towards when they deploy their visions of the future.

title: The COVID-19 pandemic: a new challenge for syndromic surveillance
abstract: The COVID-19 pandemic is exerting major pressures on society, health and social care services and science. Understanding the progression and current impact of the pandemic is fundamental to planning, management and mitigation of future impact on the population. Surveillance is the core function of any public health system, and a multi-component surveillance system for COVID-19 is essential to understand the burden across the different strata of any health system and the population. Many countries and public health bodies utilise ‘syndromic surveillance’ (using real-time, often non-specific symptom/preliminary diagnosis information collected during routine healthcare provision) to supplement public health surveillance programmes. The current COVID-19 pandemic has revealed a series of unprecedented challenges to syndromic surveillance including: the impact of media reporting during early stages of the pandemic; changes in healthcare-seeking behaviour resulting from government guidance on social distancing and accessing healthcare services; and changes in clinical coding and patient management systems. These have impacted on the presentation of syndromic outputs, with changes in denominators creating challenges for the interpretation of surveillance data. Monitoring changes in healthcare utilisation is key to interpreting COVID-19 surveillance data, which can then be used to better understand the impact of the pandemic on the population. Syndromic surveillance systems have had to adapt to encompass these changes, whilst also innovating by taking opportunities to work with data providers to establish new data feeds and develop new COVID-19 indicators. These developments are supporting the current public health response to COVID-19, and will also be instrumental in the continued and future fight against the disease.

title: Abstract Presentations from the AABB Annual Meeting Boston, MA, October 13–16, 2018

title: CoAID: COVID-19 Healthcare Misinformation Dataset
abstract: As the COVID-19 virus quickly spreads around the world, unfortunately, misinformation related to COVID-19 also gets created and spreads like wild fire. Such misinformation has caused confusion among people, disruptions in society, and even deadly consequences in health problems. To be able to understand, detect, and mitigate such COVID-19 misinformation, therefore, has not only deep intellectual values but also huge societal impacts. To help researchers combat COVID-19 health misinformation, therefore, we present CoAID (Covid-19 heAlthcare mIsinformation Dataset), with diverse COVID-19 healthcare misinformation, including fake news on websites and social platforms, along with users' social engagement about such news. CoAID includes 1,896 news, 183,564 related user engagements, 516 social platform posts about COVID-19, and ground truth labels. The dataset is available at: https://github.com/cuilimeng/CoAID.

title: Epidemiological Characteristics of COVID-19: A Systemic Review and Meta-Analysis
abstract: Our understanding of the corona virus disease 2019 (COVID-19) continues to evolve. However, there are many unknowns about its epidemiology.  To synthesize the number of deaths from confirmed COVID-19 cases, incubation period, as well as time from onset of COVID-19 symptoms to first medical visit, ICU admission, recovery and death of COVID-19.  MEDLINE, Embase, and Google Scholar from December 01, 2019 through to March 11, 2020 without language restrictions as well as bibliographies of relevant articles.  Quantitative studies that recruited people living with or died due to COVID-19.  Two independent reviewers extracted the data. Conflicts were resolved through discussion with a senior author.  Out of 1675 non-duplicate studies identified, 57 were included. Pooled mean incubation period was 5.84 (99% CI: 4.83, 6.85) days. Pooled mean number of days from the onset of COVID-19 symptoms to first clinical visit was 4.82 (95% CI: 3.48, 6.15), ICU admission was 10.48 (95% CI: 9.80, 11.16), recovery was 17.76 (95% CI: 12.64, 22.87), and until death was 15.93 (95% CI: 13.07, 18.79). Pooled probability of COVID-19-related death was 0.02 (95% CI: 0.02, 0.03).  Studies are observational and findings are mainly based on studies that recruited patient from clinics and hospitals and so may be biased toward more severe cases.  We found that the incubation period and lag between the onset of symptoms and diagnosis of COVID-19 is longer than other respiratory viral infections including MERS and SARS; however, the current policy of 14 days of mandatory quarantine for everyone might be too conservative. Longer quarantine periods might be more justified for extreme cases.

title: Pig immune response to general stimulus and to porcine reproductive and respiratory syndrome virus infection: a meta-analysis approach
abstract: The availability of gene expression data that corresponds to pig immune response challenges provides compelling material for the understanding of the host immune system. Meta-analysis offers the opportunity to confirm and expand our knowledge by combining and studying at one time a vast set of independent studies creating large datasets with increased statistical power. In this study, we performed two meta-analyses of porcine transcriptomic data: i) scrutinized the global immune response to different challenges, and ii) determined the specific response to Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) infection. To gain an in-depth knowledge of the pig response to PRRSV infection, we used an original approach comparing and eliminating the common genes from both meta-analyses in order to identify genes and pathways specifically involved in the PRRSV immune response. The software Pointillist was used to cope with the highly disparate data, circumventing the biases generated by the specific responses linked to single studies. Next, we used the Ingenuity Pathways Analysis (IPA) software to survey the canonical pathways, biological functions and transcription factors found to be significantly involved in the pig immune response. We used 779 chips corresponding to 29 datasets for the pig global immune response and 279 chips obtained from 6 datasets for the pig response to PRRSV infection, respectively.  The pig global immune response analysis showed interconnected canonical pathways involved in the regulation of translation and mitochondrial energy metabolism. Biological functions revealed in this meta-analysis were centred around translation regulation, which included protein synthesis, RNA-post transcriptional gene expression and cellular growth and proliferation. Furthermore, the oxidative phosphorylation and mitochondria dysfunctions, associated with stress signalling, were highly regulated. Transcription factors such as MYCN, MYC and NFE2L2 were found in this analysis to be potentially involved in the regulation of the immune response. The host specific response to PRRSV infection engendered the activation of well-defined canonical pathways in response to pathogen challenge such as TREM1, toll-like receptor and hyper-cytokinemia/ hyper-chemokinemia signalling. Furthermore, this analysis brought forth the central role of the crosstalk between innate and adaptive immune response and the regulation of anti-inflammatory response. The most significant transcription factor potentially involved in this analysis was HMGB1, which is required for the innate recognition of viral nucleic acids. Other transcription factors like interferon regulatory factors IRF1, IRF3, IRF5 and IRF8 were also involved in the pig specific response to PRRSV infection.  This work reveals key genes, canonical pathways and biological functions involved in the pig global immune response to diverse challenges, including PRRSV infection. The powerful statistical approach led us to consolidate previous findings as well as to gain new insights into the pig immune response either to common stimuli or specifically to PRRSV infection.

title: The first two cases of 2019‐nCoV in Italy: Where they come from?
abstract: A novel Coronavirus, 2019‐nCoV, has been identified as the causal pathogen of an ongoing epidemic, with the first cases reported in Wuhan, China, last December 2019, and has since spread to other countries worldwide, included Europe and very recently Italy. In this short report, phylogenetic reconstruction was used to better understand the transmission dynamics of the virus from its first introduction in China focusing on the more recent evidence of infection in a couple of Chinese tourists arrived in Italy on 23rd January 2020 and labeled as Coronavirus Italian cases. A maximum clade credibility tree has been built using a dataset of 54 genome sequences of 2019‐nCoV plus two closely related bat strains (SARS‐like CoV) available in GenBank. Bayesian time‐scaled phylogenetic analysis was implemented in BEAST 1.10.4. The Bayesian phylogenetic reconstruction showed that 2019‐2020 nCoV firstly introduced in Wuhan on 25 November 2019, started epidemic transmission reaching many countries worldwide, including Europe and Italy where the two strains isolated dated back 19 January 2020, the same that the Chinese tourists arrived in Italy. Strains isolated outside China were intermixed with strains isolated in China as evidence of likely imported cases in Rome, Italy, and Europe, as well. In conclusion, this report suggests that further spread of 2019‐nCoV epidemic was supported by human mobility and that quarantine of suspected or diagnosed cases is useful to prevent further transmission. Viral genome phylogenetic analysis represents a useful tool for the evaluation of transmission dynamics and preventive action.

title: The impact of changes in diagnostic testing practices on estimates of COVID-19 transmission in the United States
abstract: Estimates of the reproductive number for novel pathogens such as SARS-CoV-2 are essential for understanding the potential trajectory of the epidemic and the level of intervention that is needed to bring the epidemic under control. However, most methods for estimating the basic reproductive number (R(0)) and time-varying effective reproductive number (R(t)) assume that the fraction of cases detected and reported is constant through time. We explore the impact of secular changes in diagnostic testing and reporting on estimates of R(0) and R(t) using simulated data. We then compare these patterns to data on reported cases of COVID-19 and testing practices from different United States (US) states. We find that changes in testing practices and delays in reporting can result in biased estimates of R(0) and R(t). Examination of changes in the daily number of tests conducted and the percent of patients testing positive may be helpful for identifying the potential direction of bias. Changes in diagnostic testing and reporting processes should be monitored and taken into consideration when interpreting estimates of the reproductive number of COVID-19.

title: SARS-CoV-2 and gastrointestinal tract: the dark side of the pandemic

title: Coronavirus Disease (COVID-19) Pandemic: An Overview of Systematic Reviews
abstract: Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging and ongoing critical appraisal of this output is essential. We aimed to collate and summarize all published systematic reviews on the coronavirus disease (COVID-19).  Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO's Global Research, LILACS and Epistemonikos) were searched from December 1, 2019 to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory and radiological findings) and quality assessment (AMSTAR 2). Meta-analysis on prevalence of clinical outcomes was performed.  Eighteen systematic reviews were included; one was empty. Using AMSTAR 2, confidence in the results of 13 reviews was rated as "critically low", one as "low", one as "moderate" and two as "high". Symptoms of COVID-19 were (range values of point estimates): fever (82-95%), cough with or without sputum (58-72%), dyspnea (26-59%), myalgia or muscle fatigue (29-51%), sore throat (10-13%), headache (8-12%) and gastrointestinal complaints (5-9%). Severe symptoms were more common in men. Elevated C-reactive protein (associated with lymphocytopenia) and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. Chest imaging described a frequent pattern of uni- or bilateral multilobar ground-glass opacity. Only one review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3% to 14%.  Confidence in the results of most reviews was "critically low". Future studies and systematic reviews should adhere to established methodologies. The majority of included systematic reviews were hampered by imprecise search strategy and no previous protocol submission.

title: From genome-scale data to models of infectious disease: A Bayesian network-based strategy to drive model development.
abstract: High-throughput, genome-scale data present a unique opportunity to link host to pathogen on a molecular level. Forging such connections will help drive the development of mathematical models to better understand and predict both pathogen behavior and the epidemiology of infectious diseases, including malaria. However, the datasets that can aid in identifying these links and models are vast and not amenable to simple, reductionist, and univariate analyses. These datasets require data mining in order to identify the truly important measurements that best describe clinical and molecular observations. Moreover, these datasets typically have relatively few samples due to experimental limitations (particularly for human studies or in vivo animal experiments), making data mining extremely difficult. Here, after first providing a brief overview of common strategies for data reduction and identification of relationships between variables for inclusion in mathematical models, we present a new generalized strategy for performing these data reduction and relationship inference tasks. Our approach emphasizes the importance of robustness when using data to drive model development, particularly when using genome-scale, small-sample in vivo data. We identify the use of appropriate feature reduction combined with data permutations and subsampling strategies as being critical to enable increasingly robust results from network inference using high-dimensional, low-observation data.

title: Deep Learning for Screening COVID-19 using Chest X-Ray Images
abstract: With the ever increasing demand for screening millions of prospective "novel coronavirus" or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, we propose a new concept called domain extension transfer learning (DETL). We employ DETL, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes viz, normal, other_disease, pneumonia and Covid-19. A 5-fold cross validation is performed to estimate the feasibility of using chest X-Rays to diagnose COVID-19. The initial results show promise, with the possibility of replication on bigger and more diverse data sets. The overall accuracy was measured as 95.3%{+/-}0.02. In order to get an idea about the COVID-19 detection transparency, we employed the concept of Gradient Class Activation Map (Grad-CAM) for detecting the regions where the model paid more attention during the classification. This was found to strongly correlate with clinical findings, as validated by experts.

title: Dataset of Vietnamese student's learning habits during COVID-19
abstract: A dataset was constructed to examine Vietnamese student's learning habits during the time schools were suspended due to the novel coronavirus - SARS-CoV-2 (COVID-19), in response to a call for interdisciplinary research on the potential effects of the coronavirus pandemic (Elsevier, 2020). The questionnaires were spread over a network of educational communities on Facebook from February 7 to February 28, 2020. Using the snowball sampling method, researchers delivered the survey to teachers and parents to provide formal consent before they forwarded it to their students and children. In order to measure the influence of students' socioeconomic status and occupational aspirations on their learning habits during school closures, the survey included three major groups of questions: (1) Individual demographics, including family socioeconomic status, school type, and occupational aspirations; (2) Student's learning habits, including hours of learning before and during the period of school suspension, with and without other people's support; and (3) Students' perceptions of their self-learning during the school closures. There was a total of 920 clicks on the survey link, but only 460 responses accompanied by consent forms were received. Non-credible answers (e.g., year of birth after 2009, more than 20 hours of learning per day) were eliminated. The final dataset included 420 valid observations.

title: Quantification of Intra-Host Genomic Diversity of SARS-CoV-2 Allows a High-Resolution Characterization of Viral Evolution and Reveals Functionally Convergent Variants
abstract: A global cross-discipline effort is ongoing to characterize the evolution of SARS-CoV-2 virus and generate reliable epidemiological models of its diffusion. To this end, phylogenomic approaches leverage accumulating genomic mutations as barcodes to track the evolutionary history of the virus and can benefit from the surge of sequences deposited in public databases. Yet, such methods typically rely on consensus sequences representing the dominant virus lineage, whereas a complex sublineage architecture is often observed within single hosts. Furthermore, most approaches do not account for variants accumulation processes and might produce inaccurate results in condition of limited sampling, as witnessed in most countries affected by the epidemics. We introduce VERSO (Viral Evolution ReconStructiOn), a new comprehensive framework for the characterization of viral evolution and transmission from sequencing data of viral genomes. our approach accounts for accumulation of clonal mutations and uncertainty in the data, by taking advantage of the achievements of research in cancer evolution, to deliver robust phylogenomic lineage models, and exploits intra-host variant frequency profiles to characterize the sublineage similarity among samples, which may derive from uncovered infection events. The application of our approach to RNA-sequencing data of 162 SARS-CoV-2 samples generates a high-resolution model of evolution and spread, which improves recent findings on viral types and highlights the existence of patterns of co-occurrence of minor variants, revealing likely infection paths among hosts harboring the same viral lineage. The in-depth analysis of the mutational landscape of SARS-CoV-2 confirms a statistically significant increase of genomic diversity in time and identifies a number of variants that are transiting from minor to clonal state in the population. We also show that standard phylogenetic methods can produce unreliable results when handling datasets with noise and sampling limitations, as proven by the further application of VERSO to 12419 consensus sequences included in GISAID database. Notably, VERSO allows to pinpoint minor variants that might be positively or negatively selected across distinct lineages, thus driving the design of treatments and vaccines. In particular, minor variant g.29039A>U, detected in multiple viral lineages and validated on independent samples, shows that SARS-CoV-2 can lose its main Nucleocapsid immunogenic epitopes, raising concerns about the effectiveness of vaccines targeting the C-terminus of this protein. Finally, we here release the likely SARS-CoV-2 ancestral genome, obtained by resolving ambiguous SNPs that distinguish two widely-used reference genomes from human samples, by employing the Pangolin-CoV and the Bat-CoV-RaTG13 genomes. Our results show that the joint application of our framework and data-driven epidemiological models might allow to deliver a high-resolution platform for pathogen surveillance and analysis.

title: RNA based mNGS approach identifies a novel human coronavirus from two individual pneumonia cases in 2019 Wuhan outbreak
abstract: From December 2019, an outbreak of unusual pneumonia was reported in Wuhan with many cases linked to Huanan Seafood Market that sells seafood as well as live exotic animals. We investigated two patients who developed acute respiratory syndromes after independent contact history with this market. The two patients shared common clinical features including fever, cough, and multiple ground-glass opacities in the bilateral lung field with patchy infiltration. Here, we highlight the use of a low-input metagenomic next-generation sequencing (mNGS) approach on RNA extracted from bronchoalveolar lavage fluid (BALF). It rapidly identified a novel coronavirus (named 2019-nCoV according to World Health Organization announcement) which was the sole pathogens in the sample with very high abundance level (1.5% and 0.62% of total RNA sequenced). The entire viral genome is 29,881 nt in length (GenBank MN988668 and MN988669, Sequence Read Archive database Bioproject accession PRJNA601736) and is classified into β-coronavirus genus. Phylogenetic analysis indicates that 2019-nCoV is close to coronaviruses (CoVs) circulating in Rhinolophus (Horseshoe bats), such as 98.7% nucleotide identity to partial RdRp gene of bat coronavirus strain BtCoV/4991 (GenBank KP876546, 370 nt sequence of RdRp and lack of other genome sequence) and 87.9% nucleotide identity to bat coronavirus strain bat-SL-CoVZC45 and bat-SL-CoVZXC21. Evolutionary analysis based on ORF1a/1b, S, and N genes also suggests 2019-nCoV is more likely a novel CoV independently introduced from animals to humans.

title: TWIRLS, an automated topic-wise inference method based on massive literature, suggests a possible mechanism via ACE2 for the pathological changes in the human host after coronavirus infection
abstract: Faced with the current large-scale public health emergency, collecting, sorting, and analyzing biomedical information related to the “coronavirus” should be done as quickly as possible to gain a global perspective, which is a basic requirement for strengthening epidemic control capacity. However, for human researchers studying the viruses and the hosts, the vast amount of information available cannot be processed effectively and in a timely manner, particularly when the scientific understanding may be limited, which can further lower the information processing efficiency. We present TWIRLS, a method that can automatically acquire, organize, and classify information. Additionally, independent functional data sources can be added to build an inference system using a machine-based approach, which can provide relevant knowledge to help human researchers quickly establish subject cognition and to make more effective decisions. TWIRLS can automatically analyze more than three million words in more than 14,000 literature articles in only 4 hours. Combining with generalized gene interaction databases creates a data interface that can help researchers to further analyze the information. Using the TWIRLS system, we found that an important regulatory factor angiotensin-converting enzyme 2 (ACE2) may be involved in the host pathological changes on binding to the coronavirus after infection. After triggering functional changes in ACE2/AT2R, an imbalance in the steady-state cytokine regulatory axis involving the Renin-Angiotensin System and IP-10 leads to a cytokine storm.

title: [Nurses: A Voice to Lead, Nursing the World to Health-Viewing COVID-19 Epidemic Prevention Efforts in Light of Nightingale's Perspective on Infection Control]
abstract: Florence Nightingale (1820-1910), extolled as the founder of modern nursing, contributed greatly to the advancement of modern public health. Written 150 years ago, Nightingale's advice on infection control, addressing the importance of hand washing, environmental sanitation, ventilation, sunshine, statistical data, and health literacy, remains highly relevant in today's global fight against the coronavirus. In honor of Florence Nightingale's 200th birthday, World Health Organization declared 2020 the International Year of the Nurse and Midwife. The era of "Nurses: A Voice to Lead - Nursing the World to Health" is drawing near. This article addresses the profound impact of Nightingale's infection control and public health perspectives on measures currently being taken to contain coronavirus disease 2019 (COVID-19). Furthermore, we call on governments throughout the world to invest proactively in nursing to assure the safety of patients and promote public health in order to achieve the pledge of the United Nations' sustainable development goals to leave no one behind.

title: Studies of Novel Coronavirus Disease 19 (COVID-19) Pandemic: A Global Analysis of Literature
abstract: An exponential growth of literature about novel coronavirus disease 19 (COVID-19) has been observed in the last few months. This textual analysis of 5,780 publications extracted from the Web of Science, Medline, and Scopus databases was performed to explore the current research focuses and propose further research agenda. The Latent Dirichlet allocation was used for topic modeling. Regression analysis was conducted to examine country variations in the research focuses. Results indicated that publications were mainly contributed by the United States, China, and European countries. Guidelines for emergency care and surgical, viral pathogenesis, and global responses in the COVID-19 pandemic were the most common topics. There was variation in the research approaches to mitigate COVID-19 problems in countries with different income and transmission levels. Findings highlighted the need for global research collaboration among high- and low/middle-income countries in the different stages of prevention and control the pandemic.

title: Analytical Validation of a COVID-19 qRT-PCR Detection Assay Using a 384-well Format and Three Extraction Methods
abstract: The COVID-19 global pandemic is an unprecedented health emergency. Insufficient access to testing has hampered effective public health interventions and patient care management in a number of countries. Furthermore, the availability of regulatory-cleared reagents has challenged widespread implementation of testing. We rapidly developed a qRT-PCR SARS-CoV-2 detection assay using a 384-well format and tested its analytic performance across multiple nucleic acid extraction kits. Our data shows robust analytic accuracy on residual clinical biospecimens. Limit of detection sensitivity and specificity was confirmed with currently available commercial reagents. Our methods and results provide valuable information for other high-complexity laboratories seeking to develop effective, local, laboratory-developed procedures with high-throughput capability to detect SARS-CoV-2.

title: Survey data of COVID-19-related Knowledge, Attitude, and Practices among Indonesian Undergraduate Students
abstract: The dataset presented in this paper is an examination of COVID-19-related knowledge, attitude, and practice among Indonesian undergraduate students. The data were collected during first month of college or university closure due to COVID-19 through a survey distributed via an online questionnaire, assessing sociodemographic information (6 items), knowledge (18 items), attitude (6 item), and practice (12 items), from 27(th) April and 2(nd) May, 2020, gathering a total of 6,249 responses. A combination of purposive and snowball techniques helped to select the respondents via Whatsapp from more than ten universities in Indonesia. The survey data were analyzed using descriptive and inferential statistics. The data will assist in preventing and curbing the spread of COVID-19 in the university and can assist with planning for educational interventions for students’ awareness.

title: Automatic Detection of COVID-19 Infection from Chest X-ray using Deep Learning
abstract: COVID-19 infection has created a panic across the globe in recent times. Early detection of COVID-19 infection can save many lives in the pre-vailing situation. This virus affects the respiratory system of a person and creates white patchy shadows in the lungs. Deep learning is one of the most effective Artificial Intelligence techniques to analyse chest X-ray images for efficient and reliable COVID-19 screening. In this paper, we have proposed a Deep Convolutional Neural Network method for fast and dependable identification of COVID-19 infection cases from the patient chest X-ray images. To validate the performance of the proposed system, chest X-ray images of more than 150 confirmed COVID-19 patients from the Kaggle data repository are used in the experimentation. The results show that the proposed system identifies the cases with an accuracy of 93%.

title: Expression of the SARS-CoV-2 cell receptor gene ACE2 in a wide variety of human tissues
abstract: Since its discovery in December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 2 180 000 people worldwide and has caused more than 150 000 deaths as of April 16, 2020. SARS-CoV-2, which is the virus causing coronavirus disease 2019 (COVID-19), uses the angiotensin-converting enzyme 2 (ACE2) as a cell receptor to invade human cells. Thus, ACE2 is the key to understanding the mechanism of SARS-CoV-2 infection. This study is to investigate the ACE2 expression in various human tissues in order to provide insights into the mechanism of SARS-CoV-2 infection.  We compared ACE2 expression levels across 31 normal human tissues between males and females and between younger (ages ≤ 49 years) and older (ages > 49 years) persons using two-sided Student’s t test. We also investigated the correlations between ACE2 expression and immune signatures in various tissues using Pearson’s correlation test.  ACE2 expression levels were the highest in the small intestine, testis, kidneys, heart, thyroid, and adipose tissue, and were the lowest in the blood, spleen, bone marrow, brain, blood vessels, and muscle. ACE2 showed medium expression levels in the lungs, colon, liver, bladder, and adrenal gland. ACE2 was not differentially expressed between males and females or between younger and older persons in any tissue. In the skin, digestive system, brain, and blood vessels, ACE2 expression levels were positively associated with immune signatures in both males and females. In the thyroid and lungs, ACE2 expression levels were positively and negatively associated with immune signatures in males and females, respectively, and in the lungs they had a positive and a negative correlation in the older and younger groups, respectively.  Our data indicate that SARS-CoV-2 may infect other tissues aside from the lungs and infect persons with different sexes, ages, and races equally. The different host immune responses to SARS-CoV-2 infection may partially explain why males and females, young and old persons infected with this virus have markedly distinct disease severity. This study provides new insights into the role of ACE2 in the SARS-CoV-2 pandemic.

title: Data analytics for novel coronavirus disease
abstract: This paper describes different aspects of novel coronavirus disease (COVID-19), presents visualization of the spread of the infection, and discusses the potential applications of data analytics on this viral infection. Firstly, a literature survey is done on COVID-19 highlighting a number of factors including its origin, its similarity with previous coronaviruses, its transmission capacity, its symptoms, etc. Secondly, data analytics is applied on a dataset of Johns Hopkins University to find out the spread of the viral infection. It is shown here that although the disease started in China in December 2019, the highest number of confirmed cases up to June 04, 2020 is in the USA. Thirdly, the worldwide increase in the number of confirmed cases over time is modelled here using a polynomial regression algorithm with degree 2. Fourthly, classification algorithms are applied on a dataset of 5644 samples provided by Hospital Israelita Albert Einstein of Brazil in order to diagnose COVID-19. It is shown here that multilayer perceptron (MLP), XGBoost and logistic regression can classify COVID-19 patients at an accuracy above 91%. Finally, a discussion is presented on the potential applications of data analytics in several important factors of COVID-19.

title: Trends in Transmissibility of 2019 Novel Coronavirus-infected Pneumonia in Wuhan and 29 Provinces in China
abstract: The 2019 novel coronavirus infected pneumonia (COVID- 19) represents a significant public health threat. The COVID-19 emerged in December 2019 in Wuhan, China and rapidly spread to other regions and countries. The variation in transmission patterns and disease spread in regard to time or among different locations, partially reflecting the public health in- tervention effects, remains to be quantified. As most transmissibility-related epidemic parameters are unknown, we sought, with minimal assumptions, to estimate real-time transmissibility and forecast new cases using dynamic modelling.  Using the cases reported from the National Health Commission of China and transportation data, including the total number of travelling hours through railway, airplane, and car outbound from Wuhan, we have built a time-series model to estimate real-time underlying transmission rates of newly generated cases sequentially from January 20, 2020 to Feb 13, 2020 in Wuhan, Hubei province and other 28 provinces in China. We quantified the instantaneous transmission rate and relative reproduction number (R_t) of COVID-19, and evaluated whether public health intervention affected the case transmissibility in each province. Based on the current estimates, we have predicted the trend of disease spread with a high level of certainty.  We estimated that R_t declined from the range of 4 to 5 towards 1 and remained below unity, while there was an initial growth followed by a decline in a shorter period in Hubei and other provinces. The ratio of transmission rates decreased dramatically from January 23 to 27 likely due to the rigorous public health intervention implemented by the government beginning on January 23, 2020. The mean duration of the infectious period was 6 to 9 days. We have predicted the trend of infection sizes which be- came stable in provinces around February 19 to 24, 2020, and the date of containment would be one-week later in Wuhan.  Public health interventions implemented at both the so- cial and personal levels are effective in preventing outbreaks of COVID-19 in Wuhan and other provinces. Model prediction results suggested that COVID- 19 will be contained around the end of February 2020 in China.  Coronavirus, COVID-19, transmissibility, dynamic reproduction number R, statistical modelling, pneumonia outbreak

title: A systematic review of MERS-CoV seroprevalence and RNA prevalence in dromedary camels: Implications for animal vaccination
abstract: Human infection with Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is driven by recurring dromedary-to-human spill-over events, leading decision-makers to consider dromedary vaccination. Dromedary vaccine candidates in the development pipeline are showing hopeful results, but gaps in our understanding of the epidemiology of MERS-CoV in dromedaries must be addressed to design and evaluate potential vaccination strategies. We aim to bring together existing measures of MERS-CoV infection in dromedary camels to assess the distribution of infection, highlighting knowledge gaps and implications for animal vaccination. We systematically reviewed the published literature on MEDLINE, EMBASE and Web of Science that reported seroprevalence and/or prevalence of active MERS-CoV infection in dromedary camels from both cross-sectional and longitudinal studies. 60 studies met our eligibility criteria. Qualitative syntheses determined that MERS-CoV seroprevalence increased with age up to 80–100% in adult dromedaries supporting geographically widespread endemicity of MERS-CoV in dromedaries in both the Arabian Peninsula and countries exporting dromedaries from Africa. The high prevalence of active infection measured in juveniles and at sites where dromedary populations mix should guide further investigation – particularly of dromedary movement – and inform vaccination strategy design and evaluation through mathematical modelling.

title: Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France
abstract: SARS-Cov-2 virus has spread over the world creating one of the fastest pandemics ever. The absence of immunity, asymptomatic transmission, and the relatively high level of virulence of the COVID-19 infection it causes led to a massive flow of patients in intensive care units (ICU). This unprecedented situation calls for rapid and accurate mathematical models to best inform public health policies. We develop an original parsimonious model that accounts for the effect of the age of infection on the natural history of the disease. Analysing the ongoing COVID-19 in France, we estimate the value of the key epidemiological parameters, such as the basic reproduction number (R0), and the efficiency of the national control strategy. We then use our deterministic model to explore several scenarios posterior to lock-down lifting and compare the efficiency of non pharmaceutical interventions (NPI) described in the literature.

title: The First 75 Days of Novel Coronavirus (SARS-CoV-2) Outbreak: Recent Advances, Prevention, and Treatment
abstract: The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously known as 2019-nCoV) outbreak has engulfed an unprepared world amidst a festive season. The zoonotic SARS-CoV-2, believed to have originated from infected bats, is the seventh member of enveloped RNA coronavirus. Specifically, the overall genome sequence of the SARS-CoV-2 is 96.2% identical to that of bat coronavirus termed BatCoV RaTG13. Although the current mortality rate of 2% is significantly lower than that of SARS (9.6%) and Middle East respiratory syndrome (MERS) (35%), SARS-CoV-2 is highly contagious and transmissible from human to human with an incubation period of up to 24 days. Some statistical studies have shown that, on average, one infected patient may lead to a subsequent 5.7 confirmed cases. Since the first reported case of coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 on December 1, 2019, in Wuhan, China, there has been a total of 60,412 confirmed cases with 1370 fatalities reported in 25 different countries as of February 13, 2020. The outbreak has led to severe impacts on social health and the economy at various levels. This paper is a review of the significant, continuous global effort that was made to respond to the outbreak in the first 75 days. Although no vaccines have been discovered yet, a series of containment measures have been implemented by various governments, especially in China, in the effort to prevent further outbreak, whilst various medical treatment approaches have been used to successfully treat infected patients. On the basis of current studies, it would appear that the combined antiviral treatment has shown the highest success rate. This review aims to critically summarize the most recent advances in understanding the coronavirus, as well as the strategies in prevention and treatment.

title: Analyzing the epidemiological outbreak of COVID-19: A visual exploratory data analysis approach
abstract: There is an obvious concern globally regarding the fact about the emerging coronavirus 2019 novel coronavirus (2019-nCoV) as a worldwide public health threat. As the outbreak of COVID-19 causes by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) progresses within China and beyond, rapidly available epidemiological data are needed to guide strategies for situational awareness and intervention. The recent outbreak of pneumonia in Wuhan, China, caused by the SARS-CoV-2 emphasizes the importance of analyzing the epidemiological data of this novel virus and predicting their risks of infecting people all around the globe. In this study, we present an effort to compile and analyze epidemiological outbreak information on COVID-19 based on the several open datasets on 2019-nCoV provided by the Johns Hopkins University, World Health Organization, Chinese Center for Disease Control and Prevention, National Health Commission, and DXY. An exploratory data analysis with visualizations has been made to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China. Overall, at the outset of an outbreak like this, it is highly important to readily provide information to begin the evaluation necessary to understand the risks and begin containment activities.

title: Chapter 21 In Silico Models From Simple Networks to Complex Diseases
abstract: In this chapter, we consider in silico modeling of diseases starting from some simple to some complex (and mathematical) concepts. Examples and applications of in silico modeling for some important categories of diseases (such as for cancers, infectious diseases, and neuronal diseases) are also given.

title: Evaluating new evidence in the early dynamics of the novel coronavirus COVID-19 outbreak in Wuhan, China with real time domestic traffic and potential asymptomatic transmissions
abstract: The novel coronavirus (COVID-19), first detected in Wuhan, China in December 2019, has spread to 28 countries/regions with over 43,000 confirmed cases. Much about this outbreak is still unknown. At this early stage of the epidemic, it is important to investigate alternative sources of information to understand its dynamics and spread. With updated real time domestic traffic, this study aims to integrate recent evidence of international evacuees extracted from Wuhan between Jan. 29 and Feb. 2, 2020 to infer the dynamics of the COVD-19 outbreak in Wuhan. In addition, a modified SEIR model was used to evaluate the empirical support for the presence of asymptomatic transmissions. Based on the data examined, this study found little evidence for the presence of asymptomatic transmissions. However, it is still too early to rule out its presence conclusively due to sample size and other limitations. The updated basic reproductive number was found to be 2.12 on average with a 95% credible interval of [2.04, 2.18]. It is smaller than previous estimates probably because the new estimate factors in the social and non-pharmaceutical mitigation implemented in Wuhan through the evacuee dataset. Detailed predictions of infected individuals exported both domestically and internationally were produced. The estimated case confirmation rate has been low but has increased steadily to 23.37% on average. The findings of this study depend on the validity of the underlying assumptions, and continuing work is needed, especially in monitoring the current infection status of Wuhan residents.

title: Quest: Practical and Oblivious Mitigation Strategies for COVID-19 using WiFi Datasets
abstract: Contact tracing has emerged as one of the main mitigation strategies to prevent the spread of pandemics such as COVID-19. Recently, several efforts have been initiated to track individuals, their movements, and interactions using technologies, e.g., Bluetooth beacons, cellular data records, and smartphone applications. Such solutions are often intrusive, potentially violating individual privacy rights and are often subject to regulations (e.g., GDPR and CCPR) that mandate the need for opt-in policies to gather and use personal information. In this paper, we introduce Quest, a system that empowers organizations to observe individuals and spaces to implement policies for social distancing and contact tracing using WiFi connectivity data in a passive and privacy-preserving manner. The goal is to ensure the safety of employees and occupants at an organization, while protecting the privacy of all parties. Quest incorporates computationally- and information-theoretically-secure protocols that prevent adversaries from gaining knowledge of an individual's location history (based on WiFi data); it includes support for accurately identifying users who were in the vicinity of a confirmed patient, and then informing them via opt-in mechanisms. Quest supports a range of privacy-enabled applications to ensure adherence to social distancing, monitor the flow of people through spaces, identify potentially impacted regions, and raise exposure alerts. We describe the architecture, design choices, and implementation of the proposed security/privacy techniques in Quest. We, also, validate the practicality of Quest and evaluate it thoroughly via an actual campus-scale deployment at UC Irvine over a very large dataset of over 50M tuples.

title: Guide to Understanding the 2019 Novel Coronavirus

title: Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks
abstract: The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which however is less sensitive to identify the virus at the initial stage. Hence, a more robust and alternate diagnosis technique is desirable. Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. These datasets have limited samples concerned with the positive COVID-19 cases, which raise the challenge for unbiased learning. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. Accuracy, precision, recall, loss, and area under the curve (AUC) are utilized to evaluate the performance of the models. Considering the experimental results, the performance of each model is scenario dependent; however, NASNetLarge displayed better scores in contrast to other architectures, which is further compared with other recently proposed approaches. This article also added the visual explanation to illustrate the basis of model classification and perception of COVID-19 in CXR images.

title: On the reliability of model-based predictions in the context of the current COVID epidemic event: impact of outbreak peak phase and data paucity
abstract: The pandemic spread of the COVID-19 virus has, as of 20th of April 2020, reached most countries of the world. In an effort to design informed public health policies, many modelling studies have been performed to predict crucial outcomes of interest, including ICU solicitation, cumulated death counts, etc... The corresponding data analyses however, mostly rely on restricted (openly available) data sources, which typically include daily death rates and confirmed COVID cases time series. In addition, many of these predictions are derived before the peak of the outbreak has been observed yet (as is still currently the case for many countries). In this work, we show that peak phase and data paucity have a substantial impact on the reliability of model predictions. Although we focus on a recent model of the COVID pandemics, our conclusions most likely apply to most existing models, which are variants of the so-called 'Susceptible-Infected-Removed' or SIR framework. Our results highlight the need for performing systematic reliability evaluations for all models that currently inform public health policies. They also motivate a plea for gathering and opening richer and more reliable data time series (e.g., ICU occupancy, negative test rates, social distancing commitment reports, etc).

title: Novel Coronavirus Disease 2019 (COVID-19): An Emerging Infectious Disease in the 21st Century
abstract: At the beginning of the New Year 2020, China alerted the world health organization (WHO) to a cluster of unusual pneumonia cases in Wuhan After extensive speculation, eventually a new species of coronavirus introduced as the causative pathogen of the disease Coronavirus disease 2019 (COVID-19) is a name for the disease, and the virus that causes it is known SARS-CoV-2 The very rapid spread of the COVID-19 in China and in many other countries has caused fear among people across the world The novel coronavirus outbreak declared a Public Health Emergency of International Concern on 30 January 2020  Several databases such as PubMed, Scopus, Google scholar, and BioRxiv were searched for publications reporting on the novel coronavirus up to 29 February 2020 Literature searches were performed using keywords including “Coronavirus 2019”, “2019-nCoV”, “COVID-19”, and “SARS-CoV-2” Moreover, websites such as the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) were searched to retrieve updated data and statistics regarding the novel coronavirus We extracted data on the epidemiology, pathogenesis, virology, clinical manifestations, transmission routes, diagnosis, treatment, and prevention measures  From the 1416 articles identified in the initial search, 53 were remained after title and abstract screening After full-text review, 37 articles were eligible to include in our study Incubation period for COVID-19 is between 2-10 days, according to the World Health Organization (WHO) The case fatality rate in patients infected with SARC-CoV-2 is 4 3%, and the results indicate that the mortality is higher in elderly individuals and patients with chronic conditions including patients with coronary artery disease, diabetes, chronic pulmonary disease, and hypertension The mortality rate in healthy subjects is less than 1%  The outbreak caused by the novel coronavirus is larger than the previous human coronaviruses, showing that the SARS-CoV-2 is an extremely contagious virus However, the mortality rate of COVID-19 is lower than that of other coronaviruses diseases such as SARS or MERS and other viruses like HIV and Ebola Currently, due to the lack of an effective treatment and vaccine, the best way to deal with the COVID-19 disease is to prevent transmission and spread of the virus and to execute personal protective measures

title: Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders
abstract: The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of this study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, the results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.

title: Document Classification for COVID-19 Literature
abstract: The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields. We provide an analysis of several multi-label document classification models on the LitCovid dataset, a growing collection of 8,000 research papers regarding the novel 2019 coronavirus. We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that the BioBERT and novel Longformer models surpass all others with almost equivalent micro-F1 and accuracy scores of around 81% and 69% on the test set. We evaluate the data efficiency and generalizability of these models as essential features of any system prepared to deal with an urgent situation like the current health crisis. Finally, we explore 50 errors made by the best performing models on LitCovid documents and find that they often (1) correlate certain labels too closely together and (2) fail to focus on discriminative sections of the articles; both of which are important issues to address in future work. Both data and code are available on GitHub.

title: Identification of a Pangolin Niche for a 2019-nCoV-like Coronavirus via an Extensive Meta-metagenomic Search
abstract: In numerous instances, tracking the biological significance of a nucleic acid sequence can be augmented through the identification of environmental niches in which the sequence of interest is present. Many metagenomic datasets are now available, with deep sequencing of samples from diverse biological niches. While any individual metagenomic dataset can be readily queried using web-based tools, meta-searches through all such datasets are less accessible. In this brief communication, we demonstrate such a meta-meta-genomic approach, examining close matches to the Wuhan coronavirus 2019-nCoV in all high-throughput sequencing datasets in the NCBI Sequence Read Archive accessible with the keyword “virome”. In addition to the homology to bat coronaviruses observed in descriptions of the 2019-nCoV sequence (F. Wu et al. 2020,  P. Zhou et al. 2020, ), we note a strong homology to numerous sequence reads in a metavirome dataset generated from the lungs of deceased Pangolins reported by Liu et al. (Viruses 11:11, 2019, http://doi.org/10.3390/v11110979). Our observations are relevant to discussions of the derivation of 2019-nCoV and illustrate the utility and limitations of meta-metagenomic search tools in effective and rapid characterization of potentially significant nucleic acid sequences. Meta-metagenomic searches allow for high-speed, low-cost identification of potentially significant biological niches for sequences of interest.

title: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks
abstract: On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.

title: CORD19STS: COVID-19 Semantic Textual Similarity Dataset
abstract: In order to combat the COVID-19 pandemic, society can benefit from various natural language processing applications, such as dialog medical diagnosis systems and information retrieval engines calibrated specifically for COVID-19. These applications rely on the ability to measure semantic textual similarity (STS), making STS a fundamental task that can benefit several downstream applications. However, existing STS datasets and models fail to translate their performance to a domain-specific environment such as COVID-19. To overcome this gap, we introduce CORD19STS dataset which includes 13,710 annotated sentence pairs collected from COVID-19 open research dataset (CORD-19) challenge. To be specific, we generated one million sentence pairs using different sampling strategies. We then used a finetuned BERT-like language model, which we call Sen-SCI-CORD19-BERT, to calculate the similarity scores between sentence pairs to provide a balanced dataset with respect to the different semantic similarity levels, which gives us a total of 32K sentence pairs. Each sentence pair was annotated by five Amazon Mechanical Turk (AMT) crowd workers, where the labels represent different semantic similarity levels between the sentence pairs (i.e. related, somewhat-related, and not-related). After employing a rigorous qualification tasks to verify collected annotations, our final CORD19STS dataset includes 13,710 sentence pairs.

title: SICE: an improved missing data imputation technique
abstract: In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these data is a major concern to the stakeholders, efficiently handling missing values becomes more important. In this paper, we have proposed a new technique for missing data imputation, which is a hybrid approach of single and multiple imputation techniques. We have proposed an extension of popular Multivariate Imputation by Chained Equation (MICE) algorithm in two variations to impute categorical and numeric data. We have also implemented twelve existing algorithms to impute binary, ordinal, and numeric missing values. We have collected sixty-five thousand real health records from different hospitals and diagnostic centers of Bangladesh, maintaining the privacy of data. We have also collected three public datasets from the UCI Machine Learning Repository, ETH Zurich, and Kaggle. We have compared the performance of our proposed algorithms with existing algorithms using these datasets. Experimental results show that our proposed algorithm achieves 20% higher F-measure for binary data imputation and 11% less error for numeric data imputations than its competitors with similar execution time.

title: Is Working From Home The New Norm? An Observational Study Based on a Large Geo-tagged COVID-19 Twitter Dataset
abstract: As the COVID-19 pandemic swept over the world, people discussed facts, expressed opinions, and shared sentiments on social media. Since the reaction to COVID-19 in different locations may be tied to local cases, government regulations, healthcare resources and socioeconomic factors, we curated a large geo-tagged Twitter dataset and performed exploratory analysis by location. Specifically, we collected 650,563 unique geo-tagged tweets across the United States (50 states and Washington, D.C.) covering the date range from January 25 to May 10, 2020. Tweet locations enabled us to conduct region-specific studies such as tweeting volumes and sentiment, sometimes in response to local regulations and reported COVID-19 cases. During this period, many people started working from home. The gap between workdays and weekends in hourly tweet volumes inspired us to propose algorithms to estimate work engagement during the COVID-19 crisis. This paper also summarizes themes and topics of tweets in our dataset using both social media exclusive tools (i.e., #hashtags, @mentions) and the latent Dirichlet allocation model. We welcome requests for data sharing and conversations for more insights.

title: An interactive web-based dashboard to track COVID-19 in real time

title: Advances in disease diagnosis, vaccine development and other emerging methods to control pathogens in aquaculture
abstract: Disease is still regarded as a major constraint to aquaculture production globally. Rapid disease diagnosis and vaccination play a huge part in the control of bacterial diseases, and there has been significant progress in both of these areas. This chapter considers the limitations of existing methods and reviews recent advances made in pathogen detection technologies and vaccine development methodologies. Future directions are discussed, including nanotechnology and reversed vaccinology.

title: Evidence of Increasing Diversification of Emerging SARS‐CoV‐2 Strains
abstract: On January 30th, 2020, an outbreak of atypical pneumonia caused by a novel Betacoronavirus (βCoV), named SARS‐CoV‐2, was declared a public health emergency of international concern by the World Health Organization. For this reason, a detailed evolutionary analysis of SARS‐CoV‐2 strains currently circulating in different geographic regions of the world was performed.  A compositional analysis as well as a Bayesian coalescent analysis of complete genome sequences of SARS‐CoV‐2 strains recently isolated in Europe, North America, South America and Asia was performed.  The results of these studies revealed a diversification of SARS‐CoV‐2 strains in three different genetic clades. Co‐circulation of different clades in different countries, as well as different genetic lineages within different clades were observed. The time of the most recent common ancestor (tMRCA) was established to be around November 1, 2019. A mean rate of evolution of 6.57 x 10(‐4) substitutions per site per year was found. A significant migration rate per genetic lineage per year from Europe to South America was also observed.  The results of these studies revealed an increasing diversification of SARS‐CoV‐2 strains. High evolutionary rates and fast population growth characterizes the population dynamics of SARS‐CoV‐2 strains. This article is protected by copyright. All rights reserved.

title: Early dynamics of transmission and control of COVID-19: a mathematical modelling study
abstract: An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95â333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced.  We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020.  We estimated that the median daily reproduction number (Rt) in Wuhan declined from 2·35 (95% CI 1·15-4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41-2·39) 1 week after. Based on our estimates of Rt, assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population.  Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually.

title: Effect of Cancer on Clinical Outcomes of Patients With COVID-19: A Meta-Analysis of Patient Data
abstract: Whether cancer is associated with worse prognosis among patients with COVID-19 is unknown. We aimed to quantify the effect (if any) of the presence as opposed to absence of cancer on important clinical outcomes of patients with COVID-19 by carrying out a systematic review and meta-analysis.  We systematically searched PubMed, medRxiv, COVID-19 Open Research Dataset (CORD-19), and references of relevant articles up to April 27, 2020, to identify observational studies comparing patients with versus without cancer infected with COVID-19 and to report on mortality and/or need for admission to the intensive care unit (ICU). We calculated pooled risk ratios (RR) and 95% CIs with a random-effects model. The meta-analysis was registered with PROSPERO (CRD42020181531).  A total of 32 studies involving 46,499 patients (1,776 patients with cancer) with COVID-19 from Asia, Europe, and the United States were included. All-cause mortality was higher in patients with versus those without cancer (2,034 deaths; RR, 1.66; 95% CI, 1.33 to 2.07; P < .0001; 8 studies with 37,807 patients). The need for ICU admission was also more likely in patients with versus without cancer (3,220 events; RR, 1.56; 95% CI, 1.31 to 1.87; P < .0001; 26 studies with 15,375 patients). However, in a prespecified subgroup analysis of patients > 65 years of age, all-cause mortality was comparable between those with versus without cancer (915 deaths; RR, 1.06; 95% CI, 0.79 to 1.41; P = .71; 8 studies with 5,438 patients).  The synthesized evidence suggests that cancer is associated with worse clinical outcomes among patients with COVID-19. However, elderly patients with cancer may not be at increased risk of death when infected with COVID-19. These findings may inform discussions of clinicians with patients about prognosis and may guide health policies.

title: Quantifying transmission of emerging zoonoses: Using mathematical models to maximize the value of surveillance data
abstract: Understanding and quantifying the transmission of zoonotic pathogens is essential for directing public health responses, especially for pathogens capable of transmission between humans. However, determining a pathogen’s transmission dynamics is complicated by challenges often encountered in zoonotic disease surveillance, including unobserved sources of transmission (both human and zoonotic), limited spatial information, and unknown scope of surveillance. In this work, we present a model-based inference method that addresses these challenges for subcritical zoonotic pathogens using a spatial model with two levels of mixing. After demonstrating the robustness of the method using simulation studies, we apply the new method to a dataset of human monkeypox cases detected during an active surveillance program from 1982-1986 in the Democratic Republic of the Congo (DRC). Our results provide estimates of the reproductive number and spillover rate of monkeypox during this surveillance period and suggest that most human-to-human transmission events occur over distances of 30km or less. Taking advantage of contact-tracing data available for a subset of monkeypox cases, we find that around 80% of contact-traced links could be correctly recovered from transmission trees inferred using only date and location. Our results highlight the importance of identifying the appropriate spatial scale of transmission, and show how even imperfect spatiotemporal data can be incorporated into models to obtain reliable estimates of human-to-human transmission patterns. Surveillance datasets are often the only sources of information about the ecology and epidemiology of zoonotic infectious diseases. Methods that can extract as much information as possible from these datasets therefore provide a key advantage for informing our understanding of the disease dynamics and improving our ability to choose the optimal intervention strategy. We developed and tested a likelihood-based inference method based on a mechanistic model of the spillover and human-to-human transmission processes. We first used simulated datasets to explore which information about the disease dynamics of a subcritical zoonotic pathogen could be successfully extracted from a line-list surveillance dataset with non-localized spatial information and unknown geographic coverage. We then applied the method to a dataset of human monkeypox cases detected during an active surveillance program in the Democratic Republic of the Congo between 1982 and 1986 to obtain estimates of the reproductive number, spillover rate, and spatial dispersal of monkeypox in humans.

title: From China: hope and lessons for COVID-19 control

title: An Exploratory Characterization of Bugs in COVID-19 Software Projects
abstract: The dire consequences of the COVID-19 pandemic has influenced development of COVID-19 software i.e., software used for analysis and mitigation of COVID-19. Bugs in COVID-19 software can be consequential, as COVID-19 software projects can impact public health policy and user data privacy.  The goal of this paper is to help practitioners and researchers improve the quality of COVID-19 software through an empirical study of open source software projects related to COVID-19.  We use 129 open source COVID-19 software projects hosted on GitHub to conduct our empirical study. Next, we apply qualitative analysis on 550 bug reports from the collected projects to identify bug categories.  We identify 8 bug categories, which include data bugs i.e., bugs that occur during mining and storage of COVID-19 data. The identified bug categories appear for 7 categories of software projects including (i) projects that use statistical modeling to perform predictions related to COVID-19, and (ii) medical equipment software that are used to design and implement medical equipment, such as ventilators.  Based on our findings, we advocate for robust statistical model construction through better synergies between data science practitioners and public health experts. Existence of security bugs in user tracking software necessitates development of tools that will detect data privacy violations and security weaknesses.

title: TWIRLS, an automated topic-wise inference method based on massive literature, suggests a possible mechanism via ACE2 for the pathological changes in the human host after coronavirus infection
abstract: Faced with the current large-scale public health emergency, collecting, sorting, and analyzing biomedical information related to the "coronavirus" should be done as quickly as possible to gain a global perspective, which is a basic requirement for strengthening epidemic control capacity. However, for human researchers studying the viruses and the hosts, the vast amount of information available cannot be processed effectively and in a timely manner, particularly when the scientific understanding may be limited, which can further lower the information processing efficiency. We present TWIRLS, a method that can automatically acquire, organize, and classify information. Additionally, independent functional data sources can be added to build an inference system using a machine-based approach, which can provide relevant knowledge to help human researchers quickly establish subject cognition and to make more effective decisions. TWIRLS can automatically analyze more than three million words in more than 14,000 literature articles in only 4 hours. Combining with generalized gene interaction databases creates a data interface that can help researchers to further analyze the information. Using the TWIRLS system, we found that an important regulatory factor angiotensin-converting enzyme 2 (ACE2) may be involved in the host pathological changes on binding to the coronavirus after infection. After triggering functional changes in ACE2/AT2R, an imbalance in the steady-state cytokine regulatory axis involving the Renin-Angiotensin System and IP-10 leads to a cytokine storm.

title: Repositioning of 8565 existing drugs for COVID-19
abstract: The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected near 5 million people and led to over 0.3 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than ten years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental dataset for SARS-CoV-2 or SARS-CoV main protease inhibitors. Based on this dataset, we develop validated machine learning models with relatively low root mean square error to screen 1553 FDA-approved drugs as well as other 7012 investigational or off-market drugs in DrugBank. We found that many existing drugs might be potentially potent to SARS-CoV-2. The druggability of many potent SARS-CoV-2 main protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning.

title: Health Belief Model-based Deep Learning Classifiers for Classifying COVID-19 Social Media Content to Examine Public Behaviors towards Physical Distancing
abstract: Public health authorities (PHAs) have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions towards such interventions are to be identified so that PHAs can effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, to understand health behaviors of people.  This study is aimed at developing and evaluating deep learning-based text classification models for classifying social media content posted during the COVID-19 outbreak, using the key four constructs of HBM. We specifically focus on content related to the physical distancing interventions put forth by PHAs. We intend to test the model with a real-world case study.  The dataset for this study was prepared by analyzing Facebook comments which were posted by the public in response to the COVID-19 posts of three PHAs: Ministry of Health of Singapore (MOH), Centers for Disease Control and Prevention (CDC) and Public Health England (PHE). The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated dataset of 16,752 comments, gated recurrent unit (GRU) based recurrent neural network (RNN) models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used for evaluating the model while specificity, sensitivity and balanced accuracy were the test metrics used for evaluating the classification results in the MOH case study.  The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91 and 0.94 for the constructs perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the testing case study with MOH FB comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits while for perceived susceptibility and perceived barriers, it was 79.6% and 81.5%. The classification models were able to accurately predict the trends in the prevalence of the constructs for the examined days in the case study.  The deep learning-based text classifiers developed in this study help in getting an understanding of the public perceptions towards physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize health behaviors of public through the lens of social media. In future studies, we intend to extend the model for studying public perceptions on other important interventions of PHAs.

title: Overlap-Based Undersampling Method for Classification of Imbalanced Medical Datasets
abstract: Early diagnosis of some life-threatening diseases such as cancers and heart is crucial for effective treatments. Supervised machine learning has proved to be a very useful tool to serve this purpose. Historical data of patients including clinical and demographic information is used for training learning algorithms. This builds predictive models that provide initial diagnoses. However, in the medical domain, it is common to have the positive class under-represented in a dataset. In such a scenario, a typical learning algorithm tends to be biased towards the negative class, which is the majority class, and misclassify positive cases. This is known as the class imbalance problem. In this paper, a framework for predictive diagnostics of diseases with imbalanced records is presented. To reduce the classification bias, we propose the usage of an overlap-based undersampling method to improve the visibility of minority class samples in the region where the two classes overlap. This is achieved by detecting and removing negative class instances from the overlapping region. This will improve class separability in the data space. Experimental results show achievement of high accuracy in the positive class, which is highly preferable in the medical domain, while good trade-offs between sensitivity and specificity were obtained. Results also show that the method often outperformed other state-of-the-art and well-established techniques.

title: Charts and Tables

title: COVID-19 Datasets: A Survey and Future Challenges
abstract: In December 2019, a novel virus named as COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as most consequential global crisis after the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. On the other hand, the medical service providers are the first responders for infected persons with severe symptoms of COVID-19. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open-source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open-source efforts that can be mainly categorized as: (a) COVID-19 diagnosis from CT scans and X-ray images, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We review and critically analyze works in these directions that are accompanied by open-source data and code. We hope that the article will provide the scientific community with an initiative to start open-source extensible and transparent research in the collective fight against COVID-19.

title: A Statistical Framework for the Adaptive Management of Epidemiological Interventions
abstract: Epidemiological interventions aim to control the spread of infectious disease through various mechanisms, each carrying a different associated cost.  We describe a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic while simultaneously propagating uncertainty regarding the underlying disease model parameters through to the decision process. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates.  Using simulation studies based on a classic influenza outbreak, we demonstrate the advantages of adaptive interventions over non-adaptive ones, in terms of cost and resource efficiency, and robustness to model misspecification.

title: Virus Pathogen Database and Analysis Resource (ViPR): A Comprehensive Bioinformatics Database and Analysis Resource for the Coronavirus Research Community
abstract: Several viruses within the Coronaviridae family have been categorized as either emerging or re-emerging human pathogens, with Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) being the most well known. The NIAID-sponsored Virus Pathogen Database and Analysis Resource (ViPR, www.viprbrc.org) supports bioinformatics workflows for a broad range of human virus pathogens and other related viruses, including the entire Coronaviridae family. ViPR provides access to sequence records, gene and protein annotations, immune epitopes, 3D structures, host factor data, and other data types through an intuitive web-based search interface. Records returned from these queries can then be subjected to web-based analyses including: multiple sequence alignment, phylogenetic inference, sequence variation determination, BLAST comparison, and metadata-driven comparative genomics statistical analysis. Additional tools exist to display multiple sequence alignments, view phylogenetic trees, visualize 3D protein structures, transfer existing reference genome annotations to new genomes, and store or share results from any search or analysis within personal private ‘Workbench’ spaces for future access. All of the data and integrated analysis and visualization tools in ViPR are made available without charge as a service to the Coronaviridae research community to facilitate the research and development of diagnostics, prophylactics, vaccines and therapeutics against these human pathogens.

title: Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study
abstract: As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks.  In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time.  We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020.  News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.

title: Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing.

title: COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patients
abstract: Corona Virus Disease 2019 (COVID-19) is spreading worldwide. Effective screening for patients is important to limit the epidemic. However, some defects make the currently applied diagnosis methods are still not very ideal for early warning of patients. We aimed to develop a diagnostic model that allows for the quick screening of highly suspected patients using easy-to-get variables.  A total of 1,311 patients receiving severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleicacid detection were included, whom with a positive result were classified into COVID-19 group. Multivariate logistic regression analyses were performed to construct the diagnostic model. Receiver operating characteristic (ROC) curve analysis were used for model validation.  After analysis, signs of pneumonia on CT, history of close contact, fever, neutrophil-to-lymphocyte ratio (NLR), Tmax and sex were included in the diagnostic model. Age and meaningful respiratory symptoms were enrolled into COVID-19 early warning score (COVID-19 EWS). The areas under the ROC curve (AUROC) indicated that both of the diagnostic model (training dataset 0.956 [95%CI 0.935-0.977, P < 0.001]; validation dataset 0.960 [95%CI 0.919-1.0, P < 0.001] ) and COVID-19 EWS (training dataset 0.956 [95%CI 0.934-0.978, P < 0.001] ; validate dataset 0.966 [95%CI 0.929-1, P < 0.001]) had good discrimination capacity. In addition, we also obtained the cut-off values of disease severity predictors, such as CT score, CD8+ T cell count, CD4+ T cell count, and so on.  The new developed COVID-19 EWS was a considerable tool for early and relatively accurately warning of SARS-CoV-2 infected patients.

title: Ethnicity and COVID-19: an urgent public health research priority

title: Epidemic Surveillance of Covid-19: Considering Uncertainty and Under-Ascertainment
abstract: Epidemic surveillance is a fundamental part of public health practice. Addressing under-ascertainment of cases is relevant in most surveillance systems, especially in pandemics of new diseases with a large spectrum of clinical presentations as it may influence timings of policy implementation and public risk perception. From this perspective, this article presents and discusses early evidence on under-ascertainment of COVID-19 and its motifs, options for surveillance, and reflections around their importance to tailor public health measures. In the case of COVID-19, systematically addressing and estimating under-ascertainment of cases is essential to tailor timely public health measures, and communicating these findings is of the utmost importance for policy making and public perception.

title: Predicting Health Disparities in Regions at Risk of Severe Illness to inform Healthcare Resource Allocations during Pandemics
abstract: Pandemics including COVID-19 have disproportionately affected socioeconomically vulnerable populations. To create a repeatable modelling process to identify regional population centers with pandemic vulnerability, readily available COVID-19 and socioeconomic variable datasets were compiled, and linear regression models were built during the early days of the COVID-19 pandemic. The models were validated later in the pandemic timeline using actual COVID-19 mortality rates in states with high population densities, with New York, New Jersey, Connecticut, Massachusetts, Louisiana, Michigan and Pennsylvania showing the strongest predictive results. Our models have been shared with the Department of Health Commissioners of each of these states as input into a much needed pandemic playbook for local healthcare agencies in allocating medical testing and treatment resources.

title: Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea
abstract: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations.  Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak.  Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources.  The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word "Coronavirus" communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers' attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852).  Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

title: Feeling Like It is Time to Reopen Now? COVID-19 New Normal Scenarios based on Reopening Sentiment Analytics
abstract: The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research identifies emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to COVID-19, to identify dominant sentiment trends associated with the push to 'reopen' the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more trust sentiment support, than fear, for reopening the US economy. With additional validation, this could present a valuable time sensitive opportunity for state governments, the federal government, corporations and societal leaders to guide the nation into a successful new normal future.

title: Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications
abstract: Since the recent COVID-19 outbreak, there has been an avalanche of research papers applying deep learning based image processing to chest radiographs for detection of the disease. To test the performance of the two top models for CXR COVID-19 diagnosis on external datasets to assess model generalizability.  In this paper, we present our argument regarding the efficiency and applicability of existing deep learning models for COVID-19 diagnosis. We provide results from two popular models - COVID-Net and CoroNet evaluated on three publicly available datasets and an additional institutional dataset collected from EMORY Hospital between January and May 2020, containing patients tested for COVID-19 infection using RT-PCR.  There is a large false positive rate (FPR) for COVID-Net on both ChexPert (55.3%) and MIMIC-CXR (23.4%) dataset. On the EMORY Dataset, COVID-Net has 61.4% sensitivity, 0.54 F1-score and 0.49 precision value. The FPR of the CoroNet model is significantly lower across all the datasets as compared to COVID-Net - EMORY(9.1%), ChexPert (1.3%), ChestX-ray14 (0.02%), MIMIC-CXR (0.06%).  The models reported good to excellent performance on their internal datasets, however we observed from our testing that their performance dramatically worsened on external data. This is likely from several causes including overfitting models due to lack of appropriate control patients and ground truth labels. The fourth institutional dataset was labeled using RT-PCR, which could be positive without radiographic findings and vice versa. Therefore, a fusion model of both clinical and radiographic data may have better performance and generalization.

title: TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING
abstract: Decision scores and ethically mindful algorithms are being established to adjudicate mechanical ventilation in the context of potential resources shortage due to the current onslaught of COVID-19 cases. There is a need for a reproducible and objective method to provide quantitative information for those scores.  Towards this goal, we present a retrospective study testing the ability of a deep learning algorithm at extracting features from chest x-rays (CXR) to track and predict radiological evolution.  We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from two open-source datasets (last accessed on April 9, 2020)(Italian Society for Medical and Interventional Radiology and MILA). Data collected form 60 pairs of sequential CXRs from 40 COVID patients (mean age +/- standard deviation: 56 +/- 13 years; 23 men, 10 women, seven not reported) and were categorized in three categories: Worse, Stable, or Improved on the basis of radiological evolution ascertained from images and reports. Receiver operating characteristic analyses, Mann-Whitney tests were performed.  On patients from the CheXnet dataset, the area under ROC curves ranged from 0.71 to 0.93 for seven imaging features and one diagnosis. Deep learning features between Worse and Improved outcome categories were significantly different for three radiological signs and one diagnostic (Consolidation, Lung Lesion, Pleural Effusion and Pneumonia; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between Worse and Improved cases with 82.7% accuracy.  CXR deep learning features show promise for classifying the disease trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

title: COVID-19 serial interval estimates based on confirmed cases in public reports from 86 Chinese cities
abstract: As a novel coronavirus (COVID-19) continues to spread widely and claim lives worldwide, its transmission characteristics remain uncertain. Here, we present and analyze the serial intervals–the time period between the onset of symptoms in an index (infector) case and the onset of symptoms in a secondary (infectee) case–of 339 confirmed cases of COVID-19 identified from 264 cities in mainland China prior to February 19, 2020. Here, we provide the complete dataset in both English and Chinese to support further COVID-19 research and modeling efforts.

title: Celebrating TB day at the time of COVID-19
abstract: March 24th, the tuberculosis (TB) day, is the global call to raise awareness, celebrate successes and remind the world that TB is still the N°1 killer among the infectious causes of death . TB is preventable and curable disease, and its control is a highly cost-effective health intervention. However, diagnostic delay and inadequate treatment contribute to the severity and mortality of the disease as well as the risk of transmission and development of drug resistance. Despite the fact that TB disproportionately impacts low- and middle- income countries, it does not spare any country in the world including those in the European Union/European Economic Area where more than 4000 of people still die for the disease every year . Moreover, the high number of multidrug-resistant (MDR)-TB cases in some countries of the World Health Organization (WHO) European Region poses a growing public health threat. Despite the sheer numbers, TB has for too long been overshadowed by HIV and malaria, and currently by the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for the coronavirus disease 2019 (COVID-19) epidemic.

title: Clinical Characteristics of Hospitalized Covid-19 Patients in New York City
abstract: The coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States.  Demographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive.  A total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2nd, 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL.  This is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.

title: Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning
abstract: The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. Recent outbreak of novel coronavirus infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of COVID-19 will save the life of thousands. In this paper, we devised a machine learning (ML) model to predict the possible inhibitory synthetic antibodies for Corona virus. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, we screened thousands of hypothetical antibody sequences and found 8 stable antibodies that potentially inhibit COVID-19. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit the Corona virus.

title: COVID-19 research in Wikipedia
abstract: Wikipedia is one of the main sources of free knowledge on the Web. During the first few months of the pandemic, over 5,200 new Wikipedia pages on COVID-19 have been created and have accumulated over 400M pageviews by mid June 2020.1 At the same time, an unprecedented amount of scientific articles on COVID-19 and the ongoing pandemic have been published online. Wikipedia’s contents are based on reliable sources, primarily scientific literature. Given its public function, it is crucial for Wikipedia to rely on representative and reliable scientific results, especially so in a time of crisis. We assess the coverage of COVID-19-related research in Wikipedia via citations to a corpus of over 160,000 articles. We find that Wikipedia editors are integrating new research at a fast pace, and have cited close to 2% of the COVID-19 literature under consideration. While doing so, they are able to provide a representative coverage of COVID-19-related research. We show that all the main topics discussed in this literature are proportionally represented from Wikipedia, after accounting for article-level effects. We further use regression analyses to model citations from Wikipedia and show that Wikipedia editors on average rely on literature which is highly cited, widely shared on social media, and has been peer-reviewed.

title: Emergence of COVID-19 Infection: What Is Known and What Is to Be Expected-Narrative Review Article
abstract: The discovery of the coronavirus disease 2019 (COVID-19) during a pneumonia outbreak in Wuhan city (China) has raised a global public health concern, as the city consists of around 11 million people and is considered a major transport and logistics hub. This deadly virus caused the world to be in high alert as the death toll and the number of confirmed cases is continuously rising since the first case was reported. The Chinese government warned that the transmission ability of the virus is increasing, and international efforts are needed to overcome this outbreak. The purpose of this review is to focus on the published articles about the new virus, which will give an insight into the current state of research and data available, as well as recommending future studies.  For this narrative review, more than 20 relevant scientific articles and reports were considered from various databases (e.g., Google Scholar, PubMed and Science Direct) using keywords such as Coronavirus Outbreak, COVID-19, Emerging Epidemics, Emerging Infections, and Novel Coronavirus.  The results from this review show that the situation is rapidly evolving, as human-to-human transmission is occurring, and the number of new cases and mortalities is increasing by the day and on a global level. There is still ambiguity about mutation risks and how the virus spreads as the source was not yet identified. Major gaps in knowledge about the origin of the virus, epidemiology and transmission impose a great challenge, which emphasizes the need for further studies in the future.

title: Selecting Accurate Classifier Models for a MERS-CoV Dataset
abstract: The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a viral respiratory disease that is spreading worldwide necessitating to have an accurate diagnosis system that accurately predicts infections. As data mining classifiers can greatly assist in enhancing the prediction accuracy of diseases in general. In this paper, classifier model performance for two classification types: (1) binary and (2) multi-class were tested on a MERS-CoV dataset that consists of all reported cases in Saudi Arabia between 2013 and 2017. A cross-validation model was applied to measure the accuracy of the Support Vector Machine (SVM), Decision Tree, and k-Nearest Neighbor (k-NN) classifiers. Experimental results demonstrate that SVM and Decision Tree classifiers achieved the highest accuracy of 86.44% for binary classification based on healthcare personnel class. On the other hand, for multiclass classification based on city class, the decision tree classifier had the highest accuracy among the remaining classifiers; although it did not reach a satisfactory accuracy level (42.80%). This work is intended to be a part of a MERS-CoV prediction system to enhance the diagnosis of MERS-CoV disease.

title: Knowledge-based repositioning of the anti-HCV direct antiviral agent Sofosbuvir as SARS-CoV-2 treatment
abstract: The new human coronavirus named SARS-CoV-2 is a positive-sense RNA virus for which no specific drugs are currently available. A knowledge-based analysis strongly suggests a possible repositioning of the anti-HCV direct antiviral agent (DAA) Sofosbuvir as treatment for SARS-CoV-2. Indeed, the RNA-dependent RNA-polymerases (RdRp) of the two viruses show high sequence and structural homology, supporting the likelihood of binding the Sofosbuvir molecule with similar efficiency. Such a repositioning would allow the containment of the SARS-CoV-2 pandemic and limit the progression of disease to potentially deadly COVID19.

title: Expression of SARS-CoV-2 Entry Molecules ACE2 and TMPRSS2 in the Gut of Patients With IBD
abstract: Patients with inflammatory bowel disease (IBD) have intestinal inflammation and are treated with immune-modulating medications. In the face of the coronavirus disease-19 pandemic, we do not know whether patients with IBD will be more susceptible to infection or disease. We hypothesized that the viral entry molecules angiotensin I converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) are expressed in the intestine. We further hypothesized that their expression could be affected by inflammation or medication usage.  We examined the expression of Ace2 and Tmprss2 by quantitative polymerase chain reacion in animal models of IBD. Publicly available data from organoids and mucosal biopsies from patients with IBD were examined for expression of ACE2 and TMPRSS2. We conducted RNA sequencing for CD11b-enriched cells and peripheral and lamina propria T-cells from well-annotated patient samples.  The molecules ACE2 and TMPRSS2 were abundantly expressed in the ileum and colon and had high expression in intestinal epithelial cells. In animal models, inflammation led to downregulation of epithelial Ace2. Expression of ACE2 and TMPRSS2 was not increased in samples from patients with compared with those of control patients. In CD11b-enriched cells but not T-cells, the level of expression of ACE2 and TMPRSS2 in the mucosa was comparable to other functional mucosal genes and was not affected by inflammation. Anti-tumor necrosis factor drugs, vedolizumab, ustekinumab, and steroids were linked to significantly lower expression of ACE2 in CD11b-enriched cells.  The viral entry molecules ACE2 and TMPRSS2 are expressed in the ileum and colon. Patients with IBD do not have higher expression during inflammation; medical therapy is associated with lower levels of ACE2. These data provide reassurance for patients with IBD.

title: Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
abstract: Coronavirus disease (Covid-19) has been the main agenda of the whole world since it came in sight in December 2019. It has already caused thousands of causalities and infected several millions worldwide. Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance. The main tools practitioners currently use to diagnose Covid-19 are Reverse Transcription-Polymerase Chain reaction (RT-PCR) and Computed Tomography (CT), which require significant time, resources and acknowledged experts. X-ray imaging is a common and easily accessible tool that has great potential for Covid-19 diagnosis. In this study, we propose a novel approach for Covid-19 recognition from chest X-ray images. Despite the importance of the problem, recent studies in this domain produced not so satisfactory results due to the limited datasets available for training. Recall that Deep Learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large datasets, such data scarcity can be a crucial obstacle when using them for Covid-19 detection. Alternative approaches such as representation-based classification (collaborative or sparse representation) might provide satisfactory performance with limited size datasets, but they generally fall short in performance or speed compared to Machine Learning methods. To address this deficiency, Convolution Support Estimation Network (CSEN) has recently been proposed as a bridge between model-based and Deep Learning approaches by providing a non-iterative real-time mapping from query sample to ideally sparse representation coefficient' support, which is critical information for class decision in representation based techniques.

title: Syndromic Surveillance

title: A Simple Mathematical Model for Estimating the Inflection Points of COVID-19 Outbreaks
abstract: Exponential-like infection growths leading to peaks (which could be the inflection points or turning points) are usually the hallmarks of infectious disease outbreaks including coronaviruses. To predict the inflection points, i.e., inflection time (Tmax) & maximal infection number (Imax) of the novel coronavirus (COVID-19), we adopted a trial and error strategy and explored a series of approaches from simple logistic modeling (that has an asymptomatic line) to sophisticated tipping point detection techniques for detecting phase transitions but failed to obtain satisfactory results.  Inspired by its success in diversity-time relationship (DTR), we apply the PLEC (power law with exponential cutoff) model for detecting the inflection points of COVID-19 outbreaks. The model was previously used to extend the classic species-time relationship (STR) for general DTR (Ma 2018), and it has two secondary parameters (computed from its 3 parameters including power law scaling parameter w, taper-off parameter d to overwhelm virtually exponential growth ultimately, and a parameter c related to initial infections): one that was originally used for estimating the potential or dark biodiversity is proposed to estimate the maximal infection number (Imax) and another is proposed to determine the corresponding inflection time point (Tmax).  We successfully estimated the inflection points [Imax, Tmax] for most provinces (≈ 85%) in China with error rates <5% in both Imax and Tmax. We also discussed the constraints and limitations of the proposed approach, including (i) sensitive to disruptive jumps, (ii) requiring sufficiently long datasets, and (iii) limited to unimodal outbreaks.

title: COVID-19: Putting the General Data Protection Regulation to the Test
abstract: The coronavirus disease (COVID-19) pandemic is very much a global health issue and requires collaborative, international health research efforts to address it. A valuable source of information for researchers is the large amount of digital health data that are continuously collected by electronic health record systems at health care organizations. The European Union’s General Data Protection Regulation (GDPR) will be the key legal framework with regard to using and sharing European digital health data for research purposes. However, concerns persist that the GDPR has made many organizations very risk-averse in terms of data sharing, even if the regulation permits such sharing. Health care organizations focusing on individual risk minimization threaten to undermine COVID-19 research efforts. In our opinion, there is an ethical obligation to use the research exemption clause of the GDPR during the COVID-19 pandemic to support global collaborative health research efforts. Solidarity is a European value, and here is a chance to exemplify it by using the GDPR regulatory framework in a way that does not hinder but actually fosters solidarity during the COVID-19 pandemic.

title: Lest We Forget: A Dataset of Coronavirus-Related News Headlines in Swiss Media
abstract: We release our COVID-19 news dataset, containing more than 10,000 links to news articles related to the Coronavirus pandemic published in the Swiss media since early January 2020. This collection can prove beneficial in mining and analysis of the reaction of the Swiss media and the COVID-19 pandemic and extracting insightful information for further research. We hope this dataset helps researchers and the public deliver results that will help analyse the pandemic and potentially lead to a better understanding of the events.

title: Initial success in the identification and management of the coronavirus disease 2019 (COVID-19) indicates human-to-human transmission in Wuhan, China
abstract: Coronavirus (CoV) has been one of the major pandemic threats to human health in the last two decades. The human coronavirus was first identified in 1960s. CoVs 229E, NL63, OC43, HKU1, SARS-CoV, and MERS-CoV have caused numerous disasters or human deaths worldwide. Recently, an outbreak of the previously unknown deadly CoV disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome CoV 2 (SARS-CoV-2, early named 2019-nCoV) occurred in Wuhan, China, and it had caused 81238 cases of confirmed infection, including 3250 deaths until March 19, 2020. Its risks and pandemic potential have brought global consideration. We summarized epidemiology, virological characteristics, clinical symptoms, diagnostic methods, clinical treatments, and prevention methods for COVID-19 to present a reference for the future wave of probable CoV outbreaks.

title: Keyword Search over RDF Using Document-Centric Information Retrieval Systems
abstract: For ordinary users, the task of accessing knowledge graphs through structured query languages like SPARQL is rather demanding. As a result, various approaches exploit the simpler and widely used keyword-based search paradigm, either by translating keyword queries to structured queries, or by adopting classical information retrieval (IR) techniques. In this paper, we study and adapt Elasticsearch, an out-of-the-box document-centric IR system, for supporting keyword search over RDF datasets. Contrary to other works that mainly retrieve entities, we opt for retrieving triples, due to their expressiveness and informativeness. We specify the set of functional requirements and study the emerging questions related to the selection and weighting of the triple data to index, and the structuring and ranking of the retrieved results. Finally, we perform an extensive evaluation of the different factors that affect the IR performance for four different query types. The reported results are promising and offer useful insights on how different Elasticsearch configurations affect the retrieval effectiveness and efficiency.

title: Sexual and reproductive health (SRH): a key issue in the emergency response to the coronavirus disease (COVID- 19) outbreak
abstract: The novel coronavirus disease (COVID-19) outbreak was first declared in China in December 2019, and WHO declared the pandemic on 11 March 2020. A fast-rising number of confirmed cases has been observed in all continents, with Europe at the epicentre of the outbreak at this moment. Sexual and reproductive health (SRH) and rights is a significant public health issue during the epidemics. The novel coronavirus (SARS-CoV-2) is new to humans, and only limited scientific evidence is available to identify the impact of the disease COVID-19 on SRH, including clinical presentation and outcomes of the infection during pregnancy, or for persons with STI/HIV-related immunosuppression. Beyond the clinical scope of SRH, we should not neglect the impacts at the health system level and disruptions or interruptions in regular provision of SRH services, such as pre- and postnatal checks, safe abortion, contraception, HIV/AIDS and sexually transmitted infections. Furthermore, other aspects merit attention such as the potential increase of gender-based violence and domestic abuse, and effects of stigma and discrimination associated with COVID-19 and their effects on SRH clients and health care providers. Therefore, there is an urgent need for the scientific community to generate sound clinical, epidemiological, and psycho-social behavioral links between COVID-19 and SRH and rights outcomes.

title: Coronavirus: Update Related to the Current Outbreak of COVID-19
abstract: In December 2019, some cases of viral pneumonia were epidemiologically related to a new coronavirus in the province of Hubei, China. Subsequently, there has been an increase in infections attributable to this virus throughout China and worldwide. The World Health Organization (WHO) has officially named the infection coronavirus disease 2019 (COVID-19), and the virus has been classified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This appears to be a virus from Rhinolophus bats, but the intermediate host has not yet been identified. The mechanism of infection of SARS-CoV-2 is not yet known; it appears to have affinity for cells located in the lower airways, where it replicates. The interhuman transmission of coronaviruses mainly occurs through saliva droplets and direct and indirect contact via surfaces. As of March 10, 2020, the number of cases worldwide was 113,702. Along with severe acute respiratory syndrome (SARS) and Middle Eastern respiratory syndrome (MERS), COVID-19 appears to cause a severe clinical picture in humans, ranging from mild malaise to death by sepsis/acute respiratory distress syndrome. The prognosis is worse in elderly patients with comorbidities. To date, there is no specific therapy for COVID-19. Prevention of SARS-CoV-2 infection implies strategies that limit the spread of the virus. WHO and other international and national bodies have developed continuously updated strategic objectives and provisions to contain the spread of the virus and infection.

title: Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India during COVID-19 Infodemic
abstract: COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people's health and governance systems. Since social media is the largest source of information, managing the infodemic not only requires mitigating of misinformation but also an early understanding of psychological patterns resulting from it. During the COVID-19 crisis, Twitter alone has seen a sharp 45% increase in the usage of its curated events page, and a 30% increase in its direct messaging usage, since March 6th 2020. In this study, we analyze the psychometric impact and coupling of the COVID-19 infodemic with the official bulletins related to COVID-19 at the national and state level in India. We look at these two sources with a psycho-linguistic lens of emotions and quantified the extent and coupling between the two. We modified path, a deep skip-gram based open-sourced lexicon builder for effective capture of health-related emotions. We were then able to capture the time-evolution of health-related emotions in social media and official bulletins. An analysis of lead-lag relationships between the time series of extracted emotions from official bulletins and social media using Granger's causality showed that state bulletins were leading the social media for some emotions such as Medical Emergency. Further insights that are potentially relevant for the policymaker and the communicators actively engaged in mitigating misinformation are also discussed. Our paper also introduces CoronaIndiaDataset2, the first social media based COVID-19 dataset at national and state levels from India with over 5.6 million national and 2.6 million state-level tweets. Finally, we present our findings as COVibes, an interactive web application capturing psychometric insights captured upon the CoronaIndiaDataset, both at a national and state level.

title: Sars and Other Coronaviruses in Humans and Animals

title: Dataset on dynamics of Coronavirus on Twitter
abstract: In this data article, we provide a dataset of 8,982,694 Twitter posts around the coronavirus health global crisis. The data were collected through the Twitter REST API search. We used the rtweet R package to download raw data. The term searched was “Coronavirus” which included the word itself and its hashtag version. We collected the data over 23 days, from January 21 to February 12, 2020. The dataset is multilingual, prevailing English, Spanish, and Portuguese. We include a new variable created from other four variables; it is called “type” of tweets, which is useful for showing the diversity of tweets and the dynamics of users on Twitter. The dataset comprises seven databases which can be analysed separately. On the other hand, they can be crossed to set other researches, among them, trends and relevance of different topics, types of tweets, the embeddedness of users and their profiles, the retweets dynamics, hashtag analysis, as well as to perform social network analysis. This dataset can attract the attention of researchers related to different fields on knowledge, such as data science, social science, network science, health informatics, tourism, infodemiology, and others.

title: A transcriptional regulatory atlas of coronavirus infection of human cells
abstract: Identifying transcriptional responses that are most consistently associated with experimental coronavirus (CoV) infection can help illuminate human cellular signaling pathways impacted by CoV infection. Here, we distilled over 3,000,000 data points from publically archived CoV infection transcriptomic datasets into consensus regulatory signatures, or consensomes, that rank genes based on their transcriptional responsiveness to infection of human cells by MERS, SARS-CoV-1 and SARS-CoV-2 subtypes. We computed overlap between genes with elevated rankings in the CoV consensomes against those from transcriptomic and ChIP-Seq consensomes for nearly 880 cellular signaling pathway nodes. Validating the CoV infection consensomes, we identified robust overlap between their highly ranked genes and high confidence targets of signaling pathway nodes with known roles in CoV infection. We then developed a series of use cases that illustrate the utility of the CoV consensomes for hypothesis generation around mechanistic aspects of the cellular response to CoV infection. We make the CoV infection datasets and their universe of underlying data points freely accessible through the Signaling Pathways Project web knowledgebase at https://www.signalingpathways.org/datasets/index.jsf.

title: Single-Incision Multiport/Single Port Laparoscopic Abdominal Surgery (SILAP): A Prospective Multicenter Observational Quality Study.
abstract: Increasing experience with minimally invasive surgery and the development of new instruments has resulted in a tendency toward reducing the number of abdominal skin incisions. Retrospective and randomized prospective studies could show the feasibility of single-incision surgery without any increased risk to the patient. However, large prospective multicenter observational datasets do not currently exist.  This prospective multicenter observational quality study will provide a relevant dataset reflecting the feasibility and safety of single-incision surgery. This study focuses on external validity, clinical relevance, and the patients' perspective. Accordingly, the single-incision multiport/single port laparoscopic abdominal surgery (SILAP) study will supplement the existing evidence, which does not currently allow evidence-based surgical decision making.  The SILAP study is an international prospective multicenter observational quality study. Mortality, morbidity, complications during surgery, complications postoperatively, patient characteristics, and technical aspects will be monitored. We expect more than 100 surgical centers to participate with 5000 patients with abdominal single-incision surgery during the study period.  Funding was obtained in 2012. Enrollment began on January 01, 2013, and will be completed on December 31, 2018. As of January 2016, 2119 patients have been included, 106 German centers are registered, and 27 centers are very active (>5 patients per year).  This prospective multicenter observational quality study will provide a relevant dataset reflecting the feasibility and safety of single-incision surgery. An international enlargement and recruitment of centers outside of Germany is meaningful.

title: Binding of SARS-CoV-2 and angiotensin-converting enzyme 2: clinical implications

title: Potentially highly potent drugs for 2019-nCoV
abstract: The World Health Organization (WHO) has declared the 2019 novel coronavirus (2019-nCoV) infection outbreak a global health emergency. Currently, there is no effective anti-2019-nCoV medication. The sequence identity of the 3CL proteases of 2019-nCoV and SARS is 96%, which provides a sound foundation for structural-based drug repositioning (SBDR). Based on a SARS 3CL protease X-ray crystal structure, we construct a 3D homology structure of 2019-nCoV 3CL protease. Based on this structure and existing experimental datasets for SARS 3CL protease inhibitors, we develop an SBDR model based on machine learning and mathematics to screen 1465 drugs in the DrugBank that have been approved by the U.S. Food and Drug Administration (FDA). We found that many FDA approved drugs are potentially highly potent to 2019-nCoV.

title: Interferon-Induced Transmembrane Protein (IFITM3) Is Upregulated Explicitly in SARS-CoV-2 Infected Lung Epithelial Cells
abstract: Current guidelines for COVID-19 management recommend the utilization of various repurposed drugs. Despite ongoing research toward the development of a vaccine against SARS-CoV-2, such a vaccine will not be available in time to contribute to the containment of the ongoing pandemic. Therefore, there is an urgent need to develop a framework for the rapid identification of novel targets for diagnostic and therapeutic interventions. We analyzed publicly available transcriptomic datasets of SARS-CoV infected humans and mammals to identify consistent differentially expressed genes then validated in SARS-CoV-2 infected epithelial cells transcriptomic datasets. Comprehensive toxicogenomic analysis of the identified genes to identify possible interactions with clinically proven drugs was carried out. We identified IFITM3 as an early upregulated gene, and valproic acid was found to enhance its mRNA expression as well as induce its antiviral action. These findings indicate that analysis of publicly available transcriptomic and toxicogenomic data represents a rapid approach for the identification of novel targets and molecules that can modify the action of such targets during the early phases of emerging infections like COVID-19.

title: GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information
abstract: The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters. These non-traditional data sources are becoming vital for disease forecasts and surveillance when preparing for epidemic and pandemic outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset containing more than 524 million multilingual tweets posted over a period of 90 days since February 1, 2020. Moreover, we employ a gazetteer-based approach to infer the geolocation of tweets. We postulate that this large-scale, multilingual, geolocated social media data can empower the research communities to evaluate how societies are collectively coping with this unprecedented global crisis as well as to develop computational methods to address challenges such as identifying fake news, understanding communities' knowledge gaps, building disease forecast and surveillance models, among others.

title: FTIR microscopy contribution for comprehension of degradation mechanisms in PLA-based implantable medical devices.
abstract: The integration and evolution of implantable medical devices made of bioresorbable polymers and used for temporary biomedical applications are crucial criteria in the success of a therapy and means of follow-up after implantation are needed. The objective of this work is to develop and evaluate a method based on microscopic Fourier Transform InfraRed spectroscopy (FTIR) mappings to monitor the degradation of such polymers on tissue explant sections, after implantation. This technique provided information on their location and on both their composition and crystallinity, which is directly linked to their state of degradation induced predominantly by chain scissions. An in vitro study was first performed on poly(L-lactic acid) (PLLA) meshes to validate the procedure and the assumption that changes observed on FTIR spectra are indeed a consequence of degradation. Then, mappings of in vivo degraded PLLA meshes were realized to follow up their degradation and to better visualize their degradation mechanisms. This work further warrants its translation to medical implants made of copolymers of lactic acid and to other polyesters.

title: Meteorological factors and domestic new cases of coronavirus disease (COVID-19) in nine Asian cities: A time-series analysis
abstract: To investigate the associations of meteorological factors and the daily new cases of coronavirus disease (COVID-19) in nine Asian cities. METHOD Pearson correlation and generalized additive modeling were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.  The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=-0.565, P<0.01), Shanghai (r=-0.471, P<0.01), and Guangzhou (r=-0.530, P<0.01) , yet in contrast, positively correlated with that in Japan (r=0.441, P<0.01). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), generalized additive modeling analysis showed the number of daily new confirmed cases was positively associated with both average temperature and relative humidity, especially in lagged 3d model, where a positive influence of temperature on the daily new confirmed cases was discerned in 5 cities except in Beijing, Wuhan, Korea, and Malaysia. Nevertheless, the results were inconsistent across cities and lagged time, suggesting meteorological factors were unlikely to greatly influence the COVID-19 epidemic.  The associations between meteorological factors and the number of COVID-19 daily cases are inconsistent across cities and lagged time. Large-scale public health measures and expanded regional research are still required until a vaccine becomes available and herd immunity is established.

title: A Systematic Review of Social Contact Surveys to Inform Transmission Models of Close-contact Infections
abstract: Researchers increasingly use social contact data to inform models for infectious disease spread with the aim of guiding effective policies about disease prevention and control. In this article, we undertake a systematic review of the study design, statistical analyses, and outcomes of the many social contact surveys that have been published.  We systematically searched PubMed and Web of Science for articles regarding social contact surveys. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines as closely as possible.  In total, we identified 64 social contact surveys, with more than 80% of the surveys conducted in high-income countries. Study settings included general population (58%), schools or universities (37%), and health care/conference/research institutes (5%). The largest number of studies did not focus on a specific age group (38%), whereas others focused on adults (32%) or children (19%). Retrospective (45%) and prospective (41%) designs were used most often with 6% using both for comparison purposes. The definition of a contact varied among surveys, e.g., a nonphysical contact may require conversation, close proximity, or both. We identified age, time schedule (e.g., weekday/weekend), and household size as relevant determinants of contact patterns across a large number of studies.  We found that the overall features of the contact patterns were remarkably robust across several countries, and irrespective of the study details. By considering the most common approach in each aspect of design (e.g., sampling schemes, data collection, definition of contact), we could identify recommendations for future contact data surveys that may be used to facilitate comparison between studies.

title: To recover faster from Covid-19, open up: Managerial implications from an open innovation perspective
abstract: Abstract Covid-19 has severely tested our public health systems. Recovering from Covid-19 will soon test our economic systems. Innovation will have an important role to play in recovering from the aftermath of the coronavirus. This article discusses both how to manage innovation as part of that recovery, and also derives some lessons from how we have responded to the virus so far, and what those lessons imply for managing innovation during the recovery.

title: Comparative Global Epidemiological Investigation of SARS-CoV-2 and SARS-CoV Diseases Using Meta-MUMS Tool Through Incidence, Mortality, and Recovery Rates
abstract: COVID-19 is a novel coronavirus that was reported by the world health organization in late December 2019. As an unexplained respiratory disease epidemic, which is similar to respiratory syndrome coronavirus SARS-CoV, it rapidly spread all over the world. The study aims to compare several parameters of COVID-19 and SARS-CoV infectious diseases in terms of incidence, mortality, and recovery rates. The publicly available dataset Worldometer (extracted on April 5, 2020) confirmed by WHO report was available for meta-analysis purposes using the Meta-MUMS tool. And, the reported outcomes of the analysis used a random-effects model to evaluate the event rate, and risk ratios thorough subgroup analysis forest plots. Seventeen countries for COVID-19 and eight countries of SARS infections, including COVID-19 group n = 1124243, and SARS-CoV group n = 8346, were analyzed. In this meta-analysis, a random effect model of relations of incidence, mortality, and recovery rates of COVID-19 and SARS world infections were determined. The meta-analysis and forest plots of two viral world infections showed that the incidence rate of COVID-19 infection is more than SARS infections, while recovery and mortality event rates of SARS-CoV are more than COVID-19 infection. And subgroup analysis showed that the mortality and recovery rates were higher in both SARS-CoV wand COVID-19 in comparison to incidence and mortality rates, respectively. In conclusion, the meta-analysis approach on the abovementioned dataset revealed the epidemiological and statistical analyses for comparing COVID-19 and SARS-CoV outbreaks.

title: Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps
abstract: We describe in this paper an analysis of the spatial evolution of coronavirus pandemic around the world by using a particular type of unsupervised neural network, which is called self-organizing maps. Based on the clustering abilities of self-organizing maps we are able to spatially group together countries that are similar according to their coronavirus cases, in this way being able to analyze which countries are behaving similarly and thus can benefit by using similar strategies in dealing with the spread of the virus. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained, that could be helpful in deciding the best strategies in dealing with this virus. Most of the previous papers dealing with data of the Coronavirus have viewed the problem on temporal aspect, which is also important, but this is mainly concerned with the forecast of the numeric information. However, we believe that the spatial aspect is also important, so in this view the main contribution of this paper is the use of unsupervised self-organizing maps for grouping together similar countries in their fight against the Coronavirus pandemic, and thus proposing that strategies for similar countries could be established accordingly.

title: Insights on early mutational events in SARS-CoV-2 virus reveal founder effects across geographical regions
abstract: Here we aim to describe early mutational events across samples from publicly available SARS-CoV-2 sequences from the sequence read archive repository. Up until March 27, 2020, we downloaded 53 illumina datasets, mostly from China, USA (Washington DC) and Australia (Victoria). Of 30 high quality datasets, 27 datasets (90%) contain at least a single founder mutation and most of the variants are missense (over 63%). Five-point mutations with clonal (founder) effect were found in USA sequencing samples. Sequencing samples from USA in GenBank present this signature with 50% allele frequencies among samples. Australian mutation signatures were more diverse than USA samples, but still, clonal events were found in those samples. Mutations in the helicase and orf1a coding regions from SARS-CoV-2 were predominant, among others, suggesting that these proteins are prone to evolve by natural selection. Finally, we firmly urge that primer sets for diagnosis be carefully designed, since rapidly occurring variants would affect the performance of the reverse transcribed quantitative PCR (RT-qPCR) based viral testing.

title: Tuberculosis and COVID-19 in 2020: lessons from the past viral outbreaks and possible future outcomes
abstract: The threat of contagious infectious diseases is constantly evolving, as demographic explosion, travel globalization and changes in human lifestyle increase the risk of spreading pathogens, leading to accelerated changes in disease landscape. Of particular interest is the aftermath of superimposing viral epidemics (especially SARS-CoV-2) over long-standing diseases, such as tuberculosis (TB), which remains a significant disease for public health worldwide and especially in emerging economies.  PubMed electronic database was requested for relevant articles linking TB, influenza and SARS-CoV viruses and subsequently assessed eligibility according to inclusion criteria. Using a data mining approach, we also queried the COVID-19 Open Research Dataset (CORD-19). We aimed to answer the following questions: What can be learned from other coronavirus outbreaks (with a focus on TB patients)? Is coinfection (TB and SARS-CoV-2) more severe? Is there a vaccine for SARS-CoV-2? How does the TB vaccine affect COVID-19? How does one diagnosis affect the other? Discussions. Few essential elements about TB and SARS-CoV coinfections were discussed. First, lessons from the past outbreaks (other coronaviruses), as well as influenza pandemic / seasonal outbreaks have taught the importance of infection control to avoid the severe impact on TB patients. Second, although challenging due to data scarcity, investigating the pathological pathways linking TB and SARS-CoV-2 leads to the idea that their coexistence might yield a more severe clinical evolution. Finally, we addressed the issues of vaccination and diagnostic reliability in the context of coinfection.  Because viral respiratory infections and TB impede the host's immune responses, it can be assumed that their harmful synergism may contribute to more severe clinical evolution. Despite the rapidly growing number of cases, the data needed to predict the impact of the COVID-19 pandemic on patients with latent TB and TB sequelae still lies ahead.

title: COVID-19 most vulnerable Mexican cities lack the public health infrastructure to face the pandemic: a new temporally-explicit model
abstract: Recently, a wide array of epidemiological models has been developed to guide public health actors in containing the rapid dissemination of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), cause of COVID-19. Despite their usefulness, many epidemiological models recently developed to understand the spread of SARS-CoV-2 and infection rates of COVID-19 fall short as they ignore human mobility, limiting our understanding of the spread of the disease, together with the vulnerability of population centers in a broad scale. We developed a new temporally-explicit model and simulated several social distancing scenarios to predict the vulnerability to COVID-19 of 50 Mexican cities that are interconnected by their air transportation network. Additionally, we assessed the sufficiency of the public health infrastructure in the focal cities to face the pandemic over time. Based on our model, we show that the most important cities within the Mexican air transportation network are the most vulnerable to COVID-19, with all assessed public health infrastructure being insufficient to face the modeled scenario for the pandemic after 100 days. Despite these alarming findings, our results show that social distancing could dramatically decrease the total number of infected people (77% drop-off for the 45% distancing scenario when contrasted with no distancing), flattening the growth of infection rate. Thus, we consider that this study provides useful information that may help decision-makers to timely implement health policies to anticipate and lessen the impact of the current pandemic in Mexico.

title: Review of the 2019 novel coronavirus (SARS-CoV-2) based on current evidence
abstract: COVID-19, the disease caused by SARS-CoV-2, is a highly contagious disease. The World Health Organization has declared the ongoing outbreak to be a global public health emergency. Currently, the research on SARS-CoV-2 is in its primary stages. Based on current published evidence, this review systematically summarizes the epidemiology, clinical characteristics, diagnosis, treatment and prevention of COVID-19. It is hoped that this review will help the public to recognize and deal with SARS-CoV-2, and provide a reference for future studies.

title: Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing
abstract: The outbreak of COVID-19 Coronavirus, namely SARS-CoV-2, has created a calamitous situation throughout the world. The cumulative incidence of COVID-19 is rapidly increasing day by day. Machine Learning (ML) and Cloud Computing can be deployed very effectively to track the disease, predict growth of the epidemic and design strategies and policy to manage its spread. This study applies an improved mathematical model to analyse and predict the growth of the epidemic. An ML-based improved model has been applied to predict the potential threat of COVID-19 in countries worldwide. We show that using iterative weighting for fitting Generalized Inverse Weibull distribution, a better fit can be obtained to develop a prediction framework. This has been deployed on a cloud computing platform for more accurate and real-time prediction of the growth behavior of the epidemic. A data driven approach with higher accuracy as here can be very useful for a proactive response from the government and citizens. Finally, we propose a set of research opportunities and setup grounds for further practical applications.

title: Learning Based Methods for Code Runtime Complexity Prediction
abstract: Predicting the runtime complexity of a programming code is an arduous task. In fact, even for humans, it requires a subtle analysis and comprehensive knowledge of algorithms to predict time complexity with high fidelity, given any code. As per Turing’s Halting problem proof, estimating code complexity is mathematically impossible. Nevertheless, an approximate solution to such a task can help developers to get real-time feedback for the efficiency of their code. In this work, we model this problem as a machine learning task and check its feasibility with thorough analysis. Due to the lack of any open source dataset for this task, we propose our own annotated dataset, (The complete dataset is available for use at https://github.com/midas-research/corcod-dataset/blob/master/README.md) CoRCoD: Code Runtime Complexity Dataset, extracted from online coding platforms. We establish baselines using two different approaches: feature engineering and code embeddings, to achieve state of the art results and compare their performances. Such solutions can be highly useful in potential applications like automatically grading coding assignments, IDE-integrated tools for static code analysis, and others.

title: Recognizing flu-like symptoms from videos
abstract: Vision-based surveillance and monitoring is a potential alternative for early detection of respiratory disease outbreaks in urban areas complementing molecular diagnostics and hospital and doctor visit-based alert systems. Visible actions representing typical flu-like symptoms include sneeze and cough that are associated with changing patterns of hand to head distances, among others. The technical difficulties lie in the high complexity and large variation of those actions as well as numerous similar background actions such as scratching head, cell phone use, eating, drinking and so on.  In this paper, we make a first attempt at the challenging problem of recognizing flu-like symptoms from videos. Since there was no related dataset available, we created a new public health dataset for action recognition that includes two major flu-like symptom related actions (sneeze and cough) and a number of background actions. We also developed a suitable novel algorithm by introducing two types of Action Matching Kernels, where both types aim to integrate two aspects of local features, namely the space-time layout and the Bag-of-Words representations. In particular, we show that the Pyramid Match Kernel and Spatial Pyramid Matching are both special cases of our proposed kernels. Besides experimenting on standard testbed, the proposed algorithm is evaluated also on the new sneeze and cough set. Empirically, we observe that our approach achieves competitive performance compared to the state-of-the-arts, while recognition on the new public health dataset is shown to be a non-trivial task even with simple single person unobstructed view.  Our sneeze and cough video dataset and newly developed action recognition algorithm is the first of its kind and aims to kick-start the field of action recognition of flu-like symptoms from videos. It will be challenging but necessary in future developments to consider more complex real-life scenario of detecting these actions simultaneously from multiple persons in possibly crowded environments.

title: Generic probabilistic modelling and non-homogeneity issues for the UK epidemic of COVID-19
abstract: Coronavirus COVID-19 spreads through the population mostly based on social contact. To gauge the potential for widespread contagion, to cope with associated uncertainty and to inform its mitigation, more accurate and robust modelling is centrally important for policy making. We provide a flexible modelling approach that increases the accuracy with which insights can be made. We use this to analyse different scenarios relevant to the COVID-19 situation in the UK. We present a stochastic model that captures the inherently probabilistic nature of contagion between population members. The computational nature of our model means that spatial constraints (e.g., communities and regions), the susceptibility of different age groups and other factors such as medical pre-histories can be incorporated with ease. We analyse different possible scenarios of the COVID-19 situation in the UK. Our model is robust to small changes in the parameters and is flexible in being able to deal with different scenarios. This approach goes beyond the convention of representing the spread of an epidemic through a fixed cycle of susceptibility, infection and recovery (SIR). It is important to emphasise that standard SIR-type models, unlike our model, are not flexible enough and are also not stochastic and hence should be used with extreme caution. Our model allows both heterogeneity and inherent uncertainty to be incorporated. Due to the scarcity of verified data, we draw insights by calibrating our model using parameters from other relevant sources, including agreement on average (mean field) with parameters in SIR-based models. We use the model to assess parameter sensitivity for a number of key variables that characterise the COVID-19 epidemic. We also test several control parameters with respect to their influence on the severity of the outbreak. Our analysis shows that due to inclusion of spatial heterogeneity in the population and the asynchronous timing of the epidemic across different areas, the severity of the epidemic might be lower than expected from other models. We find that one of the most crucial control parameters that may significantly reduce the severity of the epidemic is the degree of separation of vulnerable people and people aged 70 years and over, but note also that isolation of other groups has an effect on the severity of the epidemic. It is important to remember that models are there to advise and not to replace reality, and that any action should be coordinated and approved by public health experts with experience in dealing with epidemics. The computational approach makes it possible for further extensive scenario-based analysis to be undertaken. This and a comprehensive study of sensitivity of the model to different parameters defining COVID-19 and its development will be the subject of our forthcoming paper. In that paper, we shall also extend the model where we will consider different probabilistic scenarios for infected people with mild and severe cases.

title: Emergence of a Novel Coronavirus (COVID-19): Protocol for Extending Surveillance Used by the Royal College of General Practitioners Research and Surveillance Centre and Public Health England
abstract: The Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) and Public Health England (PHE) have successfully worked together on the surveillance of influenza and other infectious diseases for over 50 years, including three previous pandemics. With the emergence of the international outbreak of the coronavirus infection (COVID-19), a UK national approach to containment has been established to test people suspected of exposure to COVID-19. At the same time and separately, the RCGP RSC’s surveillance has been extended to monitor the temporal and geographical distribution of COVID-19 infection in the community as well as assess the effectiveness of the containment strategy.  The aims of this study are to surveil COVID-19 in both asymptomatic populations and ambulatory cases with respiratory infections, ascertain both the rate and pattern of COVID-19 spread, and assess the effectiveness of the containment policy.  The RCGP RSC, a network of over 500 general practices in England, extract pseudonymized data weekly. This extended surveillance comprises of five components: (1) Recording in medical records of anyone suspected to have or who has been exposed to COVID-19. Computerized medical records suppliers have within a week of request created new codes to support this. (2) Extension of current virological surveillance and testing people with influenza-like illness or lower respiratory tract infections (LRTI)—with the caveat that people suspected to have or who have been exposed to COVID-19 should be referred to the national containment pathway and not seen in primary care. (3) Serology sample collection across all age groups. This will be an extra blood sample taken from people who are attending their general practice for a scheduled blood test. The 100 general practices currently undertaking annual influenza virology surveillance will be involved in the extended virological and serological surveillance. (4) Collecting convalescent serum samples. (5) Data curation. We have the opportunity to escalate the data extraction to twice weekly if needed. Swabs and sera will be analyzed in PHE reference laboratories.  General practice clinical system providers have introduced an emergency new set of clinical codes to support COVID-19 surveillance. Additionally, practices participating in current virology surveillance are now taking samples for COVID-19 surveillance from low-risk patients presenting with LRTIs. Within the first 2 weeks of setup of this surveillance, we have identified 3 cases: 1 through the new coding system, the other 2 through the extended virology sampling.  We have rapidly converted the established national RCGP RSC influenza surveillance system into one that can test the effectiveness of the COVID-19 containment policy. The extended surveillance has already seen the use of new codes with 3 cases reported. Rapid sharing of this protocol should enable scientific critique and shared learning.

title: Editor’s Perspectives – June 2020

title: EDGE COVID-19: A Web Platform to generate submission-ready genomes for SARS-CoV-2 sequencing efforts
abstract: Genomics has become a pivotal technology for understanding emerging outbreaks of infectious diseases. A wide range of technologies and strategies for pathogen genome enrichment and sequencing are being used by laboratories and institutes, resulting in different analytical procedures for generating genome sequences and resulting in non-standard entries of varying quality into public repositories. A standardized analytical process for genome sequence generation, particularly for outbreaks such as the ongoing COVID-19 pandemic, are critical to provide scientists with a solid genomic basis for epidemiology analyses and for making well-informed decisions. To address this challenge, we have developed a web platform (EDGE COVID-19) to run standardized workflows for reference-based genome assembly and to perform further analysis of Illumina or Oxford Nanopore Technologies data for SARS-CoV-2 genome sequencing projects. Given a raw Illumina or Oxford Nanopore FASTQ read file, this web platform automates the production of a SARS-CoV-2 genome that is ready for submission to GISAID or GenBank. EDGE COVID-19 is open-source software that is available as a web application for public use at https://covid19.edgebioinformatics.org, and as a Docker container at https://hub.docker.com/r/bioedge/edge-covid19 for local installation.

title: SARS-CoV-2 receptor networks in diabetic kidney disease, BK-Virus nephropathy and COVID-19 associated acute kidney injury
abstract: COVID-19 morbidity and mortality is significantly increased in patients with diabetes and kidney disease via unknown mechanisms. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) for entry into human host cells, and ACE2 levels in target cells may influence SARS-CoV-2 susceptibility. We investigated how pre-existing conditions and drug treatments alter receptor expression in kidney tissue. Using single cell RNA profiling (scRNAseq) to assess ACE2 and associated SARS-CoV-2 proteases in healthy living donors (LD) kidneys, diabetic kidney disease (DKD), and in kidney injury during viral infection, ACE2 expression was primarily associated with proximal tubular epithelial cells (PTEC). ACE2 mRNA expression levels were significantly upregulated in DKD versus LD, however, ACE2 levels were not altered by exposures to renin angiotensin aldosterone system (RAAS) inhibitors. ACE2+ expression signatures were defined by differential expression analysis and characterized by Bayesian integrative analysis of a large compendium of public -omics datasets, resulting in the identification of network modules induced in ACE2 positive PTEC in DKD and BK virus nephropathy. These ACE2 upregulated cell programs were linked to viral entry, immune activation, endomembrane reorganization, and RNA processing and overlapped significantly with the cellular responses induced by SARS-CoV-2 infection. Similar cellular programs were activated in ACE2-positive PTEC isolated in a urine sample from a COVID19 patient with acute kidney injury, suggesting a consistent ACE2-coregulated expression program that may interact with SARS-Cov-2 infection processes. The SARS-CoV-2 receptor associated gene signatures could seed further research into therapeutic strategies for COVID-19. Functional networks of gene expression signatures are available for further exploration to researchers at HumanBase (hb.flatironinstitute.org/covid-kidney).

title: How Do the First Days Count? A Case Study of Qatar Experience in Emergency Risk Communication during the MERS-CoV Outbreak
abstract: This case study is the first to be developed in the Middle East region to document what happened during the response to the 2013 MERS outbreak in Qatar. It provides a description of key epidemiologic events and news released from a prime daily newspaper and main Emergency Risk Communication (ERC) actions that were undertaken by public health authorities. Using the Crisis and Emergency Risk Communication (CERC) theoretical framework, the study analyzes how the performed ERC strategies during the first days of the outbreak might have contributed to the outbreak management.  MERS-CoV related events were chronologically tracked, together with the relevant stories that were published in a major newspaper over the course of three distinct phases of the epidemic. The collected media stories were then assessed against the practiced emergency risk communication (ERC) activities during the same time frame.  The Crisis & Emergency Risk Communication (CERC) framework was partially followed during the early days of the MERS-CoV epidemic, which were characterized by overwhelming uncertainty. The SCH’s commitment to a proactive and open risk communication strategy since day one, contributed to creating the SCH’s image as a credible source of information and allowed for the quick initiation of the overall response efforts. Yet, conflicting messages and over reassurance were among the observed pitfalls of the implemented ERC strategy.  The adoption of CERC principles can help restore and maintain the credibility of responding agencies. Further work is needed to develop more rigorous and comprehensive research strategies that address sharing of information by mainstream as well as social media for a more accurate assessment of the impact of the ERC strategy.

title: Relevance of enriched expression of SARS-CoV-2 binding receptor ACE2 in gastrointestinal tissue with pathogenesis of digestive symptoms, diabetes-associated mortality, and disease recurrence in COVID-19 patients
abstract: Introduction COVID-19, is caused by a new strain of coronavirus called SARS-coronavirus-2 (SARS-CoV-2), which is a positive sense single strand RNA virus. In humans, it binds to angiotensin converting enzyme 2 (ACE2) with the help a structure on its surface called the S-spike. COVID-19 poses intriguing issues with imperative relevance to clinicians. The pathogenesis of GI symptoms, diabetes-associated mortality, and disease recurrence in COVID-19 are of particular relevance because they cannot be sufficiently explained from the existing knowledge of the viral diseases. Tissue specific variation of ACE2 expression in healthy individuals can help in understanding the pathophysiological basis the aforementioned collection of symptoms.  The data were downloaded from the Human Protein Atlas available at (https://www.proteinatlas.org/search/ACE2) and the tissue specific expression (both mRNA and protein) of ACE2 as yielded from the studies with RNA sequencing and immunohistochemistry (IHC) was analyzed as a function of the various components of the digestive tract. A digestive system specific functional enrichment map of ACE2 gene was created using g:profiler (https://biit.cs.ut.ee/gprofiler/gost) utility and the data were visualized using Cytoscape software, version 3.7.2 (https://cytoscape.org/).  The correlated expression (genomic and proteomic) of ACE2 (to which SARS-CoV-2 binds through the S-spike) was found to be enriched in the lower gastrointestinal tract (GIT) (highest in small intestine, followed by colon and rectum), and was undetectable in the upper GIT components: mouth cavity (tongue, oral mucosa, and salivary glands), esophagus, and stomach. High expression of ACE2 was noted in the glandular cells as well as in the enterocytes in the lining epithelium (including brush border epithelium). Among other digestive system organs, gallbladder (GB) showed high expression of ACE2 in glandular cells, while any protein expression was undetectable in liver and pancreas.  Based on the findings of this study and supportive evidence from the literature we propose that a SARS-CoV-2 binding with ACE2 mediates dysregulation of the sodium dependent nutrient transporters and hence may be a plausible basis for the digestive symptoms in COVID-19 patients. ACE2 mediated dysregulation of sodium dependent glucose transporter (SGLT1 or SLC5A1) in the intestinal epithelium also links it to the pathogenesis of diabetes mellitus which can be a possible reason for the associated mortality in COVID-19 patients with diabetes. High expression of ACE2 in mucosal cells of the intestine and GB make these organs potential sites for the virus entry and replication. Continued replication of the virus at these ACE2 enriched sites may be a basis for the disease recurrence reported in some, thought to be cured, patients. Highlights SARS-CoV-2 binding receptor ACE2 expression is enriched in human intestine ACE2 regulates neutral amino acid (B0AT1) and glucose transporter (SGLT1) in intestinal epithelium Mucus secreted from GI cells may protect SARS-CoV-2 from harsh pH levels of the digestive juices Continued replication of SARS-CoV-2 in GI cells may cause disease recurrence

title: Visualising the Evolution of English Covid-19 Cases with Topological Data Analysis Ball Mapper
abstract: Understanding disease spread through data visualisation has concentrated on trends and maps. Whilst these are helpful, they neglect important multi-dimensional interactions between characteristics of communities. Using the Topological Data Analysis Ball Mapper algorithm we construct an abstract representation of NUTS3 level economic data, overlaying onto it the confirmed cases of Covid-19 in England. In so doing we may understand how the disease spreads on different socio-economical dimensions. It is observed that some areas of the characteristic space have quickly raced to the highest levels of infection, while others close by in the characteristic space, do not show large infection growth. Likewise, we see patterns emerging in very different areas that command more monitoring. A strong contribution for Topological Data Analysis, and the Ball Mapper algorithm especially, in comprehending dynamic epidemic data is signposted.

title: User Concerns&Tradeoffs in Technology-Facilitated Contact Tracing
abstract: Contact tracing apps are one of the primary technology-facilitated Coronavirus (COVID) responses currently proposed. In the majority of Western nations there is the expectation that citizens will have the autonomy to decide whether or not to install a COVID-related app. Yet, the more users who install a COVID app, the greater the impact on public health. As explained by Chan et al. the value of users installing a contact-tracing app grows quadratically with the number of users who install. How can we get users to install COVID apps? By understanding and mitigating user concerns and tradeoffs. This document provides a framework for considering the user-relevant components of COVID apps and corresponding user tradeoffs related to these apps. Specifically, we enumerate user tradeoffs related to data collection, data quality, identifier/data encryption, privacy invasion, mobile costs, user agency, benefits from app use, and app transparency. We break down which tradeoffs are relevant for which contact tracing implementations (centralized vs. decentralized, location- vs. proximity-based). Additionally, we compare contact tracing to an alternative technology-facilitated COVID solution: narrowcasts. Narrowcasts are location-personalized broadcasts about COVID hotspots in your vicinity.

title: Ten recommendations for supporting open pathogen genomic analysis in public health
abstract: Increasingly, public-health agencies are using pathogen genomic sequence data to support surveillance and epidemiological investigations. As access to whole-genome sequencing has grown, greater amounts of molecular data have helped improve the ability to detect and track outbreaks of diseases such as COVID-19, investigate transmission chains and explore large-scale population dynamics, such as the spread of antibiotic resistance. However, the wide adoption of whole-genome sequencing also poses new challenges for public-health agencies that must adapt to support a new set of expertise, which means that the capacity to perform genomic data assembly and analysis has not expanded as widely as the adoption of sequencing itself. In this Perspective, we make recommendations for developing an accessible, unified informatic ecosystem to support pathogen genomic analysis in public-health agencies across income settings. We hope that the creation of this ecosystem will allow agencies to effectively and efficiently share data, workflows and analyses and thereby increase the reproducibility, accessibility and auditability of pathogen genomic analysis while also supporting agency autonomy.

title: The Effect of Large-Scale Anti-Contagion Policies on the Coronavirus (COVID-19) Pandemic
abstract: Governments around the world are responding to the novel coronavirus (COVID-19) pandemic with unprecedented policies designed to slow the growth rate of infections. Many actions, such as closing schools and restricting populations to their homes, impose large and visible costs on society. In contrast, the benefits of these policies, in the form of infections that did not occur, cannot be directly observed and are currently understood through process-based simulations. Here, we compile new data on 1,659 local, regional, and national anti-contagion policies recently deployed in the ongoing pandemic across localities in China, South Korea, Iran, Italy, France, and the United States (US). We then apply reduced-form econometric methods, commonly used to measure the effect of policies on economic growth, to empirically evaluate the effect that these anti-contagion policies have had on the growth rate of infections. In the absence of any policy actions, we estimate that early infections of COVID-19 exhibit exponential growth rates of roughly 42% per day. We find that anti-contagion policies collectively have had significant effects slowing this growth. Our results suggest that similar policies may have different impacts on different populations, but we obtain consistent evidence that the policy packages now deployed are achieving large, beneficial, and measurable health outcomes. We estimate that, to date, current policies have already prevented or delayed on the order of 62 million infections across these six countries. These findings may help inform whether or when these ongoing policies should be lifted or intensified, and they can support decision-making in the other 180+ countries where COVID-19 has been reported.

title: Information Seeking Responses to News of Local COVID-19 Cases: Evidence from Internet Search Data
abstract: The novel coronavirus (COVID-19) outbreak is a global pandemic with community circulation in many countries, including the U.S. where every state is reporting confirmed cases. The course of this pandemic will be largely shaped by how governments enact timely policies, disseminate the information, and most importantly, how the public reacts to them. Here, we examine informationseeking responses to the first COVID-19 case public announcement in a state. By using an eventstudy framework, we show that such news increases collective attention to the crisis right away, but the elevated level of attention is short-lived, even though the initial announcements were followed by increasingly strong measures. We find that people respond to the first report of COVID-19 in their state by immediately seeking information about COVID-19, as measured by searches for coronavirus, coronavirus symptoms and hand sanitizer. On the other hand, searches for information regarding community level policies (e.g., quarantine, school closures, testing), or personal health strategies (e.g., masks, grocery delivery, over-the-counter medications) do not appear to be immediately triggered by first reports. These results are encouraging given our study period is relatively early in the epidemic and more elaborate policy responses were not yet part of the public discourse. Further analysis will track evolving patterns of responses to subsequent flows of public information.

title: An Interactive Online Dashboard for Tracking COVID-19 in U.S. Counties, Cities, and States in Real Time
abstract: To create an online resource that informs the public of COVID-19 outbreaks in their area.  This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard.  The web resource, called the COVID-19 Watcher, can be accessed at https://covid19watcher.research.cchmc.org/. It displays COVID-19 data from every county and 188 metropolitan areas in the U.S. Features include rankings of the worst affected areas and auto-generating plots that depict temporal changes in testing capacity, cases, and deaths.  The Centers for Disease Control and Prevention (CDC) do not publish COVID-19 data for local municipalities, so it is critical that academic resources fill this void so the public can stay informed. The data used have limitations and likely underestimate the scale of the outbreak.  The COVID-19 Watcher can provide the public with real-time updates of outbreaks in their area.

title: Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set
abstract: At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter.  In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository.  We started this ongoing data collection on January 28, 2020, leveraging Twitter’s streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter’s search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020.  Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events.  It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic—and undoubtedly more.

title: Potential effect of blood purification therapy in reducing cytokine storm as a late complication of critically ill COVID-19

title: Quantifying human mobility behavior changes in response to non-pharmaceutical interventions during the COVID-19 outbreak in the United States
abstract: Ever since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including the United States. It is one of the major community mitigation strategies, also known as non-pharmaceutical interventions. However, our understanding is remaining limited in how people practice social distancing. In this study, we construct a Social Distancing Index (SDI) to evaluate people's mobility pattern changes along with the spread of COVID-19. We utilize an integrated dataset of mobile device location data for the contiguous United States plus Alaska and Hawaii over a 100-day period from January 1, 2020 to April 9, 2020. The major findings are: 1) the declaration of the national emergency concerning the COVID-19 outbreak greatly encouraged social distancing and the mandatory stay-at-home orders in most states further strengthened the practice; 2) the states with more confirmed cases have taken more active and timely responses in practicing social distancing; 3) people in the states with fewer confirmed cases did not pay much attention to maintaining social distancing and some states, e.g., Wyoming, North Dakota, and Montana, already began to practice less social distancing despite the high increasing speed of confirmed cases; 4) some counties with the highest infection rates are not performing much social distancing, e.g., Randolph County and Dougherty County in Georgia, and some counties began to practice less social distancing right after the increasing speed of confirmed cases went down, e.g., in Blaine County, Idaho, which may be dangerous as well.

title: Cancer datasets and the SARS-CoV-2 pandemic: establishing principles for collaboration

title: Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes
abstract: Genome Detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present The Genome Detective Coronavirus Typing Tool that can accurately identify novel coronavirus (2019-nCoV) sequences isolated in China and around the world. The tool can accept up to 2,000 sequences per submission and the analysis of a new whole genome sequence will take approximately one minute. The tool has been tested and validated with hundreds of whole genomes from ten coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for 2019-nCoV. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines.

title: Comment on “Gain-of-Function Research and the Relevance to Clinical Practice”

title: The COronavirus Pandemic Epidemiology (COPE) Consortium: A Call to Action
abstract: The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) pandemic presents challenges to the real-time collection of population-scale data to inform near-term public health needs as well as future investigations. We established the COronavirus Pandemic Epidemiology (COPE) consortium to address this unprecedented crisis on behalf of the epidemiology research community. As a central component of this initiative, we have developed a COVID Symptom Study (previously known as the COVID Symptom Tracker) mobile application as a common data collection tool for epidemiologic cohort studies with active study participants. This mobile application collects information on risk factors, daily symptoms, and outcomes through a user-friendly interface that minimizes participant burden. Combined with our efforts within the general population, data collected from nearly 3 million participants in the United States and United Kingdom are being used to address critical needs in the emergency response, including identifying potential hot spots of disease and clinically actionable risk factors. The linkage of symptom data collected in the app with information and biospecimens already collected in epidemiology cohorts will position us to address key questions related to diet, lifestyle, environmental, and socioeconomic factors on susceptibility to COVID-19, clinical outcomes related to infection, and long-term physical, mental health, and financial sequalae. We call upon additional epidemiology cohorts to join this collective effort to strengthen our impact on the current health crisis and generate a new model for a collaborative and nimble research infrastructure that will lead to more rapid translation of our work for the betterment of public health.

title: Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release
abstract: Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One promising approach is to train machine learning (ML) and artificial intelligence (AI) tools to screen large numbers of small molecules. As a contribution to that effort, we are aggregating numerous small molecules from a variety of sources, using high-performance computing (HPC) to computer diverse properties of those molecules, using the computed properties to train ML/AI models, and then using the resulting models for screening. In this first data release, we make available 23 datasets collected from community sources representing over 4.2 B molecules enriched with pre-computed: 1) molecular fingerprints to aid similarity searches, 2) 2D images of molecules to enable exploration and application of image-based deep learning methods, and 3) 2D and 3D molecular descriptors to speed development of machine learning models. This data release encompasses structural information on the 4.2 B molecules and 60 TB of pre-computed data. Future releases will expand the data to include more detailed molecular simulations, computed models, and other products.

title: SARS‐CoV‐2, an evolutionary perspective of interaction with human ACE2 reveals undiscovered amino acids necessary for complex stability
abstract: The emergence of SARS‐CoV‐2 has resulted in nearly 1,280,000 infections and 73,000 deaths globally so far. This novel virus acquired the ability to infect human cells using the SARS‐CoV cell receptor hACE2. Because of this, it is essential to improve our understanding of the evolutionary dynamics surrounding the SARS‐CoV‐2 hACE2 interaction. One way theory predicts selection pressures should shape viral evolution is to enhance binding with host cells. We first assessed evolutionary dynamics in select betacoronavirus spike protein genes to predict whether these genomic regions are under directional or purifying selection between divergent viral lineages, at various scales of relatedness. With this analysis, we determine a region inside the receptor‐binding domain with putative sites under positive selection interspersed among highly conserved sites, which are implicated in structural stability of the viral spike protein and its union with human receptor ACE2. Next, to gain further insights into factors associated with recognition of the human host receptor, we performed modeling studies of five different betacoronaviruses and their potential binding to hACE2. Modeling results indicate that interfering with the salt bridges at hot spot 353 could be an effective strategy for inhibiting binding, and hence for the prevention of SARS‐CoV‐2 infections. We also propose that a glycine residue at the receptor‐binding domain of the spike glycoprotein can have a critical role in permitting bat SARS‐related coronaviruses to infect human cells.

title: Aerodynamic Characteristics and RNA Concentration of SARS-CoV-2 Aerosol in Wuhan Hospitals during COVID-19 Outbreak
abstract: The ongoing outbreak of COVID-19 has spread rapidly and sparked global concern. While the transmission of SARS-CoV-2 through human respiratory droplets and contact with infected persons is clear, the aerosol transmission of SARS-CoV-2 has been little studied.  Thirty-five aerosol samples of three different types (total suspended particle, size segregated and deposition aerosol) were collected in Patient Areas (PAA) and Medical Staff Areas (MSA) of Renmin Hospital of Wuhan University (Renmin) and Wuchang Fangcang Field Hospital (Fangcang), and Public Areas (PUA) in Wuhan, China during COVID-19 outbreak. A robust droplet digital polymerase chain reaction (ddPCR) method was employed to quantitate the viral SARS-CoV-2 RNA genome and determine aerosol RNA concentration.  The ICU, CCU and general patient rooms inside Renmin, patient hall inside Fangcang had undetectable or low airborne SARS-CoV-2 concentration but deposition samples inside ICU and air sample in Fangcang patient toilet tested positive. The airborne SARS-CoV-2 in Fangcang MSA had bimodal distribution with higher concentration than those in Renmin during the outbreak but turned negative after patients number reduced and rigorous sanitization implemented. PUA had undetectable airborne SARS-CoV-2 concentration but obviously increased with accumulating crowd flow.  Room ventilation, open space, proper use and disinfection of toilet can effectively limit aerosol transmission of SARS-CoV-2. Gathering of crowds with asymptomatic carriers is a potential source of airborne SARS-CoV-2. The virus aerosol deposition on protective apparel or floor surface and their subsequent resuspension is a potential transmission pathway and effective sanitization is critical in minimizing aerosol transmission of SARS-CoV-2.

title: Somatic symptoms and concern regarding COVID-19 among Chinese college and primary school students: A cross-sectional survey
abstract: The rapid, ongoing and worldwide coronavirus 2019 (COVID-19) pandemic represents a global public health emergency. Our objective was to explore the impact of the COVID-19 pandemic on somatic symptoms among Chinese college and primary school students, to provide reference data pertaining to the mental health of this population in the context of a public health emergency. In February and March 2020, we explored the somatic symptoms and concerns regarding COVID-19 of 399 college and primary school students in Sichuan Province using the Somatic Self-rating Scale (SSS) and a novel questionnaire, respectively. Logistic regression analysis and non-parametric tests were used to analyze the data. The incidence of somatic symptoms among college students was 34.85 (mild, 26.26%; moderate, 8.59%). The incidence of somatic symptoms in primary school students was 2.39% (all mild). Among the entire cohort, concern regarding COVID-19 was positively correlated with the occurrence of somatic symptoms. Somatic symptoms were more likely among college students expressing greater concern regarding the threat to life and health posed by COVID-19, and the efficacy of prevention and control measures. Among primary school students, only the concern for life and health was associated with a higher likelihood of somatic symptoms. Our data indicate that governments and other relevant agencies should implement different measures to prevent and control mental health disorders diseases in primary school and college students.

title: A systematic review on the efficacy and safety of chloroquine for the treatment of COVID-19
abstract: COVID-19 (coronavirus disease 2019) is a public health emergency of international concern. As of this time, there is no known effective pharmaceutical treatment, although it is much needed for patient contracting the severe form of the disease. The aim of this systematic review was to summarize the evidence regarding chloroquine for the treatment of COVID-19.  PubMed, EMBASE, and three trial Registries were searched for studies on the use of chloroquine in patients with COVID-19.  We included six articles (one narrative letter, one in-vitro study, one editorial, expert consensus paper, two national guideline documents) and 23 ongoing clinical trials in China. Chloroquine seems to be effective in limiting the replication of SARS-CoV-2 (virus causing COVID-19) in vitro.  There is rationale, pre-clinical evidence of effectiveness and evidence of safety from long-time clinical use for other indications to justify clinical research on chloroquine in patients with COVID-19. However, clinical use should either adhere to the Monitored Emergency Use of Unregistered Interventions (MEURI) framework or be ethically approved as a trial as stated by the World Health Organization. Safety data and data from high-quality clinical trials are urgently needed.

title: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks
abstract: On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.

title: Dutch General Public Reaction on Governmental COVID-19 Measures and Announcements in Twitter Data
abstract: Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. While the staying-at-home policy makes face-to-face interactions and interviews challenging, analysing real-time Twitter data that reflects public opinion toward policy measures is a cost-effective way to access public sentiment. In this paper, we collect streaming data using the Twitter API starting from the COVID-19 outbreak in the Netherlands in February 2020, and track Dutch general public reactions on governmental measures and announcements. We provide temporal analysis of tweet frequency and public sentiment over the past four months. We also identify public attitudes towards the Dutch policy on wearing face masks in a case study. By presenting those preliminary results, we aim to provide visibility into the social media discussions around COVID-19 to the general public, scientists and policy makers. The data collection and analysis will be updated and expanded over time.

title: Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation
abstract: Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection areas and how to better conduct this transfer procedure. This paper provides a way to understand the transferability of non-COVID19 lung lesions. Based on a publicly available COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four transfer learning methods using 3D U-Net as a standard encoder-decoder method. The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models. We further show the capability of the encoder to learn feature representations of lung lesions, which improves segmentation accuracy and facilitates training convergence. In addition, our proposed Multi-encoder learning method incorporates transferred lung lesion features from non-COVID19 datasets effectively and achieves significant improvement. These findings promote new insights into transfer learning for COVID-19 CT image segmentation, which can also be further generalized to other medical tasks.

title: Data Explosion, Data Nature and Dataology
abstract: The essence of computer applications is to store things in the real world into computer systems in the form of data, i.e., it is a process of producing data. Some data are the records related to culture and society, and others are the descriptions of phenomena of universe and life. The large scale of data is rapidly generated and stored in computer systems, which is called data explosion. Data explosion forms data nature in computer systems. To explore data nature, new theories and methods are required. In this paper, we present the concept of data nature and introduce the problems arising from data nature, and then we define a new discipline named dataology (also called data science or science of data), which is an umbrella of theories, methods and technologies for studying data nature. The research issues and framework of dataology are proposed.

title: Data sharing in the era of COVID-19

title: Prediction of SARS-CoV interaction with host proteins during lung aging reveals a potential role for TRIB3 in COVID-19
abstract: COVID-19 is prevalent in the elderly. Old individuals are more likely to develop pneumonia and respiratory failure due to alveolar damage, suggesting that lung senescence may increase the susceptibility to SARS-CoV-2 infection and replication. Considering that human coronavirus (HCoVs; SARS-CoV-2 and SARS-CoV) require host cellular factors for infection and replication, we analyzed Genotype-Tissue Expression (GTEx) data to test whether lung aging is associated with transcriptional changes in human protein-coding genes that potentially interact with these viruses. We found decreased expression of the gene tribbles homolog 3 (TRIB3) during aging in male individuals, and its protein was predicted to interact with HCoVs nucleocapsid protein and RNA-dependent RNA polymerase. Using publicly available lung single-cell data, we found TRIB3 expressed mainly in alveolar epithelial cells that express SARS-CoV-2 receptor ACE2. Functional enrichment analysis of age-related genes, in common with SARS-CoV-induced perturbations, revealed genes associated with the mitotic cell cycle and surfactant metabolism. Given that TRIB3 was previously reported to decrease virus infection and replication, the decreased expression of TRIB3 in aged lungs may help explain why older male patients are related to more severe cases of the COVID-19. Thus, drugs that stimulate TRIB3 expression should be evaluated as a potential therapy for the disease.

title: Dataset to Support the Adoption of Social Media and Emerging Technologies for Students’ Continuous Engagement
abstract: The recent advancements in ICT have made it possible for teaching and learning to be conducted outside the four walls of a University. Furthermore, the recent COVID-19 pandemic that has crippled educational activities in all nations of the world has further revealed the urgent need for academic institutions to embrace and integrate alternative modes of teaching and learning via social media platforms and emerging technologies into existing teaching tools. This article contains data collected from 850 face to face University students during the COVID-19 pandemic lockdown. An online google form was used to elicit information from the students about their awareness and intention to use these alternative modes of teaching and learning. The questions were structured using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This data article includes the questionnaire used to retrieve the data, the responses obtained in spreadsheet format, the charts generated from the responses received, the Statistical Package of the Social Sciences (SPSS) file, the descriptive statistics, and reliability analysis computed for all the UTAUT variables. The dataset will enhance understanding of how face to face students use social media platforms and how these platforms could be used to engage the students outside their classroom activities. Also, the dataset exposes how familiar face to face University students are to these emerging teaching and learning technologies. The challenges that could inhibit the adoption of these technologies were also revealed.

title: Is there evidence that BCG vaccination has non-specific protective effects for COVID 19 infections or is it an illusion created by lack of testing?
abstract: The goal of this paper is to showcase that the COVID-19 disease pattern is evolving and to study the relationship between mandatory BCG policy and caseload/million or death/per million. We analyze seven recent publications on the impact of BCG vaccinations on the development of COVID19 illness and extend presented findings using the latest data from April 10, 2020. We analyze data from 98 countries and we extend existing models by adding the dimension of COVID-19-related testing conducted by the analyzed countries. Similarly to prior studies, we find that COVID-19 attributable case and death incidences across countries share a relationship with the BCG vaccination inclusion in the national immunization program of a country when testing is not taken into consideration. However, this relationship vanishes when we add the dimension of testing. We observe that case and death incidences conditional on testing do not get affected by the BCG vaccination inclusion in the national immunization program of a country. Therefore, we show that there is no statistical evidence to support the assertion that inclusion of BCG vaccination in national immunization program (NIP) has any impact of COVID 19 infections (cases) or mortality.

title: Mood of India During Covid-19 -- An Interactive Web Portal Based on Emotion Analysis of Twitter Data
abstract: The severe outbreak of Covid-19 pandemic has affected many countries across the world, and disrupted the day to day activities of many people. During such outbreaks, understanding the emotional state of citizens of a country could be of interest to various organizations to carry out tasks and to take necessary measures. Several studies have been performed on data available on various social media platforms and websites to understand the emotions of people against many events, inclusive of Covid-19, across the world. Twitter and other social media platforms have been bridging the gap between the citizens and government in various countries and are of more prominence in India. Sentiment Analysis of posts on twitter is observed to accurately reveal the sentiments. Analysing real time posts on twitter in India during Covid-19, could help in identifying the mood of the nation. However, most of the existing studies related to Covid-19, on twitter and other social media platforms are performed on data posted during a specific interval. We are not aware of any research that identifies emotional state of India on a daily basis. Hence, we present a web portal that aims to display mood of India during Covid-19, based on real time twitter data. This portal also enables users to select date range, specific date and state in India to display mood of people belonging to the specified region, on the specified date or during the specified date range. Also, the number of Covid-19 cases and mood of people at specific cities and states on specific dates is visualized on the country map. As of May 6 2020, the web portal has about 194370 tweets, and each of these tweets are classified into seven categories that include six basic emotions and a neutral category. A list of Trigger Events are also specified, to allow users to view the mood of India on specific events happening in the country during Covid-19.

title: Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset
abstract: The COVID-19 coronavirus is one of the devastating viruses according to the world health organization. This novel virus leads to pneumonia, which is an infection that inflames the lungs' air sacs of a human. One of the methods to detect those inflames is by using x-rays for the chest. In this paper, a pneumonia chest x-ray detection based on generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a limited dataset will be presented. The use of GAN positively affects the proposed model robustness and made it immune to the overfitting problem and helps in generating more images from the dataset. The dataset used in this research consists of 5863 X-ray images with two categories: Normal and Pneumonia. This research uses only 10% of the dataset for training data and generates 90% of images using GAN to prove the efficiency of the proposed model. Through the paper, AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to detect the pneumonia from chest x-rays. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models proved it is efficiency according to testing accuracy measurement. The research concludes that the Resnet18 is the most appropriate deep transfer model according to testing accuracy measurement and achieved 99% with the other performance metrics such as precision, recall, and F1 score while using GAN as an image augmenter. Finally, a comparison result was carried out at the end of the research with related work which used the same dataset except that this research used only 10% of original dataset. The presented work achieved a superior result than the related work in terms of testing accuracy.

title: SARS-CoV-2 is an appropriate name for the new coronavirus

title: Genotyping SARS-CoV-2 through an interactive web application

title: The amphotropic murine leukemia virus receptor gene encodes a 71-kilodalton protein that is induced by phosphate depletion.
abstract: The amphotropic murine leukemia virus (MuLV) can infect cells from a number of mammals, including humans, via its specific receptor. Basic knowledge of amphotropic MuLV receptor expression is likely to be useful in the development and improvement of gene therapy protocols based on amphotropic-pseudotyped vectors. To investigate the expression of the human receptor for the amphotropic MuLV (GLVR-2, newly termed Pit2), we determined its mRNA levels in several cell lines and found them to vary significantly. Induction of increased levels of mRNA after removal of phosphate from the media was observed in two osteosarcoma cell lines. The increase in GLVR-2 mRNA resulted in a concomitant rise in the levels of a 71-kDa protein specifically recognized by affinity-purified antibodies against GLVR-2. Using these antibodies, we were able to confirm the intracellular topology of the large hydrophilic domain between the proposed sixth and seventh transmembrane domains of the GLVR-2 protein. This assignment is in agreement with the fourth extracellular loop being outside the cell, consistent with the proposal that the fourth extracellular loop of GLVR-2 contains the envelope binding site.

title: Associations between COVID-19 infection, tobacco smoking and nicotine use, common respiratory conditions and inhaled corticosteroids: a prospective QResearch-Case Mix Programme data linkage study January-May 2020
abstract: Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19. Likewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments.  In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England's database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID. Using Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: 1. Assess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity. 2. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD. 3. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality. 4. Examine the association between former smokers who are using NRT or are vaping and disease severity. 5. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection. 6. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status. 7. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease. 8. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD.  This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19. We will undertake several sensitivity analyses to examine the potential for bias in these associations.

title: Visualising COVID-19 Research
abstract: The world has seen in 2020 an unprecedented global outbreak of SARS-CoV-2, a new strain of coronavirus, causing the COVID-19 pandemic, and radically changing our lives and work conditions. Many scientists are working tirelessly to find a treatment and a possible vaccine. Furthermore, governments, scientific institutions and companies are acting quickly to make resources available, including funds and the opening of large-volume data repositories, to accelerate innovation and discovery aimed at solving this pandemic. In this paper, we develop a novel automated theme-based visualisation method, combining advanced data modelling of large corpora, information mapping and trend analysis, to provide a top-down and bottom-up browsing and search interface for quick discovery of topics and research resources. We apply this method on two recently released publications datasets (Dimensions' COVID-19 dataset and the Allen Institute for AI's CORD-19). The results reveal intriguing information including increased efforts in topics such as social distancing; cross-domain initiatives (e.g. mental health and education); evolving research in medical topics; and the unfolding trajectory of the virus in different territories through publications. The results also demonstrate the need to quickly and automatically enable search and browsing of large corpora. We believe our methodology will improve future large volume visualisation and discovery systems but also hope our visualisation interfaces will currently aid scientists, researchers, and the general public to tackle the numerous issues in the fight against the COVID-19 pandemic.

title: Antiviral Phosphorodiamidate Morpholino Oligomers are Protective against Chikungunya Virus Infection on Cell-based and Murine Models
abstract: Chikungunya virus (CHIKV) infection in human is associated with debilitating and persistent arthralgia and arthritis. Currently, there is no specific vaccine or effective antiviral available. Anti-CHIKV Phosphorodiamidate Morpholino Oligomer (CPMO) was evaluated for its antiviral efficacy and cytotoxcity in human cells and neonate murine model. Two CPMOs were designed to block translation initiation of a highly conserved sequence in CHIKV non-structural and structural polyprotein, respectively. Pre-treatment of HeLa cells with CPMO1 signficantly suppressed CHIKV titre, CHIKV E2 protein expression and prevented CHIKV-induced CPE. CPMO1 activity was also CHIKV-specific as shown by the lack of cross-reactivity against SINV or DENV replication. When administered prophylactically in neonate mice, 15 μg/g CPMO1v conferred 100% survival against CHIKV disease. In parallel, these mice demonstrated significant reduction in viremia and viral load in various tissues. Immunohistological examination of skeletal muscles and liver of CPMO1v-treated mice also showed healthy tissue morphology, in contrast to evident manifestation of CHIKV pathogenesis in PBS- or scrambled sCPMO1v-treated groups. Taken together, our findings highlight for the first time that CPMO1v has strong protective effect against CHIKV infection. This warrants future development of morpholino as an alternative antiviral agent to address CHIKV infection in clinical applications.

title: COVID-19Base: A knowledgebase to explore biomedical entities related to COVID-19
abstract: We are presenting COVID-19Base, a knowledgebase highlighting the biomedical entities related to COVID-19 disease based on literature mining. To develop COVID-19Base, we mine the information from publicly available scientific literature and related public resources. We considered seven topic-specific dictionaries, including human genes, human miRNAs, human lncRNAs, diseases, Protein Databank, drugs, and drug side effects, are integrated to mine all scientific evidence related to COVID-19. We have employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. To the best of our knowledge, this is the first knowledgebase dedicated to COVID-19, which integrates such large variety of related biomedical entities through literature mining. Proper investigation of the mined biomedical entities along with the identified interactions among those, reported in COVID-19Base, would help the research community to discover possible ways for the therapeutic treatment of COVID-19.

title: Incarceration And Its Disseminations: COVID-19 Pandemic Lessons From Chicago's Cook County Jail.
abstract: Jails and prisons are major sites of novel coronavirus (SARS-CoV-2) infection. Many jurisdictions in the United States have therefore accelerated release of low-risk offenders. Early release, however, does not address how arrest and pre-trial detention practices may be contributing to disease spread. Using data from Cook County Jail, in Chicago, Illinois, one of the largest known nodes of SARS-CoV-2 spread, we analyze the relationship between jailing practices and community infections at the zip-code level. We find that jail cycling is a significant predictor of SARS-CoV-2 infection, accounting for 55 percent of the variance in case rates across zip codes in Chicago and 37 percent in Illinois. By comparison, jail cycling far exceeds race, poverty, public transit utilization, and population density as a predictor of variance. The data suggest that cycling through Cook County Jail alone is associated with 15.7 percent of all documented novel coronavirus disease (COVID-19) cases in Illinois and 15.9 percent in Chicago as of April 19, 2020. Our findings support arguments for reduced reliance on incarceration and for related justice reforms both as emergency measures during the present pandemic and as sustained structural changes vital for future pandemic preparedness and public health.

title: Deep Learning COVID-19 Features on CXR using Limited Training Data Sets
abstract: Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of the CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.

title: A Comprehensive Analysis of COVID-19 Outbreak situation in India
abstract: The outbreak of COVID-19 in different parts of the world is a major concern for all the administrative units of respective countries. India is also facing this very tough task for controlling the virus outbreak and has managed its growth rate through some strict measures. This study presents the current situation of coronavirus spread in India along with the impact of various measures taken for it. With the help of data sources (till 7th-8th April 2020) from various state units of India and Ministry of Health and Family Welfare, Government of India, this study presents various trends and patterns. This study answers six different research ques-tions in a comprehensive manner. It has been reported that growth rate of infected cases has been controlled with the help of National Lockdown, however some uncontrolled mass level events had negatively impacted the infected cases. With the help of exponential and polyno-mial regression modelling, the predictions of up to 75000 cases have been done by the end of April 2020. It has also been seen that there are some prominent clusters and patient nodes in the network of patients which are the major influencers for COVID-19 spread. Also, death rate case predictions have been done through multi-class classification models with an accuracy of 75%. At the end, strategies for continuation for lockdown has been discussed and presented. It appears that only essential services should be open for the citizens of India and the national lockdown should be carried on for next 2-4 weeks. This study will be useful for the Government of India and various states of India, Administrative Units of India, Frontline health workforce of India, researchers and scientists. This study will also be favorable for the administrative units of other countries to consider various aspects related to the control of COVID-19 outspread in their respective regions.

title: Laboratory data analysis of novel coronavirus (COVID-19) screening in 2510 patients
abstract: Novel coronavirus (COVID-19) is highly infectious and requires early detection, isolation, and treatment. We tried to find some useful information by analyzing the covid-19 screening data, so as to provide help for clinical practice.  We collected nucleic acid and hematology data from 2510 patients for COVID-19 infection for retrospective analysis. Result COVID-19 and influenza A and B infection rates were 1.3%, 3%, and 3%, respectively. COVID-19 nucleic acid was detected in stool but not in tear samples from 8 positive patients. Among the 32 patients with COVID-19, 15 (47%) and 16 (50%) patients showed decreased lymphocyte count and lymphocyte ratio, 21(66%) and 24(75%) patients showed decreased eosinophil count and eosinophil ratio, and 18 (56%) patients showed increased C-reactive protein. Ten hematological indicators significantly differed in the blood of patients with COVID-19 and those with influenza A and B (P < 0.05). Eighteen hematological indicators significantly differed between patients with COVID-19 and negative patients (P < 0.05).  The positive rate of influenza A and B infection was higher than that of COVID-19. When pharyngeal swab collection may cause infection, fecal samples can be examined. Evaluation of pharyngeal swab and fecal samples can improve the positive rate of nucleic acid detection. The COVID-19 can cause some hematological indices changes.

title: Boosting the arsenal against COVID-19 through computational drug repurposing

title: Forthcoming articles page

title: Transmission patterns of smallpox: systematic review of natural outbreaks in Europe and North America since World War II
abstract: Because smallpox (variola major) may be used as a biological weapon, we reviewed outbreaks in post-World War II Europe and North America in order to understand smallpox transmission patterns.  A systematic review was used to identify papers from the National Library of Medicine, Embase, Biosis, Cochrane Library, Defense Technical Information Center, WorldCat, and reference lists of included publications. Two authors reviewed selected papers for smallpox outbreaks.  51 relevant outbreaks were identified from 1,389 publications. The median for the effective first generation reproduction rate (initial R) was 2 (range 0–38). The majority outbreaks were small (less than 5 cases) and contained within one generation. Outbreaks with few hospitalized patients had low initial R values (median of 1) and were prolonged if not initially recognized (median of 3 generations); outbreaks with mostly hospitalized patients had higher initial R values (median 12) and were shorter (median of 3 generations). Index cases with an atypical presentation of smallpox were less likely to have been diagnosed with smallpox; outbreaks in which the index case was not correctly diagnosed were larger (median of 27.5 cases) and longer (median of 3 generations) compared to outbreaks in which the index case was correctly diagnosed (median of 3 cases and 1 generation).  Patterns of spread during Smallpox outbreaks varied with circumstances, but early detection and implementation of control measures is a most important influence on the magnitude of outbreaks. The majority of outbreaks studied in Europe and North America were controlled within a few generations if detected early.

title: An evaluation of the inhibitory effects against rotavirus infection of edible plant extracts
abstract: Rotaviruses are the single most important cause of severe diarrhea in young children worldwide. The developments of specific, potent and accessible antiviral treatments that restrain rotavirus infection remain important to control rotavirus disease.  150 plant extracts with nutritional applications were screened in vitro on MA-104 cells for their antiviral activity against rhesus rotavirus (RRV). One extract (Aspalathus linearis (Burm.f.) R.Dahlgren) was also tested for its effect on the loss of transepithelial resistance (TER) of Caco-2 cells caused by simian rotavirus (SA-11) infection.  Aqueous extracts of Nelumbo nucifera Gaertn. fruit, Urtica dioica L. root, Aspalathus linearis (Burm.f.) R.Dahlgren leaves, Glycyrrhiza glabra L. root and Olea europaea L. leaves were found to have strong significant antiviral activity with a 50% inhibitory concentration (IC50) < 300 μg/ml. The pure compound 18ß-glycyrrhetinic acid from Glycyrrhiza glabra was found to have the strongest antiviral activity (IC50 46 μM), followed by luteolin and vitexin from Aspalathus linearis (IC50 respectively 116 μM and 129 μM) and apigenin-7-O-glucoside from Melissa officinalis (IC50 150 μM). A combination of Glycyrrhiza glabra L. + Nelumbo nucifera Gaertn. and Urtica dioica L. + Nelumbo nucifera Gaertn. showed synergy in their anti-viral activities. Aspalathus linearis (Burm.f.) R.Dahlgren showed no positive effect on the maintenance of the TER.  These results indicate that nutritional intervention with extracts of Nelumbo nucifera Gaertn., Aspalathus linearis (Burm.f.) R.Dahlgren, Urtica dioica L., Glycyrrhiza glabra L. and Olea europaea L. might be useful in the treatment of diarrhea caused by rotavirus infection.

title: Tracing patients' PLOD with mobile phones: Mitigation of epidemic risks through patients' locational open data
abstract: In the cases when public health authorities confirm a patient with highly contagious disease, they release the summaries about patient locations and travel information. However, due to privacy concerns, these releases do not include the detailed data and typically comprise the information only about commercial facilities and public transportation used by the patients. We addressed this problem and proposed to release the patient location data as open data represented in a structured form of the information described in press releases. Therefore, residents would be able to use these data for automated estimation of the potential risks of contacts combined with the location information stored in their mobile phones. This paper proposes the design of the open data based on Resource Description Framework (RDF), and performs a preliminary evaluation of the first draft of the specification followed by a discussion on possible future directions.

title: COVID-19 and Inflammatory Bowel Diseases: risk assessment, shared molecular pathways and therapeutic challenges
abstract: The novel coronavirus SARS-CoV-2 causing COVID-19 disease is yielding a global outbreak with serious threats to public health. In this paper, we aimed to review the current knowledge about COVID-19 infectious risk status in inflammatory bowel disease (IBD) patients requiring immunosuppressive medication. Also, we focused on several molecular insights that could explain why IBD patients appear to not have higher risks of infection and worse outcome in COVID-19 than the general population, in attempt to provide scientific support for safer decisions in IBD patient care.  PubMed electronic database was interogated for relevant articles involving data about common molecular pathways and shared treatment strategies between SARS-CoV-2, SARS-CoV-1, MERS-CoV and inflammatory bowel diseases. In addition, Neural Covidex, an artificial intelligence tool, was used to answer queries about pathogenic coronaviruses and possible IBD interactions using the COVID-19 Open Research Dataset (CORD-19). Discussions. Few molecular and therapeutic interactions between IBD and pathogenic coronaviruses were explored. First, we showed how the activity of soluble angiotensin-converting enzyme 2, CD209L alternate receptor and phosphorylated subunit of eukaryotic translation initiation factor 2 might exert protective impact in IBD in case of coronavirus infection. Second, IBD medication was discussed in the context of possible beneficial effects on COVID-19 pathogeny including "cytokine storm" prevention and treatment, immunomodulation, interferon signaling blocking, viral endocytosis inhibition.  Using current understanding of SARS-CoV-2 as well as other pathogenic coronaviruses immunopathology, we showed why IBD patients should not be considered at an increased risk of infection or more severe outcomes. Whether our findings are entirely applicable to the pathogenesis, disease susceptibility and treatment management of SARS-CoV-2 infection in IBD must be further explored.

title: COVID19-Tracker: A shiny app to produce comprehensive data visualization for SARS-CoV-2 epidemic in Spain
abstract: Data visualization is an essential tool for exploring and communicating findings in medical research, and especially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of the SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases, intensive care unit admissions, and mortality, from February 24th, 2020 onwards. Two applications have already been developed; 1) to analyze data trends and estimating short-term projections; 2) to estimate the case fatality rate, and; 3) To assess the effect of the lockdown measures on the trends of incident data. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain.

title: GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm
abstract: In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121 and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is adapted to determine the best values for the hyperparameters of the DenseNet121 architecture, and to achieve a high level of accuracy in diagnosing COVID-19 disease through chest x-ray image analysis. The obtained results showed that the proposed approach was able to correctly classify 98% of the test set. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121, it was compared to another optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19 and its ability to better diagnose COVID-19 disease than the SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. As well as, the proposed approach was compared to an approach based on a CNN architecture called Inception-v3 and the manual search method for determining the values of the hyperparameters. The results of the comparison showed that the GSA-DenseNet121 was able to beat the other approach, as the second was able to classify only 95% of the test set samples.

title: Emerging research fronts in science and technology: patterns of new knowledge development
abstract: Research fronts represent the most dynamic areas of science and technology and the areas that attract the most scientific interest. We construct a methodology to identify these fronts, and we use quantitative and qualitative methodology to analyze and describe them. Our methodology is able to identify these fronts as they form—with potential use by firms, venture capitalists, researchers, and governments looking to identify emerging high-impact technologies. We also examine how science and technology absorbs the knowledge developed in these fronts and find that fronts which maximize impact have very different characteristics than fronts which maximize growth, with consequences for the way science develops over time.

title: Scientific Abstracts

title: Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
abstract: Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNNs) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and adapt our previously developed CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 95.12% (with a sensitivity of 97.91%, a specificity of 91.87%, and a precision of 93.36%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.

title: Shortlisting SARS-CoV-2 Peptides for Targeted Studies from Experimental Data-Dependent Acquisition Tandem Mass Spectrometry Data
abstract: Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a crucial tool for fighting the COVID-19 pandemic. This dataset brief presents the exploration of a shotgun proteomics dataset acquired on SARS-CoV-2 infected Vero cells. Proteins from inactivated virus samples were extracted, digested with trypsin, and the resulting peptides were identified by data-dependent acquisition tandem mass spectrometry. The 101 peptides reporting for six viral proteins were specifically analyzed in terms of their analytical characteristics, species specificity and conservation, and their proneness to structural modifications. Based on these results, a shortlist of 14 peptides from the N, S, and M main structural proteins that could be used for targeted mass-spectrometry method development and diagnostic of the new SARS-CoV-2 is proposed and the best candidates are commented.

title: An evidence-based framework for priority clinical research questions for COVID-19
abstract: On 31 December, 2019, the World Health Organization China Country Office was informed of cases of pneumonia of unknown aetiology. Since then, there have been over 75 000 cases globally of the 2019 novel coronavirus (COVID-19), 2000 deaths, and over 14 000 cases recovered. Outbreaks of novel agents represent opportunities for clinical research to inform real-time public health action. In 2018, we conducted a systematic review to identify priority research questions for Severe Acute Respiratory Syndrome-related coronavirus (SARS-CoV) and Middle East Respiratory Syndrome-related coronavirus (MERS-CoV). Here, we review information available on COVID-19 and provide an evidenced-based framework for priority clinical research in the current outbreak.  Three bibliographic databases were searched to identify clinical studies published on SARS-CoV and MERS-CoV in the outbreak setting. Studies were grouped thematically according to clinical research questions addressed. In February 2020, available information on COVID19 was reviewed and compared to the results of the SARS-CoV and MERS-CoV systematic review.  From the research objectives for SARS-CoV and MERS-CoV, ten themes in the literature were identified: Clinical characterisation, prognosis, diagnosis, clinical management, viral pathogenesis, epidemiological characterisation, infection prevention and control/transmission, susceptibility, psychosocial, and aetiology. For COVID19, some information on clinical presentation, diagnostic testing, and aetiology is available but many clinical research gaps have yet to be filled.  Based on a systematic review of other severe coronaviruses, we summarise the state of clinical research for COVID-19, highlight the research gaps, and provide recommendations for the implementation of standardised protocols. Data based on internationally standardised protocols will inform clinical practice real-time.

title: Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery
abstract: A study with a societal objective was carried out on people exchanging on social networks and more particularly on Twitter to observe their feelings on the COVID-19. A dataset of more than 600,000 tweets with hashtags like COVID and coronavirus posted between February 27, 2020 and March 25, 2020 was built. An exploratory treatment of the number of tweets posted by country, by language and other parameters revealed an overview of the apprehension of the pandemic around the world. A sentiment analysis was elaborated on the basis of the tweets posted in English because these constitute the great majority. On the other hand, the FP-Growth algorithm was adapted to the tweets in order to discover the most frequent patterns and its derived association rules, in order to highlight the tweeters insights relatively to COVID-19.

title: CoronaVis: A Real-time COVID-19 Tweets Data Analyzer and Data Repository
abstract: Due to the nature of the data and public interaction, twitter is becoming more and more useful to understand and model various events. The goal of CoronaVis is to use tweets as the information shared by the people to visualize topic modeling, study subjectivity, and to model the human emotions during the COVID-19 pandemic. The main objective is to explore the psychology and behavior of the societies at large which can assist in managing the economic and social crisis during the ongoing pandemic as well as the after-effects of it. The novel coronavirus (COVID-19) pandemic forced people to stay at home to reduce the spread of the virus by maintaining social distancing. However, social media is keeping people connected both locally and globally. People are sharing information (e.g. personal opinions, some facts, news, status, etc.) on social media platforms which can be helpful to understand the various public behavior such as emotions, sentiments, and mobility during the ongoing pandemic. In this work, we develop a live application to observe the tweets on COVID-19 generated from the USA. In this paper, we have generated various data analytics over a period of time to study the changes in topics, subjectivity, and human emotions. We also share a cleaned and processed dataset named CoronaVis Twitter dataset (focused on the United States) available to the research community at https://github.com/mykabir/COVID19. This will enable the community to find more useful insights and create different applications and models to fight with COVID-19 pandemic and future pandemics as well.

title: A single-cell RNA expression map of human coronavirus entry factors
abstract: To predict the tropism of human coronaviruses, we profile 28 SARS-CoV-2 and coronavirus-associated receptors and factors (SCARFs) using single-cell RNA-sequencing data from a wide range of healthy human tissues. SCARFs include cellular factors both facilitating and restricting viral entry. Among adult organs, enterocytes and goblet cells of the small intestine and colon, kidney proximal tubule cells, and gallbladder basal cells appear most permissive to SARS-CoV-2, consistent with clinical data. Our analysis also suggests alternate entry paths for SARS-CoV-2 infection of the lung, central nervous system, and heart. We predict spermatogonial cells and prostate endocrine cells, but not ovarian cells, to be highly permissive to SARS-CoV-2, suggesting male-specific vulnerabilities. Early stages of embryonic and placental development show a moderate risk of infection. The nasal epithelium looks like another battleground, characterized by high expression of both promoting and restricting factors and a potential age-dependent shift in SCARF expression. Lastly, SCARF expression appears broadly conserved across human, chimpanzee and macaque organs examined. Our study establishes an important resource for investigations of coronavirus biology and pathology.

title: COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images
abstract: Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in most medical centers and hence in many cases CXR images become the most time/cost effective tool for assisting clinicians in making decisions. Deep learning neural networks have a great potential for building triage systems for detecting COVID-19 patients, especially patients with low severity. Unfortunately, current databases do not allow building such systems as they are highly heterogeneous and biased towards severe cases. This paper is three-fold: (i) we demystify the high sensitivities achieved by most recent COVID-19 classification models, (ii) under a close collaboration with Hospital Universitario Cl\'inico San Cecilio, Granada, Spain, we built COVIDGR-1.0, a homogeneous and balanced database that includes all levels of severity, from Normal with positive RT-PCR, Mild, Moderate to Severe. COVIDGR-1.0 contains 377 positive and 377 negative PA (PosteroAnterior) CXR views and (iii) we propose COVID Smart Data based Network (COVID-SDNet) methodology for improving the generalization capacity of COVID-classification models. Our approach reaches good and stable results with an accuracy of $97.37\% \pm 1.86 \%$, $88.14\% \pm 2.02\%$, $66.5\% \pm 8.04\%$ in severe, moderate and mild COVID severity levels. Our approach could help in the early detection of COVID-19. COVIDGR-1.0 dataset will be made available after the review process.

title: Low serum 25-hydroxyvitamin D levels are associated with increased risk of viral coinfections in wheezing children

title: SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far
abstract: In December 2019, a cluster of fatal pneumonia cases presented in Wuhan, China. They were caused by a previously unknown coronavirus. All patients had been associated with the Wuhan Wholefood market, where seafood and live animals are sold. The virus spread rapidly and public health authorities in China initiated a containment effort. However, by that time, travelers had carried the virus to many countries, sparking memories of the previous coronavirus epidemics, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), and causing widespread media attention and panic. Based on clinical criteria and available serological and molecular information, the new disease was called coronavirus disease of 2019 (COVID-19), and the novel coronavirus was called SARS Coronavirus-2 (SARS-CoV-2), emphasizing its close relationship to the 2002 SARS virus (SARS-CoV). The scientific community raced to uncover the origin of the virus, understand the pathogenesis of the disease, develop treatment options, define the risk factors, and work on vaccine development. Here we present a summary of current knowledge regarding the novel coronavirus and the disease it causes.

title: Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound.
abstract: Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DLbased solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, videolevel, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

title: Constructing Co-occurrence Network Embeddings to Assist Association Extraction for COVID-19 and Other Coronavirus Infectious Diseases
abstract: As COVID-19 started its rapid emergence and gradually transformed into an unprecedented pandemic, the need for having a knowledge repository for the disease became crucial. To address this issue, a new COVID-19 machine readable dataset known as COVID-19 Open Research Dataset (CORD-19) has been released. Based on this, our objective was to build a computable co-occurrence network embeddings to assist association detection amongst COVID-19 related biomedical entities.  Leveraging a Linked Data version of CORD-19 (i.e., CORD-19-on-FHIR), we first utilized SPARQL to extract co-occurrences among chemicals, diseases, genes, and mutations and build a co-occurrence network. We then trained the representation of the derived co-occurrence network using node2vec with four edge embeddings operations (L1, L2, Average, and Hadamard). Six algorithms (Decision Tree, Linear Regression, Support Vector Machine, Random Forest, Naive Bayes, and Multi-layer Perceptron) were applied to evaluate performance on link prediction. An unsupervised learning strategy was also developed incorporating the t-SNE and DBSCAN algorithms for case studies.  Random Forest classifier showed the best performance on link prediction across different network embeddings. For edge embeddings generated using the Average operation, Random Forest achieved the optimal average precision of 0.97 and F1 score of 0.90. For unsupervised learning, 63 clusters were formed with silhouette score of 0.128. Significant associations were detected for five coronavirus infectious diseases in their corresponding subgroups.  In this study, we constructed COVID-19-centered co-occurrence network embeddings. Results indicated that the generated embeddings were able to extract significant associations for COVID-19 and coronavirus infectious diseases.

title: On the evolutionary epidemiology of SARS-CoV-2
abstract: There is no doubt that the novel coronavirus SARS-CoV-2 that causes COVID-19 is mutating and thus has the potential to adapt during the current pandemic. Whether this evolution will lead to changes in the transmission, the duration, or the severity of the disease is not clear. This has led to considerable scientific and media debate, from raising alarms about evolutionary change to dismissing it. Here we review what little is currently known about the evolution of SARS-CoV-2 and extend existing evolutionary theory to consider how selection might be acting upon the virus during the COVID-19 pandemic. While there is currently no definitive evidence that SARS-CoV-2 is undergoing further adaptation, continued, evidence-based, analysis of evolutionary change is important so that public health measures can be adjusted in response to substantive changes in the infectivity or severity of COVID-19.

title: Can AI help in screening Viral and COVID-19 pneumonia?
abstract: Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.

title: COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature
abstract: The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. We also designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared clinical symptoms and clinical findings of COVID-19 vs. those of influenza to demonstrate the disease differences in the scientific publications. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.

title: Dataset on the COVID-19 Pandemic Situation in Tunisia with application to SIR Model
abstract: April 9, 2020 marks 100 days since first cases of the Coronavirus Disease 2019 in China. In this crucial day with 1 436 198 confirmed infected cases in the world (New confirmed infected cases 82 837) and 85 521 deaths (6286 New daily deaths), the Global Level of the Covid-19 pandemic is evaluated at very high according to the World Health Organization (WHO) situation report. For most people, COVID-19 infection will cause mild illness (fever and at least one sign/symptom of respiratory disease), however for more than 3.4% of people, it can be fatal. Older people and those with pre-existing medical conditions (such as cardiovascular disease, chronic respiratory disease or diabetes) are at risk for severe disease and mortality. The incubation period of the virus is estimated to be between 2 and 14 days but longer incubation are reported. Furthermore, Data published by world authorities shows that statistics are different for different geographical regions and depends on many social and environmental factors. The sad reality of the COVID-19 is that there are currently no medications or vaccines proven to be effective for the treatment or prevention of the disease. The pandemic spread is consequenctly followed by a worldwide panic. Facing this dramatic uncertain situation, implementing a country wide strategy for social distancing and a general logistic policy for critical and life-saving supplies is an urgency for governomental and sanitaire authorities. Several mathematical models have been proposed for developed countries to predict the epidemic spread. However these models can't be adapted to developing countries due to the major differences in geographic, societal, economic and political adopted strategies. Here, we propose the application of the well known SIR model to the case study of Tunisia where Data are collected from three official databases showing differences in reporting methods. The proposed data could be useful to predict the spread of COVID-2019 and design a more reliable model that can estimate the finale size of the spread and help in monitoring infection control.

title: Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
abstract: ( The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.

title: Joint spatio-temporal analysis of multiple response types using the hierarchical generalized transformation model with application to coronavirus disease 2019 and social distancing
abstract: Social distancing can be described as an effort to maintain a physical distance between individuals and has become a necessary public health measure to combat cornoavirus disease 2019 (COVID-19). Social distancing is known to weaken incidences and deaths due to COVID-19, however, there are detrimental economic and psychological effects. This motivates us to analyze incidences (and deaths) of COVID-19 along with a measure of the health of the US economy (i.e., the adjusted closing price of the Dow Jones Industrial), and a measure of the public interest in COVID-19 through Google Trends data. The model we implement is developed to be easily adapted to a data scientist's preferred method for continuous data, which is done to aid future analyses of this important dataset. This dataset consists of multiple response types (e.g., continuous-valued, count-valued, binomial counts). Thus, we introduce a reasonable easy-to-implement all-purpose method that"converts"a statistical model for continuous responses (the preferred model) into a Bayesian model for multi-response data sets. To do this, we transform the data such that the continuous-valued transformed data can be reasonably modeled using the preferred model and the transformation itself is treated as unknown. The implementation of our approach involves two steps. The first step produces posterior replicates of the transformed data using a latent conjugate multivariate (LCM) model. The second step involves generating values from the posterior distribution implied by the preferred model. We refer to our model as the hierarchical generalized transformation (HGT) model. In a simulation, we demonstrate the flexibility of the HGT model by incorporating two different preferred models: Bayesian additive regression trees (BART) and the spatial mixed effects (spatio-temporal mixed effects) models.

title: Public Engagement and Government Responsiveness in the Communications About COVID-19 During the Early Epidemic Stage in China: Infodemiology Study on Social Media Data
abstract: Effective risk communication about the outbreak of a newly emerging infectious disease in the early stage is critical for managing public anxiety and promoting behavioral compliance. China has experienced the unprecedented epidemic of the coronavirus disease (COVID-19) in an era when social media has fundamentally transformed information production and consumption patterns.  This study examined public engagement and government responsiveness in the communications about COVID-19 during the early epidemic stage based on an analysis of data from Sina Weibo, a major social media platform in China.  Weibo data relevant to COVID-19 from December 1, 2019, to January 31, 2020, were retrieved. Engagement data (likes, comments, shares, and followers) of posts from government agency accounts were extracted to evaluate public engagement with government posts online. Content analyses were conducted for a random subset of 644 posts from personal accounts of individuals, and 273 posts from 10 relatively more active government agency accounts and the National Health Commission of China to identify major thematic contents in online discussions. Latent class analysis further explored main content patterns, and chi-square for trend examined how proportions of main content patterns changed by time within the study time frame.  The public response to COVID-19 seemed to follow the spread of the disease and government actions but was earlier for Weibo than the government. Online users generally had low engagement with posts relevant to COVID-19 from government agency accounts. The common content patterns identified in personal and government posts included sharing epidemic situations; general knowledge of the new disease; and policies, guidelines, and official actions. However, personal posts were more likely to show empathy to affected people (χ(2)(1)=13.3, P<.001), attribute blame to other individuals or government (χ(2)(1)=28.9, P<.001), and express worry about the epidemic (χ(2)(1)=32.1, P<.001), while government posts were more likely to share instrumental support (χ(2)(1)=32.5, P<.001) and praise people or organizations (χ(2)(1)=8.7, P=.003). As the epidemic evolved, sharing situation updates (for trend, χ(2)(1)=19.7, P<.001) and policies, guidelines, and official actions (for trend, χ(2)(1)=15.3, P<.001) became less frequent in personal posts but remained stable or increased significantly in government posts. Moreover, as the epidemic evolved, showing empathy and attributing blame (for trend, χ(2)(1)=25.3, P<.001) became more frequent in personal posts, corresponding to a slight increase in sharing instrumental support, praising, and empathizing in government posts (for trend, χ(2)(1)=9.0, P=.003).  The government should closely monitor social media data to improve the timing of communications about an epidemic. As the epidemic evolves, merely sharing situation updates and policies may be insufficient to capture public interest in the messages. The government may adopt a more empathic communication style as more people are affected by the disease to address public concerns.

title: Tympanic, infrared skin, and temporal artery scan thermometers compared with rectal measurement in children: a real-life assessment.
abstract: Body temperature measurement in children is of clinical relevance. Although rectal measurement is the gold standard, less invasive tools have become available. We aimed to describe the accuracy of tympanic, infrared skin, or temporal artery scan thermometers compared with rectal measurement to reflect core temperature.  Rectal (Filac 3000; Covidien, Mechelen, Belgium), tympanic (AccuSystem Genius2 Typmanic Infrared Ear Thermometer, Covidien, Mechelen, Belgium), temporal artery scan (Exergen, Exergen Corp, Watertown, Massachusetts), and infrared (ThermoFlash Contactless Medical Electronic Thermometer, Visiomedlab, Paris, France) body temperature measurements were randomly performed and readings were collected once. Temperature readings were described as median and range, and observations were compared with rectal temperature readings (using Wilcoxon, Bland-Altman, sensitivity, and specificity tests). The child's comfort was assessed by the child, parent, and nurse (using Likert scales) and ease of use was assessed by nurses (using visual analog scale).  Based on observations in 294 (median age = 3.2 years, range = 0.02-17 years) children, the mean difference was 0.49°C (tympanic scan; P < 0.0001), 0.34°C (infrared skin scan; P < 0.0001), and 0°C (temporal artery scan; P = 0.9288), respectively, when compared with rectal temperature readings. Based on visual inspection of Bland-Altman plots, all tools overestimated the temperature at lower body temperature and underestimated the temperature at higher body temperature, resulting in a sensitivity of 22% to 41% and a specificity of 98% to 100% for rectal temperatures above 38°C. The Likert scale scores and the visual analog scale scores for rectal measurement were only slightly higher when compared with the other methods.  All noninvasive techniques underperformed compared with rectal measurement. The temporal artery scan deviations were smallest, but all noninvasive techniques overestimate lower temperatures and underestimate higher temperatures compared with rectal measurement. In our hands, temporal artery scan measurement seems to be second best, but not yet ideal.

title: Virus-host interactomics: new insights and opportunities for antiviral drug discovery
abstract: The current therapeutic arsenal against viral infections remains limited, with often poor efficacy and incomplete coverage, and appears inadequate to face the emergence of drug resistance. Our understanding of viral biology and pathophysiology and our ability to develop a more effective antiviral arsenal would greatly benefit from a more comprehensive picture of the events that lead to viral replication and associated symptoms. Towards this goal, the construction of virus-host interactomes is instrumental, mainly relying on the assumption that a viral infection at the cellular level can be viewed as a number of perturbations introduced into the host protein network when viral proteins make new connections and disrupt existing ones. Here, we review advances in interactomic approaches for viral infections, focusing on high-throughput screening (HTS) technologies and on the generation of high-quality datasets. We show how these are already beginning to offer intriguing perspectives in terms of virus-host cell biology and the control of cellular functions, and we conclude by offering a summary of the current situation regarding the potential development of host-oriented antiviral therapeutics.

title: COVID-19: Open-data resources for monitoring, modeling, and forecasting the epidemic
abstract: We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources.

title: Fully automatic deep convolutional approaches for the analysis of Covid-19 using chest X-ray images
abstract: Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose complementary fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To face these classification tasks, we exploited and adapted to this topic a densely convolutional network architecture, which connects each layer to every other layer in a feed-forward fashion. To validate the designed approaches, several representative experiments were performed using images retrieved from different public chest X-ray images datasets. Overall, satisfactory results were obtained from the designed experiments, facilitating the doctors' work and allowing better an early diagnostic/screening and treatment of this relevant pandemic pathology.

title: Videogames and guns in adolescents: T ests of a bipartite theory
abstract: The possible role of video gaming in imprinting aggressive and specifically gun-related behaviors has been elusive, and findings regarding these associations have been inconsistent. I address this gap by proposing and testing a bipartite theory that can explain inconsistent results regarding the previously assumed linear association between videogames and gun-related behaviors. The theory suggests that this association follows a U-shape. It posits that at low levels of video gaming time, video gaming displaces gun-related behaviors and shelters adolescents by keeping them occupied and by reducing opportunities and motivation to acquire guns. However, at some level of gaming time (because most popular games adolescents play include violent aspects), the assumed imprinting of aggressive behaviors overpowers the positive displacement force, and this can trivialize and naturalize gun-carrying behaviors, and ultimately increase motivation to obtain and carry guns. I tested this theory with two national samples of American adolescents (n1 = 24,779 and n2 = 26,543, out of which 403 and 378, respectively, reported bringing a gun to school in the last month). Multiple analyses supported the proposed U-shaped association. These findings show that the moral panic over video games is largely unsubstantiated, especially among light to moderate gamers.

title: An overview of coronaviruses including the SARS-2 coronavirus – Molecular biology, epidemiology and clinical implications
abstract: Coronavirus infections have emerged as epidemic and pandemic threats in last two decades. After the H1N1 influenza pandemic in 2009, recently diagnosed novel betacoronavirus or severe acute respiratory syndrome coronavirus (SARS-CoV)-2 has spread across 203 countries and territories in all 5 major continents. World Health Organization (WHO) declared this as a public health emergency of international concern on January 30, 2020. Subsequently on February 11, 2020 a new name was given to this disease i.e. COVID-19 by an expert group from WHO. As of April 12, 2020, 10:00 CET, GMT+2:00, 1,696,588 confirmed cases and 105,952 confirmed deaths have been reported to the WHO. (Coronavirus disease 2019, situation report 83). It possibly originated from a small animal market in Wuhan, China. A cluster of patients were admitted with unusual pneumonia not responding to treatment in various hospitals. Epidemiological, genomic analysis and correlation with other coronaviruses led to the isolation of new coronavirus, closely resembling the bat coronaviruses, from such patients in Wuhan. They were identified as the SARS-CoV-2. This virus infection presents as influenza like illness in the affected people. Fever, cough, respiratory distress with fatigue, diarrhea, nausea and vomiting are common symptoms seen in adults. This may progress on to respiratory distress, hypoxia, need for oxygen supplementation and ventilator support as seen in patients in the SARS-CoV-1 epidemic (2003) in Guangdong, China. The transmissibility of SARS-CoV-1 was less as compared to SARS-CoV-2 infection, and it was well controlled with good public health efforts. The present COVID-19 epidemic is still in the acceleration phase of 3 and 4 in various countries. Without any effective antiviral agents available at present, the need of the hour is early case detection, isolation of cases, use of good preventive care measures by the household contacts and in the hospital set up. The results of ongoing clinical trials on hydroxychloroquine, azithromycin alone or in combination and a new antiviral agent remdesivir may help to treat some of the infections. A need for effective vaccine is being seen an as good preventive strategy in this pandemic. However the results of clinical trials and incorporation of vaccines in public health programs is a long way to go.

title: Smoking, SARS-CoV-2 and COVID-19: A review of reviews considering implications for public health policy and practice
abstract: There has been significant speculation regarding the association between the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pathogen, coronavirus disease (COVID-19) and smoking. We provide an overview of the available literature regarding the association between smoking, risk of SARS-CoV-2 infection, and risk of severe COVID-19 and poor clinical outcomes, with the aim of informing public health policy and practice, particularly in England.  Publications were identified utilising a systematic search approach on PUBMED and Google Scholar. Publications presenting a systematic review or meta-analysis considering the association between smoking and SARS-COV-2 infection or COVID-19 outcomes were included.  Eight studies were identified. One considered the relationship between smoking and the probability of SARS-CoV-2 infection, three considered the association between COVID-19 hospitalisation and smoking history, and six reviewed the association between smoking history and development of severe COVID-19. One study specifically investigated the risk of mortality. The studies considering risk of severe disease indicate that there is a significant association between COVID-19 and current or ever smoking.  This is a rapidly evolving topic. Current analysis remains limited due to the quality of primary data, although, early results indicate an association between smoking and COVID-19 severity. We highly recommend public health messaging to continue focusing on smoking cessation efforts.

title: ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks
abstract: In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 31st of March 2020. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pandemic that includes around 748k popular tweets (according to Twitter search criterion) alongside the propagation networks of the most-popular subset of them. The propagation networks include both retweets and conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others. Preliminary analysis shows that ArCOV-19 captures rising discussions associated with the first reported cases of the disease as they appeared in the Arab world. In addition to the source tweets and the propagation networks, we also release the search queries and the language-independent crawler used to collect the tweets to encourage the curation of similar datasets.

title: Preliminary epidemiological analysis on children and adolescents with novel coronavirus disease 2019 outside Hubei Province, China: an observational study utilizing crowdsourced data
abstract: The outbreak of coronavirus disease 2019 (COVID-19) continues to expand across the world. Though both the number of cases and mortality rate in children and adolescents is reported to be low in comparison to adults, limited data has been reported on the outbreak with respect to pediatric patients. To elucidate information, we utilized crowdsourced data to perform a preliminary epidemiologic analysis of pediatric patients with COVID-19  In this observational study, data was collected from two open-access, line list crowdsourced online databases. Pediatric cases of COVID-19 were defined as patients ≤19 years of age with a laboratory confirmed diagnosis. The primary outcomes were case counts and cumulative case counts. Secondary outcomes included days between symptoms onset and first medical care and days between first medical care and reporting. Tertiary outcomes were rate of travel to Wuhan, rate of infected family members and rates of symptoms.  A total of 82 patients were included. The median age was 10 [IQR: 5-15] years. Patients from mainland China (outside Hubei) accounted for 46.3% of cases, while the remaining 53.7% of cases were international. Males and females accounted for 52.4% and 32.9% of cases, respectively, with the remaining 14.6% being designated as unknown. A male skew persisted across subgroup analyses by age group (p=1.0) and location (inside/outside China) (p=0.22). While the number of reported international cases has been steadily increasing over the study period, the number of reported cases in China rapidly decreased from the start point. The median reporting delay was 3 [IQR: 2-4.8] days. The median delay between symptom onset and first seeking medical care was 1 [IQR: 0-3.25] day. In international cases, time to first seeking medical care was a median of 2.5 days longer than in China (p=0.04). When clinical features were reported, fever was the most common presentation (68.0%), followed by cough (36.0%).  The number of reported international pediatric COVID-19 cases is rapidly increasing. COVID-19 infections are, to-date, more common in males than females in both the children and adolescent age groups. Additionally, this male predominance remains the case both inside and outside of China. Crowdsourced data enabled early analysis of epidemiologic variables in pediatric patients with COVID-19. Further data sharing is required to enable analyses that are required to understand the course of this infection in children.

title: Factor V Leiden mutation does not affect coagulopathy or outcome in lethal H1N1 influenza.
abstract: Influenza A is a major cause of mortality. Knowledge on coagulation activation in influenza infection is limited. The factor V Leiden (FVL) mutation is possibly subject to positive selection pressure. It is unknown whether this mutation impacts on the outcome of severe influenza. In the present study, the effect of lethal influenza on pulmonary and systemic coagulation activation and whether or not FVL mutation alters coagulation activation in and the course of lethal influenza, was determined. Wild-type mice, and mice heterozygous or homozygous for FVL were infected intranasally with a lethal dose of H1N1 (haemagglutinin 1 and neuraminidase 1) influenza A. Mice were sacrificed after 48 or 96 h for determination of coagulation activation, histopathology, pulmonary inflammatory parameters and viral load, or were observed in a survival study. Extensive local and systemic coagulation activation during lethal influenza was demonstrated by increased lung and plasma levels of thrombin-antithrombin complexes and fibrin degradation products, and by pulmonary fibrin deposition. FVL mutation did not influence the procoagulant response, lung histopathology or survival. FVL mice demonstrated elevated viral loads 48 h after infection. In conclusion, coagulation is activated locally and systemically during lethal murine influenza A infection. The FVL mutation does not influence coagulation activation, lung inflammation or survival in lethal influenza A.

title: Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors
abstract: The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes, airway club cells, and ciliated cells are likely underappreciated targets of SARS-CoV-2 infection, in addition to type II pneumocytes and olfactory epithelial cells. We further identify mature small intestinal enterocytes as a possible hotspot of COVID-19 fecal-oral transmission, where an intriguing maturation-correlated transcriptional signature is shared between ACE2 and the other coronavirus receptors DPP4 (MERS-CoV) and ANPEP (α-coronavirus). This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses. The nferX Platform Single-cell resource -

title: Role of Human Sec63 in Modulating the Steady-State Levels of Multi-Spanning Membrane Proteins
abstract: The Sec61 translocon of the endoplasmic reticulum (ER) membrane forms an aqueous pore, allowing polypeptides to be transferred across or integrated into membranes. Protein translocation into the ER can occur co- and posttranslationally. In yeast, posttranslational translocation involves the heptameric translocase complex including its Sec62p and Sec63p subunits. The mammalian ER membrane contains orthologs of yeast Sec62p and Sec63p, but their function is poorly understood. Here, we analyzed the effects of excess and deficit Sec63 on various ER cargoes using human cell culture systems. The overexpression of Sec63 reduces the steady-state levels of viral and cellular multi-spanning membrane proteins in a cotranslational mode, while soluble and single-spanning ER reporters are not affected. Consistent with this, the knock-down of Sec63 increases the steady-state pools of polytopic ER proteins, suggesting a substrate-specific and regulatory function of Sec63 in ER import. Overexpressed Sec63 exerts its down-regulating activity on polytopic protein levels independent of its Sec62-interacting motif, indicating that it may not act in conjunction with Sec62 in human cells. The specific action of Sec63 is further sustained by our observations that the up-regulation of either Sec62 or two other ER proteins with lumenal J domains, like ERdj1 and ERdj4, does not compromise the steady-state level of a multi-spanning membrane reporter. A J domain-specific mutation of Sec63, proposed to weaken its interaction with the ER resident BiP chaperone, reduces the down-regulating capacity of excess Sec63, suggesting an involvement of BiP in this process. Together, these results suggest that Sec63 may perform a substrate-selective quantity control function during cotranslational ER import.

title: AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data
abstract: The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.

title: BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
abstract: This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests. The findings have been mapped onto standard Unified Medical Language System (UMLS) terminology and cover a wide spectrum of thoracic entities, unlike the considerably more reduced number of entities annotated in previous datasets. Images are stored in high resolution and entities are localized with anatomical labels and stored in a Medical Imaging Data Structure (MIDS) format. In addition, 10 images were annotated by a team of radiologists to include semantic segmentation of radiological findings. This first iteration of the database includes 1,380 CX, 885 DX and 163 CT studies from 1,311 COVID-19+ patients. This is, to the best of our knowledge, the largest COVID-19+ dataset of images available in an open format. The dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/bimcv-covid19.

title: [An increased neutrophil/lymphocyte ratio is an early warning signal of severe COVID-19].
abstract: To identify the biomarkers as early warning signals for severe COVID-19.  We retrospectively analyzed the clinical data of 63 patients with COVID- 19 from Hubei Provincial Hospital of Integrated Chinese and Western Medicine, including 32 moderate cases and 31 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory test results were compared between the two groups. Logistic regression analysis was performed to identify the factors that predicted the severity of COVID-19. The receiver- operating characteristic curve (ROC) of neutrophil/lymphocyte ratio (NLR) was calculated, and the area under the curve (AUC) was determined to estimate the optimal threshold of NLR for predicting severe cases of COVID-19.  The patients with moderate and server COVID-19 showed significant differences in the rate of diabetes, NLR, serum amyloid A (SSA), C-reactive protein (CRP) and serum albumin (ALB) levels (P < 0.05). The co- morbidity of diabetes, NLR, SSA and CRP were found to positively correlate and ALB to inversely correlate with the severity of COVID-19 (P < 0.05). Multivariate logistic regression analysis showed that NLR was an independent risk factor for severe COVID-19 (OR=1.264, 95% CI: 1.046-1.526, P=0.015) with an AUC of 0.831 (95% CI: 0.730-0.932), an optimal diagnostic threshold of 4.795, a sensitivity of 0.839, and a specificity of 0.750.  An increased NLR can serve as an early warning signal of severe COVID-19.

title: Perioperative preparations for COVID -19 -The Pediatric Cardiac Team Perspective

title: A database resource for Genome-wide dynamics analysis of Coronaviruses on a historical and global scale
abstract: The recent outbreak of a new zoonotic origin Coronavirus has ring the bell for the potential spread of epidemic Coronavirus crossing the species. With the urgent needs to assist the control of the Coronavirus spread and to provide valuable scientific information, we developed a coronavirus database (CoVdb), an online genomics and proteomics analysis platform. Based on public available coronavirus genomic information, the database annotates the genome of every strain and identifies 780 possible ORFs of all strains available in Genebank. In addition, the comprehensive evaluation of all the published genomes of Coronavirus strains, including population genetics analysis, functional analysis and structural analysis on a historical and global scale were presented in the CoVdb. In the database, the researcher can easily obtain the basic information of a Coronavirus gene with the distribution of the gene among strains, conserved or high mutation regions, possible subcellular location and topology of the gene. Moreover, sliding windows for population genetics analysis results is provided, thereby facilitating genetics and evolutional analysis at the genomic level. CoVdb can be accessed freely at http://covdb.popgenetics.net.

title: Coronavirus disease 2019 (COVID-19): current status and future perspectives
abstract: Coronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date. COVID-19 is caused by a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [previously provisionally known as 2019 novel coronavirus (2019-nCoV)]. At present, the newly identified SARS-CoV-2 has caused a large number of deaths with tens of thousands of confirmed cases worldwide, posing a serious threat to public health. However, there are no clinically approved vaccines or specific therapeutic drugs available for COVID-19. Intensive research on the newly emerged SARS-CoV-2 is urgently needed to elucidate the pathogenic mechanisms and epidemiological characteristics and to identify potential drug targets, which will contribute to the development of effective prevention and treatment strategies. Hence, this review will focus on recent progress regarding the structure of SARS-CoV-2 and the characteristics of COVID-19, such as the aetiology, pathogenesis and epidemiological characteristics.

title: Suppression of innate antiviral response by severe acute respiratory syndrome coronavirus M protein is mediated through the first transmembrane domain
abstract: Coronaviruses have developed various measures to evade innate immunity. We have previously shown that severe acute respiratory syndrome (SARS) coronavirus M protein suppresses type I interferon (IFN) production by impeding the formation of functional TRAF3-containing complex. In this study, we demonstrate that the IFN-antagonizing activity is specific to SARS coronavirus M protein and is mediated through its first transmembrane domain (TM1) located at the N terminus. M protein from human coronavirus HKU1 does not inhibit IFN production. Whereas N-linked glycosylation of SARS coronavirus M protein has no influence on IFN antagonism, TM1 is indispensable for the suppression of IFN production. TM1 targets SARS coronavirus M protein and heterologous proteins to the Golgi apparatus, yet Golgi localization is required but not sufficient for IFN antagonism. Mechanistically, TM1 is capable of binding with RIG-I, TRAF3, TBK1 and IKKε, and preventing the interaction of TRAF3 with its downstream effectors. Our work defines the molecular architecture of SARS coronavirus M protein required for suppression of innate antiviral response.

title: Accuracy of epidemiological inferences based on publicly available information: retrospective comparative analysis of line lists of human cases infected with influenza A(H7N9) in China
abstract: Appropriate public health responses to infectious disease threats should be based on best-available evidence, which requires timely reliable data for appropriate analysis. During the early stages of epidemics, analysis of ‘line lists’ with detailed information on laboratory-confirmed cases can provide important insights into the epidemiology of a specific disease. The objective of the present study was to investigate the extent to which reliable epidemiologic inferences could be made from publicly-available epidemiologic data of human infection with influenza A(H7N9) virus.  We collated and compared six different line lists of laboratory-confirmed human cases of influenza A(H7N9) virus infection in the 2013 outbreak in China, including the official line list constructed by the Chinese Center for Disease Control and Prevention plus five other line lists by HealthMap, Virginia Tech, Bloomberg News, the University of Hong Kong and FluTrackers, based on publicly-available information. We characterized clinical severity and transmissibility of the outbreak, using line lists available at specific dates to estimate epidemiologic parameters, to replicate real-time inferences on the hospitalization fatality risk, and the impact of live poultry market closure.  Demographic information was mostly complete (less than 10% missing for all variables) in different line lists, but there were more missing data on dates of hospitalization, discharge and health status (more than 10% missing for each variable). The estimated onset to hospitalization distributions were similar (median ranged from 4.6 to 5.6 days) for all line lists. Hospital fatality risk was consistently around 20% in the early phase of the epidemic for all line lists and approached the final estimate of 35% afterwards for the official line list only. Most of the line lists estimated >90% reduction in incidence rates after live poultry market closures in Shanghai, Nanjing and Hangzhou.  We demonstrated that analysis of publicly-available data on H7N9 permitted reliable assessment of transmissibility and geographical dispersion, while assessment of clinical severity was less straightforward. Our results highlight the potential value in constructing a minimum dataset with standardized format and definition, and regular updates of patient status. Such an approach could be particularly useful for diseases that spread across multiple countries.

title: Pulmonary embolism in COVID-19. When nothing is what it seems

title: Tracking the COVID-19 pandemic in Australia using genomics
abstract: Whole-genome sequencing of pathogens can improve resolution of outbreak clusters and define possible transmission networks. We applied high-throughput genome sequencing of SARS-CoV-2 to 75% of cases in the State of Victoria (population 6.24 million) in Australia.  Cases of SARS-CoV-2 infection were detected through active case finding and contact tracing. A dedicated SARS-CoV-2 multidisciplinary genomic response team was formed to enable rapid integration of epidemiological and genomic data. Phylodynamic analysis was performed to assess the putative impact of social restrictions.  Between 25 January and 14 April 2020, 1,333 COVID-19 cases were reported in Victoria, with a peak in late March. After applying internal quality control parameters, 903 samples were included in genomic analyses. Sequenced samples from Australia were representative of the global diversity of SARS-CoV-2, consistent with epidemiological findings of multiple importations and limited onward transmission. In total, 76 distinct genomic clusters were identified; these included large clusters associated with social venues, healthcare facilities and cruise ships. Sequencing of sequential samples from 98 patients revealed minimal intra-patient SARS-CoV-2 genomic diversity. Phylodynamic modelling indicated a significant reduction in the effective viral reproductive number (Re) from 1.63 to 0.48 after the implementation of travel restrictions and population-level physical distancing.  Our data provide a comprehensive framework for the use of SARS-CoV-2 genomics in public health responses. The application of genomics to rapidly identify SARS-CoV-2 transmission chains will become critically important as social restrictions ease globally. Public health responses to emergent cases must be swift, highly focused and effective.

title: Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation
abstract: Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, greatly reduces the doctors' workload, increasing reliability and quantitative analysis. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSC of 97.5% on the JSRT dataset.

title: Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning
abstract: The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. Recent outbreak of novel coronavirus infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of COVID-19 will save the life of thousands. In this paper, we devised a machine learning (ML) model to predict the possible inhibitory synthetic antibodies for Corona virus. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, we screened thousands of hypothetical antibody sequences and found 8 stable antibodies that potentially inhibit COVID-19. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit the Corona virus.

title: Partial RdRp sequences offer a robust method for Coronavirus subgenus classification
abstract: The recent reclassification of the Riboviria, and the introduction of multiple new taxonomic categories including both subfamilies and subgenera for coronaviruses (family Coronaviridae, subfamily Orthocoronavirinae) represents a major shift in how official classifications are used to designate specific viral lineages. While the newly defined subgenera provide much-needed standardisation for commonly cited viruses of public health importance, no method has been proposed for the assignment of subgenus based on partial sequence data, or for sequences that are divergent from the designated holotype reference genomes. Here, we describe the genetic variation of a partial region of the coronavirus RNA-dependent RNA polymerase (RdRp), which is one of the most used partial sequence loci for both detection and classification of coronaviruses in molecular epidemiology. We infer Bayesian phylogenies from more than 7000 publicly available coronavirus sequences and examine clade groupings relative to all subgenus holotype sequences. Our phylogenetic analyses are largely coherent with genome-scale analyses based on designated holotype members for each subgenus. Distance measures between sequences form discrete clusters between taxa, offering logical threshold boundaries that can attribute subgenus or indicate sequences that are likely to belong to unclassified subgenera both accurately and robustly. We thus propose that partial RdRp sequence data of coronaviruses is sufficient for the attribution of subgenus-level taxonomic classifications and we supply the R package, “MyCoV”, which provides a method for attributing subgenus and assessing the reliability of the attribution. The analysis of polymerase chain reaction amplicons derived from biological samples is the most common modern method for detection and classification of infecting viral agents, such as Coronaviruses. Recent updates to the official standard for taxonomic classification of Coronaviruses, however, may leave researchers unsure as to whether the viral sequences they obtain by these methods can be classified into specific viral taxa due to variations in the sequences when compared to type strains. Here, we present a plausible method for defining genetic dissimilarity cut-offs that will allow researchers to state which taxon their virus belongs to and with what level of certainty. To assist in this, we also provide the R package ‘MyCoV’ which classifies user generated sequences.

title: Mapping of Health Literacy and Social Panic Via Web Search Data During the COVID-19 Public Health Emergency: Infodemiological Study
abstract: Coronavirus disease (COVID-19) is a type of pneumonia caused by a novel coronavirus that was discovered in 2019. As of May 6, 2020, 84,407 cases and 4643 deaths have been confirmed in China. The Chinese population has expressed great concern since the COVID-19 outbreak. Meanwhile, an average of 1 billion people per day are using the Baidu search engine to find COVID-19–related health information.  The aim of this paper is to analyze web search data volumes related to COVID-19 in China.  We conducted an infodemiological study to analyze web search data volumes related to COVID-19. Using Baidu Index data, we assessed the search frequencies of specific search terms in Baidu to describe the impact of COVID-19 on public health, psychology, behaviors, lifestyles, and social policies (from February 11, 2020, to March 17, 2020).  The search frequency related to COVID-19 has increased significantly since February 11th. Our heat maps demonstrate that citizens in Wuhan, Hubei Province, express more concern about COVID-19 than citizens from other cities since the outbreak first occurred in Wuhan. Wuhan citizens frequently searched for content related to “medical help,” “protective materials,” and “pandemic progress.” Web searches for “return to work” and “go back to school” have increased eight-fold compared to the previous month. Searches for content related to “closed community and remote office” have continued to rise, and searches for “remote office demand” have risen by 663% from the previous quarter. Employees who have returned to work have mainly engaged in the following web searches: “return to work and prevention measures,” “return to work guarantee policy,” and “time to return to work.” Provinces with large, educated populations (eg, Henan, Hebei, and Shandong) have been focusing on “online education” whereas medium-sized cities have been paying more attention to “online medical care.”  Our findings suggest that web search data may reflect changes in health literacy, social panic, and prevention and control policies in response to COVID-19.

title: Ancestral origin, antigenic resemblance and epidemiological insights of novel coronavirus (SARS-CoV-2): Global burden and Bangladesh perspective
abstract: SARS-CoV-2, a new coronavirus strain responsible for COVID-19 has emerged in Wuhan City, China and still continuing its worldwide pandemic nature. Considering the severity of the disease, a number of studies are underway, and full genomic sequences have already been released in the last few weeks to enable the understanding of the evolutionary origin and molecular characteristics of this virus. Bioinformatics analysis, satellite derived imaging data and epidemiological attributes were employed to investigate origin, immunogenic resemblance and global threat of newly pandemic SARS-CoV-2 including Bangladesh perspective. Based on currently available genomic information, a phylogeny study was employed focusing four types of representative viral proteins (spike, membrane, envelope and nucleoprotein) of SARS-CoV-2, HCoV-229E, HCoV-OC43, SARS-CoV, HCoV-NL63, HKU1, MERS-CoV, HKU4, HKU5 and BufCoV-HKU26. The findings clearly demonstrated that SARS-CoV-2 exhibited evolutionary convergent relation with previously reported SARS-CoV. It was also found that SARS-CoV-2 proteins were highly similar and identical to SARS-CoV proteins, though proteins from other coronaviruses showed lower level of similarity and identical patterns. The cross-checked conservancy analysis of SARS-CoV-2 antigenic epitopes showed significant conservancy with antigenic epitopes derived from SARS-CoV. The study also prioritized the temperature comparison through satellite imaging alongside compiling and analyzing the epidemiological outbreak information on the 2019 novel coronavirus based on several open datasets on COVID-19 (SARS-CoV-2) and discussed possible threats to Bangladesh.

title: Developing a supervised learning-based social media business sentiment index
abstract: The fast-growing digital data generation leads to the emergence of the era of big data, which become particularly more valuable because approximately 70% of the collected data in the world comes from social media. Thus, the investigation of online social network services is of paramount importance. In this paper, we use the sentiment analysis, which detects attitudes and emotions toward issues of society posted in social media, to understand the actual economic situation. To this end, two steps are suggested. In the first step, after training the sentiment classifiers with several big data sources of social media datasets, we consider three types of feature sets: feature vector, sequence vector and a combination of dictionary-based feature and sequence vectors. Then, the performance of six classifiers is assessed: MaxEnt-L1, C4.5 decision tree, SVM-kernel, Ada-boost, Naïve Bayes and MaxEnt. In the second step, we collect datasets that are relevant to several economic words that the public use to explicitly express their opinions. Finally, we use a vector auto-regression analysis to confirm our hypothesis. The results show the statistically significant relationship between public sentiment and economic performance. That is, “depression” and “unemployment” lead to KOSPI. Also, it shows that the extracted keywords from the sentiment analysis, such as “price,” “year-end-tax” and “budget deficit,” cause the exchange rates.

title: The All of Us Research Program: data quality, utility, and diversity
abstract: The All of Us Research Program hypothesizes that accruing one million or more diverse participants engaged in a longitudinal research cohort will advance precision medicine and ultimately improve human health. Launched nationally in 2018, to date All of Us has recruited more than 345,000 participants. All of Us plans to open beta access to researchers in May 2020.  To demonstrate the quality, utility, and diversity of the All of Us Research Programs initial data release and beta launch of the cloud-based analysis platform, the cloud-based Researcher Workbench.  We analyzed the initial All of Us data release, comprising surveys, physical measurements (PM), and electronic health record (EHR) data, to characterize All of Us participants including self-reported descriptors of diversity. Data depth, density, and quality were evaluated using medication sequencing analyses for depression and type 2 diabetes. Replication of known oncologic associations with smoking exposure ascertained by EHR and survey data and calculation of population-based atherosclerotic cardiovascular disease risk scores demonstrated the utility of data and platform capability.  The beta launch of the All of Us Researcher Workbench contains data on 224,143 participants. Seventy-seven percent of this cohort were identified as Underrepresented in Biomedical Research (UBR) including over forty-eight percent self-reporting non-White race. Medication usage patterns in common diseases depression and type 2 diabetes replicated prior findings previously reported in the literature and showed differences based on race. Oncologic associations with smoking were replicated and effect sizes compared for EHR and survey exposures finding general agreement. A cardiovascular disease score was calculated utilizing multiple data elements curated across sources. The cloud-based architecture built in the Researcher Workbench provided secure access and powerful computational resources at a low cost. All analyses have been made available for replication and reuse by registered researchers.  The All of Us Research Programs initial release of cohort data contains longitudinal and multidimensional data on diverse participants that replicate known associations. This dataset and the cloud-based Researcher Workbench advance the mission of All of Us to make data widely and securely available to researchers to improve human health and advance precision medicine.

title: Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation
abstract: The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55 000 organisms (>4800 viruses, >40 000 prokaryotes and >10 000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.

title: Epidemiology and clinical features of coronavirus disease 2019 in children
abstract: Coronavirus disease-2019 (COVID-19), which started in Wuhan, China, in December 2019 and declared a worldwide pandemic on March 11, 2020, is a novel infectious disease that causes respiratory illness and death. Pediatric COVID-19 accounts for a small percentage of patients and is often milder than that in adults; however, it can progress to severe disease in some cases. Even neonates can suffer from COVID-19, and children may spread the disease in the community. This review summarizes what is currently known about COVID-19 in children and adolescents.

title: Global Reach of an Online COVID-19 Course in Multiple Languages on OpenWHO in the First Quarter of 2020: Analysis of Platform Use Data
abstract: At the onset of the coronavirus outbreak, the World Health Organization’s (WHO) Health Emergencies Learning and Capacity Development Unit, together with the WHO’s health technical lead on coronaviruses, developed a massive open online course within 3 weeks as part of the global response to the emergency. The introductory coronavirus disease (COVID‑19) course was launched on January 26, 2020, on the health emergencies learning platform OpenWHO.org.  The aim of this paper is to investigate the geographic reach of different language courses accessed by a worldwide audience seeking information on COVID-19. Users’ professional identities and backgrounds were explored to inform course owners on the use case. The course was developed and delivered via the open-access learning platform OpenWHO.org. The self-paced resources are available in a total of 13 languages and were produced between January 26 and March 25, 2020.  Data were collected from the online courses’ statistical data and metrics reporting system on the OpenWHO platform. User patterns and locations were analyzed based on Google Analytics and the platform’s own statistics capabilities, and data sets were overlaid. This analysis was conducted based on user location, with the data disaggregated according to the six WHO regions, the top 10 countries, and the proportion of use for each language version. Data included affiliation, gender, age, and other parameters for 32.43% (52,214/161,007) of the users who indicated their background.  As of March 25, 2020, the introductory COVID-19 course totaled 232,890 enrollments across all languages. The Spanish language course was comprised of more than half (n=118,754, 50.99%) of all course enrollments, and the English language course was comprised of 38.21% (n=88,988) of enrollments. The WHO’s Region of the Americas accounted for most of the course enrollments, with more than 72.47% (138,503/191,130) enrollment across all languages. Other regions were more evenly distributed with less than 10% enrollment for each. A total of 32.43% (52,214/161,007) of users specified a professional affiliation by choosing from the 12 most common backgrounds in the OpenWHO user profiles. Before the COVID-19 pandemic, users were spread over the 11 distinct affiliations, with a small fraction of users identifying themselves as “Other.” With the COVID-19 introductory course, the largest number of users selected “Other” (16,527/52,214, 31.65%), suggesting a large number of users who were not health professionals or academics. The top 10 countries with the most users across all languages were Argentina, Chile, Colombia, Ecuador, India, Mexico, Peru, Spain, the United Kingdom, and the United States.  The online course has addressed a worldwide learning need by providing WHO’s technical guidance packaged in simple formats for access and use. The learning material development was expedited to meet the onset of the epidemic. Initial data suggest that the various language versions of the course, in particular Spanish, have reached new user groups, fulfilling the platform’s aim of providing learning everywhere to anyone that is interested. User surveys will be carried out to measure the real impact.

title: The value of early transmission dynamic studies in emerging infectious diseases

title: Collecting Data To Assess SARS Interventions
abstract: With cases of severe acute respiratory syndrome (SARS) occurring across geographic regions, data collection on the effectiveness of intervention strategies should be standardized to facilitate analysis. We propose a minimum dataset to capture data needed to examine the basic reproduction rate, case status and criteria, symptoms, and outcomes of SARS.

title: The impact of early scientific literature in response to COVID-19: a scientometric perspective
abstract: In the early phases of a new pandemic, identifying the most relevant evidence and quantifying which studies are shared the most can help researchers and policy makers. The aim of this study is to describe and quantify the impact of early scientific production in response to COVID-19 pandemic.  The study consisted of: 1) review of the scientific literature produced in the first 30 days since the first COVID-19 paper was published; 2) analysis of papers' metrics with the construction of a Computed-Impact-Score (CIS) that represents a unifying score over heterogeneous bibliometric indicators. In this study we use metrics and alternative metrics collected into five separate categories. On top of those categories we compute the CIS. Highest CIS papers are further analyzed.  239 papers have been included in the study. The mean of citations, mentions and social media interactions resulted in 1.63, 10 and 1250, respectively. The paper with highest CIS resulted "Clinical features of patients[...]" by Chaolin Huang et al., which rated first also in citations and mentions. This is the first paper describing patients affected by the new disease and reporting data that are clearly of great interest to both the scientific community and the general population.  The early response of scientific literature during an epidemic does not follow a pre- established pattern. Being able to monitor how communications spread from the scientific world toward the general population using both traditional and alternative metric measures is essential, especially in the early stages of a pandemic.

title: Impact of weather on COVID-19 pandemic in Turkey
abstract: The coronavirus pandemic, which has numerous global implications, has led people to believe that nothing will be the same as before. The present day is dominated by studies on determining the factors that affect, taking preventive actions, and trying to find an effective treatment on top priority. Meteorological parameters are among the crucial factors affecting infectious diseases. The present study examines the correlation between weather and coronavirus disease 2019 (COVID-19) by considering nine cities in Turkey. In this regard, temperature (°C), dew point (°C), humidity (%), and wind speed (mph) are considered as parameters of weather. Research states that the incubation period of COVID-19 varies from 1 day to 14 days. Therefore, the effects of each parameter within 1, 3, 7, and 14 days are examined. In addition, the population is included as an effective parameter for evaluation. The analyses are conducted based on Spearman's correlation coefficients. The results showed that the highest correlations were observed for population, wind speed 14 days ago, and temperature on the day, respectively. The study results may guide authorities and decision-makers on taking specific measures for the cities.

title: Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020
abstract: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) in Wuhan, China since December 2019. Based on the publicly available surveillance data, we identified 21 transmission chains in Hong Kong and estimated the serial interval (SI) of COVID-19.  Index cases were identified and reported after symptoms onset, and contact tracing was conducted to collect the data of the associated secondary cases. An interval censored likelihood framework is adopted to fit a Gamma distribution function to govern the SI of COVID-19.  Assuming a Gamma distributed model, we estimated the mean of SI at 4.4 days (95%CI: 2.9−6.7) and SD of SI at 3.0 days (95%CI: 1.8−5.8) by using the information of all 21 transmission chains in Hong Kong.  The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are recommended in combating the COVID-19 outbreak.

title: Development and Evaluation of an AI System for COVID-19
abstract: Early detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease. With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts. Here, we propose an artificial intelligence (AI) system for fast COVID-19 diagnosis with an accuracy comparable to experienced radiologists. A large dataset was constructed by collecting 970 CT volumes of 496 patients with confirmed COVID-19 and 260 negative cases from three hospitals in Wuhan, China, and 1,125 negative cases from two publicly available chest CT datasets. Trained using only 312 cases, our diagnosis system, which is based on deep convolutional neural network, is able to achieve an accuracy of 94.98%, an area under the receiver operating characteristic curve (AUC) of 97.91%, a sensitivity of 94.06%, and a specificity of 95.47% on an independent external verification dataset of 1,255 cases. In a reader study involving five radiologists, only one radiologist is slightly more accurate than the AI system. The AI system is two orders of magnitude faster than radiologists and the code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.

title: Open access epidemiological data from the COVID-19 outbreak

title: Knowledge and Awareness-based Survey of COVID-19 within the Eye Care Profession in Nepal: Misinformation is Hiding the Truth.
abstract: Nepal is currently under lockdown due to the COVID-19 pandemic with misinformation circulating on social media. This study aimed to analyse the knowledge and awareness of COVID-19 amongst eye care professionals in Nepal.  We invited 600 participants from 12 ophthalmic centres across Nepal to complete a qualitative, anonymous online survey.  Of the 600 eye care professionals invited, 310 (51%) participated in the survey. The symptoms of COVID-19 were known to 94%, whilst only 49% were aware of disease transmission. 98% of participants recognised the World Health Organization (WHO) awareness message, yet 41% of participants felt that consumption of hot drinks helps to destroy the virus. 41% disagreed that PPE should be mandatory for eye care practitioners.  There is still considerable scope to improve the knowledge of COVID-19 amongst ophthalmic professionals in Nepal. Opinion is also split on measures to prevent transmission, with misinformation potentially fuelling confusion.

title: Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
abstract: The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep-learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.

title: A Visual Approach for the SARS (Severe Acute Respiratory Syndrome) Outbreak Data Analysis
abstract: Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades in the world. SARS-CoV (Severe Acute Respiratory Syndrome Associated Coronavirus) is a member of the coronavirus family, so its study is useful for relevant virus data research. In this work, we conduct a proposed approach that is non-medical/clinical, generate graphs from five features of the SARS outbreak data in five countries and regions, and offer insights from a visual analysis perspective. The results show that prevention measures such as quarantine are the most common control policies used, and areas with strict measures did have fewer peak period days; for instance, Hong Kong handled the outbreak better than other areas. Data conflict issues found with this approach are discussed as well. Visual analysis is also proved to be a useful technique to present the SARS outbreak data at this stage; furthermore, we are proceeding to apply a similar methodology with more features to future COVID-19 research from a visual analysis perfective.

title: Animal Viruses Probe Dataset (AVPDS) for Microarray-Based Diagnosis and Identification of Viruses
abstract: Probe dataset) is a dataset of virus-specific and conserve oligonucleotides for identification and diagnosis of viruses infecting animals. The current dataset contain 20,619 virus specific probes for 833 viruses and their subtypes and 3,988 conserved probes for 146 viral genera. Dataset of virus specific probe has been divided into two fields namely virus name and probe sequence. Similarly conserved probes for virus genera table have genus, and subgroup within genus name and probe sequence. The subgroup within genus is artificially divided subgroups with no taxonomic significance and contains probes which identifies viruses in that specific subgroup of the genus. Using this dataset we have successfully diagnosed the first case of Newcastle disease virus in sheep and reported a mixed infection of Bovine viral diarrhea and Bovine herpesvirus in cattle. These dataset also contains probes which cross reacts across species experimentally though computationally they meet specifications. These probes have been marked. We hope that this dataset will be useful in microarray-based detection of viruses. The dataset can be accessed through the link https://dl.dropboxusercontent.com/u/94060831/avpds/HOME.html.

title: Knowledge and practice of preventive measures against COVID-19 infection among pregnant women in a low-resource African setting
abstract: Coronavirus disease pandemic has resulted in death of thousands of people across several countries. Several preventive measures have been recommended to halt the spread of the disease and its associated mortality. However, the level knowledge and practice of these preventive measures against COVID-19 infection among pregnant women, which constitute vulnerable groups, are yet to be evaluated.  To determine the knowledge and practice of preventive measures against COVID-19 infection among pregnant women in Abakaliki.  This was a self-administered questionnaire-based cross-sectional study conducted from February 1, 2020 to March 31, 2020 among 284 antenatal clinic attendees at Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State. A pretested and validated questionnaire was used to collect the data. Data analysis was done using SPSS version 22.  Of 284 participants, 60.9% (n=173) had adequate knowledge of the preventive measures against COVID-19 infection. However, the overall practice of these preventive measures among the participants were poor as 69.7% of the participants were not practicing the preventive measures against the coronavirus. The determinants of poor practice of the preventive measures among the participants were being in age group 31-40 years (AOR=2.04, 95%CI: 1.26 - 5.37, p=0.022), married (AOR=2.99, 95%CI: 1.40 - 6.33, p=0.035) grandmultiparous (AOR=3.11, 95%CI: 1.32 - 6.56, p=0.021), residing in rural area (AOR=2.08, 95%CI: 1.32 - 4.05, p=0.031), and having no formal education (AOR=6.73, 95%CI: 2.66 - 18.34, p=0.002).  The study showed that most of the participants had adequate knowledge of preventive measures against COVID-19 infection but the practice of these preventive measures were poor among the participants.

title: The Coronavirus Pandemic: What Does the Evidence Show?
abstract: Coronavirus disease 2019 (COVID-19) is a newly emerged disease that has become a global public health concern as it rapidly spread around the world. The etiologic agent responsible for this disease has been named as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses as it shows similar genomic features to that of SARS-CoV which caused a pandemic in 2002. This disease first appeared in Hubei province of China and it follows human-to-human transmission but the path this virus took to set up human infection remains a mystery. By 17 April 2020, globally there have been 2,074,529 confirmed cases with 139,378 deaths because of COVID-19. SARS-CoV-2 shows several similarities with SARS?CoV, and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) with its clinical presentations. This can vary from asymptomatic infection to severe disease and mortality. Real-time reverse-transcription polymerase chain reaction (rRT-PCR) screening is considered as the standard laboratory test for the diagnosis of COVID-19. There is no proven antiviral agent against SARS-CoV-2 so the treatment for COVID-19 is symptomatic, aiming for the management of the symptoms and prevention of the complications. The outbreak of COVID-19 has led to the implementation of extraordinary public health measures throughout the world. Numerous antiviral compounds used to treat other infections are being clinically researched to find possible treatment. Similarly, the traditional public health outbreak response strategy of isolation, quarantine, social distancing and community containment has been implemented in multiple countries and has played an important role in the prevention of new outbreaks. This review aims to enhance our understanding of COVID 19.  Coronavirus disease 2019; COVID-19; SARS-CoV-2; novel coronavirus 2019; severe acute respiratory syndrome-2.

title: Pandemic Publishing: Medical journals drastically speed up their publication process for Covid-19
abstract: In times of public crises, including the current Covid-19 pandemic, rapid dissemination of relevant scientific knowledge is of paramount importance. The duration of scholarly journals’ publication process is one of the main factors hindering quick delivery of new information. While proper editorial assessment and peer review obviously require some time, turnaround times for medical journals can be up to several months, which is undesirable in the era of a crisis. Following initiatives of medical journals and scholarly publishers to accelerate their publication process, this study assesses whether medical journals have indeed managed to speed up their publication process for Covid-19 related articles. It studies the duration of 14 medical journals’ publication process both during and prior to the current pandemic. Assessing a total of 669 articles, the study concludes that medical journals have indeed drastically accelerated the publication process for Covid-19 related articles since the outbreak of the pandemic. Compared to articles published in the same journals before the pandemic, turnaround times have decreased on average by 49%. The largest decrease in number of days between submission and publication of articles was due to a decrease in the number of days required for peer review. For articles not related to Covid-19, no acceleration of the publication process is found. While the acceleration of journals’ publication process is laudable from the perspective of quick information dissemination, it also raises concerns relating to the quality of the peer review process and the quality of the resulting publications.

title: Dataset of Vietnamese student's learning habits during COVID-19
abstract: A dataset was constructed to examine Vietnamese student's learning habits during the time schools were suspended due to the novel coronavirus - SARS-CoV-2 (COVID-19), in response to a call for interdisciplinary research on the potential effects of the coronavirus pandemic (Elsevier, 2020). The questionnaires were spread over a network of educational communities on Facebook from February 7 to February 28, 2020. Using the snowball sampling method, researchers delivered the survey to teachers and parents to provide formal consent before they forwarded it to their students and children. In order to measure the influence of students’ socioeconomic status and occupational aspirations on their learning habits during school closures, the survey included three major groups of questions: (1) Individual demographics, including family socioeconomic status, school type, and occupational aspirations; (2) Student's learning habits, including hours of learning before and during the period of school suspension, with and without other people's support; and (3) Students’ perceptions of their self-learning during the school closures. There was a total of 920 clicks on the survey link, but only 460 responses accompanied by consent forms were received. Non-credible answers (e.g., year of birth after 2009, more than 20 hours of learning per day) were eliminated. The final dataset included 420 valid observations.

title: Fatalism in the context of COVID-19: Perceiving coronavirus as a death sentence predicts reluctance to perform recommended preventative behaviors()
abstract: To manage the spread of coronavirus, health entities have urged the public to take preventative measures such as social distancing and handwashing. Yet, many appear reluctant to take these measures. Research is needed to understand factors underlying such reluctance, with the aim of developing targeted health interventions. We identify associating coronavirus with death as one such factor. 590 participants completed surveys in mid-March 2020, which included attitudes toward coronavirus, preventative behavioral intentions, and sociodemographic factors. Associating coronavirus with death negatively predicted intentions to perform preventative behaviors. Further, associating coronavirus with death was not evenly distributed throughout the sample and was related with a number of sociodemographic factors including age, race, and availability of sick leave. Following recommended preventative measures to slow the spread of coronavirus appears to relate to the degree to which people associate coronavirus with death. These findings can be used by public health researchers and practitioners to identify those for whom targeted health communication and interventions would be most beneficial, as well as to frame health messaging in ways that combat fatalism.

title: Pangolins Harbor SARS-CoV-2-Related Coronaviruses
abstract: The pandemic of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has posed a severe threat to global public health. Yet, the origin of SARS-CoV-2 remains mysterious. Several recent studies (e.g., Lam et al ., Xiao et al .) identified SARS-CoV-2-related viruses in pangolins, providing novel insights into the evolution and diversity of SARS-CoV-2-related viruses.

title: A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models
abstract: We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. Specifically, our method is able to produce stable and accurate forecasts 2 days ahead of current time, and uses as inputs (a) official health reports from Chinese Center Disease for Control and Prevention (China CDC), (b) COVID-19-related internet search activity from Baidu, (c) news media activity reported by Media Cloud, and (d) daily forecasts of COVID-19 activity from GLEAM, an agent-based mechanistic model. Our machine-learning methodology uses a clustering technique that enables the exploitation of geo-spatial synchronicities of COVID-19 activity across Chinese provinces, and a data augmentation technique to deal with the small number of historical disease activity observations, characteristic of emerging outbreaks. Our model's predictive power outperforms a collection of baseline models in 27 out of the 32 Chinese provinces, and could be easily extended to other geographies currently affected by the COVID-19 outbreak to help decision makers.

title: Hybrid deep learning for detecting lung diseases from X-ray images
abstract: Lung disease is common throughout the world. These include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is essential. Many image processing and machine learning models have been developed for this purpose. Different forms of existing deep learning techniques including convolutional neural network (CNN), vanilla neural network, visual geometry group based neural network (VGG), and capsule network are applied for lung disease prediction. The basic CNN has poor performance for rotated, tilted, or other abnormal image orientation. Therefore, we propose a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN. This new hybrid method is termed here as VGG Data STN with CNN (VDSNet). As implementation tools, Jupyter Notebook, Tensorflow, and Keras are used. The new model is applied to NIH chest X-ray image dataset collected from Kaggle repository. Full and sample versions of the dataset are considered. For both full and sample datasets, VDSNet outperforms existing methods in terms of a number of metrics including precision, recall, F0.5 score and validation accuracy. For the case of full dataset, VDSNet exhibits a validation accuracy of 73%, while vanilla gray, vanilla RGB, hybrid CNN and VGG, and modified capsule network have accuracy values of 67.8%, 69%, 69.5%, 60.5% and 63.8%, respectively. When sample dataset rather than full dataset is used, VDSNet requires much lower training time at the expense of a slightly lower validation accuracy. Hence, the proposed VDSNet framework will simplify the detection of lung disease for experts as well as for doctors.

title: Measuring Emotions in the COVID-19 Real World Worry Dataset
abstract: The COVID-19 pandemic is having a dramatic impact on societies and economies around the world. With various measures of lockdowns and social distancing in place, it becomes important to understand emotional responses on a large scale. In this paper, we present the first ground truth dataset of emotional responses to COVID-19. We asked participants to indicate their emotions and express these in text. This resulted in the Real World Worry Dataset of 5,000 texts (2,500 short + 2,500 long texts). Our analyses suggest that emotional responses correlated with linguistic measures. Topic modeling further revealed that people in the UK worry about their family and the economic situation. Tweet-sized texts functioned as a call for solidarity, while longer texts shed light on worries and concerns. Using predictive modeling approaches, we were able to approximate the emotional responses of participants from text within 14% of their actual value. We encourage others to use the dataset and improve how we can use automated methods to learn about emotional responses and worries about an urgent problem.

title: Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: a systematic review
abstract: To undertake a review and critical appraisal of published/ reports that offer methods of determining the effects of hypertension, diabetes, stroke, cancer, kidney issues, and high-cholesterol on COVID-19 disease severity.  Google Scholar, PubMed, COVID-19 Open Research Dataset: a resource of over 128,000 scholarly articles, including over 59,000 articles with full text related to COVID-19, SARS-CoV-2, and coronaviruses.  A search was conducted by two authors independently on the freely available COVID-19 Open Research Dataset (CORD-19). We developed an automated search engine to screen a total of 59,000 articles in a few seconds. The search engine was built using a retrieval function that ranks a set of documents based on the query terms appearing in each document regardless of their proximity within the document. Filtering of the articles was then undertaken using keywords and questions, e.g. "Effects of diabetes on COVID/normal coronavirus/SARS-CoV-2/nCoV/COVID-19 disease severity, mortality?". The search terms were repeated for all the comorbidities considered in this paper. Additional articles were retrieved by searching via Google Scholar and PubMed.  A total of 54 articles were considered for a full review. It was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity. Other comorbidities, such as cancer, kidney disease, and stroke, must be further evaluated to determine a strong relationship to the virus. Reports associating cancer, kidney disease, and stroke with COVID-19 should be carefully interpreted, not only because of the size of the samples, but also because patients could be old, have a history of smoking, or have any other clinical condition suggesting that these factors might be associated with the poor COVID-19 outcomes rather than the comorbidity itself. Such reports could lead many oncologists and physicians to change their treatment strategies without solid evidence and recommendations. Further research regarding this relationship and its clinical management is warranted. Additionally, treatment options must be examined further to provide optimal treatment and ensure better outcomes for patients suffering from these comorbidities. It should be noted that, whether definitive measurements exist or not, the care of patients as well as the research involved should be largely prioritized to tackle this deadly pandemic.

title: The scientific literature on Coronaviruses, COVID-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review
abstract: The COVID-19 global pandemic has generated an abundance of research quickly following the outbreak. Within only a few months, more than a thousand studies on this topic have already appeared in the scientific literature. In this short review, we analyse the bibliometric aspects of these studies on a macro level, as well as those addressing Coronaviruses in general. Furthermore, through a scoping analysis of the literature on COVID-19, we identify the main safety-related dimensions that these studies have thus far addressed. Our findings show that across various research domains, and apart from the medical and clinical aspects such as the safety of vaccines and treatments, issues related to patient transport safety, occupational safety of healthcare professionals, biosafety of laboratories and facilities, social safety, food safety, and particularly mental/psychological health and domestic safety have thus far attracted most attention of the scientific community in relation to the COVID-19 pandemic. Our analysis also uncovers various potentially significant safety problems caused by this global health emergency which currently have attracted only limited scientific focus but may warrant more attention. These include matters such as cyber safety, economic safety, and supply-chain safety. These findings highlight why, from an academic research perspective, a holistic interdisciplinary approach and a collective scientific effort is required to help understand and mitigate the various safety impacts of this crisis whose implications reach far beyond the bio-medical risks. Such holistic safety-scientific understanding of the COVID-19 crisis can furthermore be instrumental to be better prepared for a future pandemic.

title: Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease
abstract: The rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted life all over the world. While some economies are actively recovering from this pestilence, others are experiencing fast and consistent disease spread, compelling governments to impose social distancing measures that have put a halt on routines, especially in densely-populated areas. Aiming at bringing more light on key economic and public health factors affecting the disease spread, this initial study utilizes a quantitative statistical analysis based on the most recent publicly-available COVID-19 datasets. The study had shown and explained multiple significant relationships between the COVID-19 data and other country-level statistics. We have also identified and statistically profiled four major country-level clusters with relation to different aspects of COVID-19 development and country-level economic and health indicators. Specifically, this study has identified potential COVID-19 under-reporting traits as well as various economic factors that impact COVID-19 Diagnosis, Reporting, and Treatment. Based on the country clusters, we have also described the four disease development scenarios, which are tightly knit to country-level economic and public health factors. Finally, we have highlighted the potential limitation of reporting and measuring COVID-19 and provided recommendations on further in-depth quantitative research.

title: COVID-19: Unique public health issues facing Black, Asian and minority ethnic communities
abstract: The 2019 coronavirus disease is a serious public health emergency, with serious adverse implications for populations, healthcare systems, and economies globally. Recently, concerns have been raised about possible association between ethnicity, incidence and outcomes of COVID-19 arisen from early government data. In this review, we will explore the possible association using both recent COVID-19 studies and studies of previous pandemics. We call for data on ethnicity to be routinely collected by governments, as part of international collaboration, alongside other patient demographics and further research to robustly determine magnitude of association. Moreover, governments must learn from previous pandemics and recommended strategies to mitigate risks on minority ethnicities due to socioeconomic disadvantages.

title: Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases
abstract: While the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, may be helpful for the diagnosis. In this study, the problem of automatic classification of pulmonary diseases, including the recently emerged COVID-19, from X-ray images, is considered.  Deep Learning has proven to be a remarkable method to extract massive high-dimensional features from medical images. Specifically, in this paper, the state-of-the-art Convolutional Neural Network called Mobile Net is employed and trained from scratch to investigate the importance of the extracted features for the classification task. A large-scale dataset of 3905 X-ray images, corresponding to 6 diseases, is utilized for training MobileNet v2, which has been proven to achieve excellent results in related tasks.  Training the CNNs from scratch outperforms the other transfer learning techniques, both in distinguishing the X-rays between the seven classes and between Covid-19 and non-Covid-19. A classification accuracy between the seven classes of 87.66% is achieved. Besides, this method achieves 99.18% accuracy, 97.36% Sensitivity, and 99.42% Specificity in the detection of COVID-19.  The results suggest that training CNNs from scratch may reveal vital biomarkers related but not limited to the COVID-19 disease, while the top classification accuracy suggests further examination of the X-ray imaging potential.

title: Knowledge of Latin American gastroenterologists and endoscopists regarding SARS-CoV-2 infection
abstract: After the World Health Organization declared the COVID-19 outbreak a pandemic, the number of patients with confirmed SARS-CoV-2 infection (COVID-19) has increased exponentially, and gastroenterologists and other specialists most likely will be involved in the care of those patients.  To evaluate the knowledge Latin American gastroenterologists and endoscopists (staff physicians and residents) have about the characteristics of COVID-19, as well as the prevention measures to be taken during endoscopic procedures.  We conducted a cross-sectional study that included gastroenterologists and endoscopists from 9 Latin American countries. An electronic questionnaire was applied that was designed to evaluate the knowledge of symptoms, risk groups for severe disease, prevention measures, and the reprocessing of endoscopes utilized in patients with COVID-19.  Information was obtained from 133 physicians. Ninety-five percent of them correctly identified the most frequent symptoms of the virus, and 60% identified the 3 risk groups for severe disease. Sixty-six percent of those surveyed did not consider it necessary to use standard precautions during endoscopic procedures, and 30% did not consider contact precautions necessary. Forty-eight percent of the participants surveyed were not familiar with the protocol for reprocessing the endoscopes utilized in patients with COVID-19.  The majority of the gastroenterologists and endoscopists surveyed were familiar with the signs and symptoms of COVID-19 and the populations at risk for complications. There was a lack of knowledge about prevention measures (during clinical care and endoscopic procedures) and the reprocessing of endoscopic equipment by 70% and 48%, respectively, of those surveyed. Dissemination and teaching strategies that increase the knowledge of specific biosafety measures must be carried out.

title: Early epidemiological assessment of the transmission potential and virulence of coronavirus disease 2019 (COVID-19) in Wuhan City: China, January-February, 2020
abstract: Since the first cluster of cases was identified in Wuhan City, China, in December, 2019, coronavirus disease 2019 (COVID-19) has rapidly spread across China, causing multiple introductions in 109 countries/territories/areas as of March 10th. Despite the scarcity of publicly available data, scientists around the world have made strides in estimating the magnitude of the epidemic, the basic reproduction number, and transmission patterns. Recently more evidence suggests that a substantial fraction of the infected individuals with the novel coronavirus show little if any symptoms, which suggest the need to reassess the transmission potential of this emerging disease. In this study, we derive estimates of the transmissibility and virulence of COVID-19 in Wuhan City, China, by reconstructing the underlying transmission dynamics using multiple data sources.  We employ statistical methods and publicly available epidemiological datasets to jointly derive estimates of transmissibility and severity associated with the novel coronavirus. For this purpose, the daily series of laboratory-confirmed COVID-19 cases and deaths in Wuhan City and epidemiological data of Japanese evacuees from Wuhan City on board government-chartered flights were integrated into our analysis.  Our posterior estimates of basic reproduction number (R) in Wuhan City, China in 2019-2020 reached values as high as 5.20 (95%CrI: 5.04-5.47) and the enhanced public health intervention after January 23rd in 2020 was associated with a declined R at 0.58 (95%CrI: 0.51-0.64), with the total number of infections (i.e. cumulative infections) estimated at 1905526 (95%CrI: 1350283-2655936) in Wuhan City, raising the proportion of infected individuals to 19.1% (95%CrI: 13.5-26.6%). We also found that most recent crude infection fatality ratio (IFR) and time-delay adjusted IFR is estimated to be 0.04% (95% CrI: 0.03-0.06%) and 0.12% (95%CrI: 0.08-0.17%), which is several orders of magnitude smaller than the crude CFR estimated at 4.19%  We have estimated key epidemiological parameters of the transmissibility and virulence of COVID-19 in Wuhan, China during January-February, 2020 using an ecological modelling approach. The power of our approach lies in the ability to infer epidemiological parameters with quantified uncertainty from partial observations collected by surveillance systems.

title: Analyzing the epidemiological outbreak of COVID-19: A visual exploratory data analysis approach
abstract: There is an obvious concern globally regarding the fact about the emerging coronavirus 2019 novel coronavirus (2019-nCoV) as a worldwide public health threat. As the outbreak of COVID-19 causes by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) progresses within China and beyond, rapidly available epidemiological data are needed to guide strategies for situational awareness and intervention. The recent outbreak of pneumonia in Wuhan, China, caused by the SARS-CoV-2 emphasizes the importance of analyzing the epidemiological data of this novel virus and predicting their risks of infecting people all around the globe. In this study, we present an effort to compile and analyze epidemiological outbreak information on COVID-19 based on the several open datasets on 2019-nCoV provided by the Johns Hopkins University, World Health Organization, Chinese Center for Disease Control and Prevention, National Health Commission, and DXY. An exploratory data analysis with visualizations has been made to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China. Overall, at the outset of an outbreak like this, it is highly important to readily provide information to begin the evaluation necessary to understand the risks and begin containment activities.

title: Dynamic models for Coronavirus Disease 2019 and data analysis
abstract: In this letter, two time delay dynamic models, a Time Delay Dynamical–Novel Coronavirus Pneumonia (TDD‐NCP) model and Fudan‐Chinese Center for Disease Control and Prevention (CCDC) model, are introduced to track the data of Coronavirus Disease 2019 (COVID‐19). The TDD‐NCP model was developed recently by Chengąŕs group in Fudan and Shanghai University of Finance and Economics (SUFE). The TDD‐NCP model introduced the time delay process into the differential equations to describe the latent period of the epidemic. The Fudan‐CDCC model was established when Wenbin Chen suggested to determine the kernel functions in the TDD‐NCP model by the public data from CDCC. By the public data of the cumulative confirmed cases in different regions in China and different countries, these models can clearly illustrate that the containment of the epidemic highly depends on early and effective isolations.

title: Exploring the coronavirus epidemic using the new WashU Virus Genome Browser
abstract: Since its debut in mid-December, 2019, the novel coronavirus (2019-nCoV) has rapidly spread from its origin in Wuhan, China, to several countries across the globe, leading to a global health crisis. As of February 7, 2020, 44 strains of the virus have been sequenced and uploaded to NCBI’s GenBank , providing insight into the virus’s evolutionary history and pathogenesis. Here, we present the WashU Virus Genome Browser, a web-based portal for viewing virus genomic data. The browser is home to 16 complete 2019-nCoV genome sequences, together with hundreds of related viral sequences including severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Ebola virus. In addition, the browser features unique customizability, supporting user-provided upload of novel viral sequences in various formats. Sequences can be viewed in both a track-based representation as well as a phylogenetic tree-based view, allowing the user to easily compare sequence features across multiple strains. The WashU Virus Genome Browser inherited many features and track types from the WashU Epigenome Browser, and additionally incorporated a new type of SNV track to address the specific needs of viral research. Our Virus Browser portal can be accessed at https://virusgateway.wustl.edu, and documentation is available at https://virusgateway.readthedocs.io/.

title: Scientometric Trends for Coronaviruses and Other Emerging Viral Infections
abstract: COVID-19 is the most rapidly expanding coronavirus outbreak in the past two decades. To provide a swift response to a novel outbreak, prior knowledge from similar outbreaks is essential. Here, we study the volume of research conducted on previous coronavirus outbreaks, specifically SARS and MERS, relative to other infectious diseases by analyzing over 35 million papers from the last 20 years. Our results demonstrate that previous coronavirus outbreaks have been understudied compared to other viruses. We also show that the research volume of emerging infectious diseases is very high after an outbreak and drops drastically upon the containment of the disease. This can yield inadequate research and limited investment in gaining a full understanding of novel coronavirus management and prevention. Independent of the outcome of the current COVID-19 outbreak, we believe that measures should be taken to encourage sustained research in the field.

title: Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis
abstract: The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting 201 countries and territories around the globe. As of April 4, 2020, it has caused a pandemic outbreak with more than 11,16,643 confirmed infections and more than 59,170 reported deaths worldwide. The main focus of this paper is two-fold: (a) generating short term (real-time) forecasts of the future COVID-19 cases for multiple countries; (b) risk assessment (in terms of case fatality rate) of the novel COVID-19 for some profoundly affected countries by finding various important demographic characteristics of the countries along with some disease characteristics. To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. The predictions of the future outbreak for different countries will be useful for the effective allocation of health care resources and will act as an early-warning system for government policymakers. In the second problem, we applied an optimal regression tree algorithm to find essential causal variables that significantly affect the case fatality rates for different countries. This data-driven analysis will necessarily provide deep insights into the study of early risk assessments for 50 immensely affected countries.

title: Droplet-Transmitted Infection Risk Ranking Based on Close Proximity Interaction
abstract: We propose an automatic method to identify people who are potentially-infected by droplet-transmitted diseases. This high-risk group of infection was previously identified by conducting large-scale visits/interviews, or manually screening among tons of recorded surveillance videos. Both are time-intensive and most likely to delay the control of communicable diseases like influenza. In this paper, we address this challenge by solving a multi-tasking problem from the captured surveillance videos. This multi-tasking framework aims to model the principle of Close Proximity Interaction and thus infer the infection risk of individuals. The complete workflow includes three essential sub-tasks: (1) person re-identification (REID), to identify the diagnosed patient and infected individuals across different cameras, (2) depth estimation, to provide a spatial knowledge of the captured environment, (3) pose estimation, to evaluate the distance between the diagnosed and potentially-infected subjects. Our method significantly reduces the time and labor costs. We demonstrate the advantages of high accuracy and efficiency of our method. Our method is expected to be effective in accelerating the process of identifying the potentially infected group and ultimately contribute to the well-being of public health.

title: Cigarette smoke exposure and inflammatory signaling increase the expression of the SARS-CoV-2 receptor ACE2 in the respiratory tract
abstract: The factors mediating fatal SARS-CoV-2 infections are poorly understood. Here, we show that cigarette smoke causes a dose-dependent upregulation of Angiotensin Converting Enzyme 2 (ACE2), the SARS-CoV-2 receptor, in rodent and human lungs. Using single-cell sequencing data, we demonstrate that ACE2 is expressed in a subset of secretory cells in the respiratory tract. Chronic smoke exposure triggers the expansion of this cell population and a concomitant increase in ACE2 expression. In contrast, quitting smoking decreases the abundance of these secretory cells and reduces ACE2 levels. Finally, we demonstrate that ACE2 expression is responsive to inflammatory signaling and can be upregulated by viral infections or interferon treatment. Taken together, these results may partially explain why smokers are particularly susceptible to severe SARS-CoV-2 infections. Furthermore, our work identifies ACE2 as an interferon-stimulated gene in lung cells, suggesting that SARS-CoV-2 infections could create positive-feedback loops that increase ACE2 levels and facilitate viral dissemination.

title: First known person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the USA
abstract: Coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first detected in China in December, 2019. In January, 2020, state, local, and federal public health agencies investigated the first case of COVID-19 in Illinois, USA.  Patients with confirmed COVID-19 were defined as those with a positive SARS-CoV-2 test. Contacts were people with exposure to a patient with COVID-19 on or after the patient's symptom onset date. Contacts underwent active symptom monitoring for 14 days following their last exposure. Contacts who developed fever, cough, or shortness of breath became persons under investigation and were tested for SARS-CoV-2. A convenience sample of 32 asymptomatic health-care personnel contacts were also tested.  Patient 1—a woman in her 60s—returned from China in mid-January, 2020. One week later, she was hospitalised with pneumonia and tested positive for SARS-CoV-2. Her husband (Patient 2) did not travel but had frequent close contact with his wife. He was admitted 8 days later and tested positive for SARS-CoV-2. Overall, 372 contacts of both cases were identified; 347 underwent active symptom monitoring, including 152 community contacts and 195 health-care personnel. Of monitored contacts, 43 became persons under investigation, in addition to Patient 2. These 43 persons under investigation and all 32 asymptomatic health-care personnel tested negative for SARS-CoV-2.  Person-to-person transmission of SARS-CoV-2 occurred between two people with prolonged, unprotected exposure while Patient 1 was symptomatic. Despite active symptom monitoring and testing of symptomatic and some asymptomatic contacts, no further transmission was detected.

title: The response of Milan's Emergency Medical System to the COVID-19 outbreak in Italy

title: Disinformation and Misinformation on Twitter during the Novel Coronavirus Outbreak
abstract: As the novel coronavirus spread globally, a growing public panic was expressed over the internet. We examine the public discussion concerning COVID-19 on Twitter. We use a dataset of 67 million tweets from 12 million users collected between January 29, 2020 and March 4, 2020. We categorize users based on their home countries, social identities, and political orientation. We find that news media, government officials, and individual news reporters posted a majority of influential tweets, while the most influential ones are still written by regular users. Tweets mentioning"fake news"URLs and disinformation story-lines are also more likely to be spread by regular users. Unlike real news and normal tweets, tweets containing URLs pointing to"fake news"sites are most likely to be retweeted within the source country and so are less likely to spread internationally.

title: Constructing Co-occurrence Network Embeddings to Assist Association Extraction for COVID-19 and Other Coronavirus Infectious Diseases
abstract: As COVID-19 started its rapid emergence and gradually transformed into an unprecedented pandemic, the need for having a knowledge repository for the disease became crucial. To address this issue, a new COVID-19 machine readable dataset known as COVID-19 Open Research Dataset (CORD-19) has been released. Based on this, our objective was to build a computable co-occurrence network embeddings to assist association detection amongst COVID-19 related biomedical entities.  Leveraging a Linked Data version of CORD-19 (i.e., CORD-19-on-FHIR), we first utilized SPARQL to extract co-occurrences among chemicals, diseases, genes, and mutations and build a co-occurrence network. We then trained the representation of the derived co-occurrence network using node2vec with four edge embeddings operations (L1, L2, Average, and Hadamard). Six algorithms (Decision Tree, Linear Regression, Support Vector Machine, Random Forest, Naive Bayes, and Multi-layer Perceptron) were applied to evaluate performance on link prediction. An unsupervised learning strategy was also developed incorporating the t-SNE and DBSCAN algorithms for case studies.  Random Forest classifier showed the best performance on link prediction across different network embeddings. For edge embeddings generated using the Average operation, Random Forest achieved the optimal average precision of 0.97 and F1 score of 0.90. For unsupervised learning, 63 clusters were formed with silhouette score of 0.128. Significant associations were detected for five coronavirus infectious diseases in their corresponding subgroups.  In this study, we constructed COVID-19-centered co-occurrence network embeddings. Results indicated that the generated embeddings were able to extract significant associations for COVID-19 and coronavirus infectious diseases.

title: ACE2 variants underlie interindividual variability and susceptibility to COVID-19 in Italian population
abstract: In December 2019, an initial cluster of unexpected interstitial bilateral pneumonia emerged in Wuhan, Hubei province. A human-to-human transmission was immediately assumed and a previously unrecognized entity, termed coronavirus disease 19 (COVID-19) due to a novel coronavirus (2019-nCov) was suddenly described. The infection has rapidly spread out all over the world and Italy has been the first European Country experiencing the endemic wave with unexpected clinical severity in comparison with Asian countries. It has recently been shown that 2019-nCov utilizes host receptors namely angiotensin converting enzyme 2 (ACE2) as host receptor and host proteases for cell surface binding and internalization. Thus, a predisposing genetic background can give reason for interindividual disease susceptibility and/or severity. Taking advantage of the Network of Italian Genomes (NIG), here we mined around 7000 exomes from 5 different Centers looking for ACE2 variants. A number of variants with a potential impact on protein stability were identified. Among these, three missense changes, p.Asn720Asp, p.Lys26Arg, p.Gly211Arg (MAF 0.002 to 0.015), which have never been reported in the Eastern Asia population, were predicted to interfere with protein cleavage and stabilization. Rare truncating variants likely interfering with the internalization process and one missense variant, p.Trp69Cys, predicted to interfere with 2019-nCov spike protein binding were also observed. These findings suggest that a predisposing genetic background may contribute to the observed inter-individual clinical variability associated with COVID-19. They allow an evidence-based risk assessment opening up the way to personalized preventive measures and therapeutic options.

title: ACE2 receptor polymorphism: susceptibility to SARS-CoV-2, hypertension, multi-organ failure, and COVID-19 disease outcome
abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged in Chinese people in December 2019 and has currently spread worldwide causing the COVID-19 pandemic with more than 150,000 deaths. In order for a SARS-CoV like virus circulating in wild life for a very long time to infect the index case-patient, a number of conditions must be met, foremost among which is the encounter with humans and the presence in homo sapiens of a cellular receptor allowing the virus to bind. Recently it was shown that the SARS-CoV-2 spike protein, binds to the human angiotensin I converting enzyme 2 (ACE2). This molecule is a peptidase expressed at the surface of lung epithelial cells and other tissues, that regulates the renin-angiotensin-aldosterone system. Humans are not equal with respect to the expression levels of the cellular ACE2. Moreover, ACE2 polymorphisms were recently described in human populations. Here we review the most recent evidence that ACE2 expression and/or polymorphism could influence both the susceptibility of people to SARS-CoV-2 infection and the outcome of the COVID-19 disease. Further exploration of the relationship between the virus, the peptidase function of ACE2 and the levels of angiotensin II in SARS-CoV-2 infected patients should help to better understand the pathophysiology of the disease and the multi-organ failures observed in severe COVID-19 cases, particularly heart failure.

title: Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science
abstract: The coronavirus disease 2019 (COVID-19) pandemic is having a profound effect on all aspects of society, including mental health and physical health. We explore the psychological, social, and neuroscientific effects of COVID-19 and set out the immediate priorities and longer-term strategies for mental health science research. These priorities were informed by surveys of the public and an expert panel convened by the UK Academy of Medical Sciences and the mental health research charity, MQ: Transforming Mental Health, in the first weeks of the pandemic in the UK in March, 2020. We urge UK research funding agencies to work with researchers, people with lived experience, and others to establish a high level coordination group to ensure that these research priorities are addressed, and to allow new ones to be identified over time. The need to maintain high-quality research standards is imperative. International collaboration and a global perspective will be beneficial. An immediate priority is collecting high-quality data on the mental health effects of the COVID-19 pandemic across the whole population and vulnerable groups, and on brain function, cognition, and mental health of patients with COVID-19. There is an urgent need for research to address how mental health consequences for vulnerable groups can be mitigated under pandemic conditions, and on the impact of repeated media consumption and health messaging around COVID-19. Discovery, evaluation, and refinement of mechanistically driven interventions to address the psychological, social, and neuroscientific aspects of the pandemic are required. Rising to this challenge will require integration across disciplines and sectors, and should be done together with people with lived experience. New funding will be required to meet these priorities, and it can be efficiently leveraged by the UK's world-leading infrastructure. This Position Paper provides a strategy that may be both adapted for, and integrated with, research efforts in other countries.

title: Developing an automated mechanism to identify medical articles from Wikipedia for knowledge extraction
abstract: Wikipedia contains rich biomedical information that can support medical informatics studies and applications. Identifying the subset of medical articles of Wikipedia has many benefits, such as facilitating medical knowledge extraction, serving as a corpus for language modeling, or simply making the size of data easy to work with. However, due to the extremely low prevalence of medical articles in the entire Wikipedia, articles identified by generic text classifiers would be bloated by irrelevant pages. To control the false discovery rate while maintaining a high recall, we developed a mechanism that leverages the rich page elements and the connected nature of Wikipedia and uses a crawling classification strategy to achieve accurate classification. Structured assertional knowledge in Infoboxes and Wikidata items associated with the identified medical articles were also extracted. This automatic mechanism is aimed to run periodically to update the results and share them with the informatics community.

title: All for one and one for all: Why a pandemic preparedness league of nations?
abstract: In light of today's COVID-19 crisis, it is argued that new ways to collaborate among all nations for pandemic preparedness may be the next big thing. A workable solution for pandemic preparedness requires leaders of all nations to be on the same page (all for one), aiming at a swift turnaround of the crisis in tandem that can only benefit everyone on a global scale (one for all). After all, a public health crisis of this magnitude involves all humankind, demanding not only the most sensible and intelligent adoption of progressive policies and innovative technology, but an effective balancing of emergency supply chain management (SCM) reserve of personal protective equipment (PPE), professional workers and/or other urgently needed resources (e.g., ICU beds) to save lives. Above all, accurate sharing of information and massive-scale testings vis-à-vis targeted isolations must be sustained. Notably, such a framework may not just be limited to infuenza. Here, the authors elaborate on several key strategies and aim to provide grounds for scientific innovations and clearer policy thinking across international boundaries to combat a global public health pandemic via a league of nations conceived as IPPO: Intercontinental Pandemic Preparedness Organization.

title: Breadth of concomitant immune responses underpinning viral clearance and patient recovery in a non-severe case of COVID-19
abstract: We report the kinetics of the immune response in relation to clinical and virological features of a patient with mild-to-moderate coronavirus disease-19 (COVID-19) requiring hospitalisation. Increased antibody-secreting cells, follicular T-helper cells, activated CD4+ and CD8+ T-cells and IgM/IgG SARS-CoV-2-binding antibodies were detected in blood, prior to symptomatic recovery. These immunological changes persisted for at least 7 days following full resolution of symptoms, indicating substantial anti-viral immunity in this non-severe COVID-19.

title: Molecular analysis of several in-house rRT-PCR protocols for SARS-CoV-2 detection in the context of genetic variability of the virus in Colombia
abstract: The COVID-19 pandemic caused by SARS-CoV-2 is a public health problem unprecedented in the recent history of humanity. Different in-house real-time RT-PCR (rRT-PCR) methods for SARS-CoV-2 diagnosis and the appearance of genomes with mutations in primer regions have been reported. Hence, whole-genome data from locally-circulating SARS-CoV-2 strains contribute to the knowledge of its global variability and the development and fine tuning of diagnostic protocols. To describe the genetic variability of Colombian SARS-CoV-2 genomes in hybridization regions of oligonucleotides of the main in-house methods for SARS-CoV-2 detection, RNA samples with confirmed SARS-CoV-2 molecular diagnosis were processed through next-generation sequencing. Primers/probes sequences from 13 target regions for SARS-CoV-2 detection suggested by 7 institutions and consolidated by WHO during the early stage of the pandemic were aligned with Muscle tool to assess the genetic variability potentially affecting their performance. Finally, the corresponding codon positions at the 3′ end of each primer, the open reading frame inspection was identified for each gene/protein product. Complete SARS-CoV-2 genomes were obtained from 30 COVID-19 cases, representative of the current epidemiology in the country. Mismatches between at least one Colombian sequence and five oligonucleotides targeting the RdRP and N genes were observed. The 3′ end of 4 primers aligned to the third codon position, showed high risk of nucleotide substitution and potential mismatches at this critical position. Genetic variability was detected in Colombian SARS-CoV-2 sequences in some of the primer/probe regions for in-house rRT-PCR diagnostic tests available at WHO COVID-19 technical guidelines; its impact on the performance and rates of false-negative results should be experimentally evaluated. The genomic surveillance of SARS-CoV-2 is highly recommended for the early identification of mutations in critical regions and to issue recommendations on specific diagnostic tests to ensure the coverage of locally-circulating genetic variants.

title: Preprinting a pandemic: the role of preprints in the COVID-19 pandemic
abstract: The world continues to face an ongoing viral pandemic that presents a serious threat to human health. The virus underlying the COVID-19 disease, SARS-CoV-2, has caused over 3.2 million confirmed cases and 220,000 deaths between January and April 2020. Although the last pandemic of respiratory disease of viral origin swept the globe only a decade ago, the way science operates and responds to current events has experienced a paradigm shift in the interim. The scientific community has responded rapidly to the COVID-19 pandemic, releasing over 16,000 COVID-19 related scientific articles within 4 months of the first confirmed case, of which at least 6,000 were hosted by preprint servers. We focused our analysis on bioRxiv and medRxiv, two growing preprint servers for biomedical research, investigating the attributes of COVID-19 preprints, their access and usage rates, characteristics of their sharing on online platforms, and the relationship between preprints and their published articles. Our data provides evidence for increased scientific and public engagement (COVID-19 preprints are accessed and distributed at least 15 times more than non-COVID-19 preprints) and changes in journalistic practice with reference to preprints. We also find evidence for changes in preprinting and publishing behaviour: COVID-19 preprints are shorter, with fewer panels and tables, and reviewed faster. Our results highlight the unprecedented role of preprints and preprint servers in the dissemination of COVID-19 science, and the likely long-term impact of the pandemic on the scientific publishing landscape.

title: SARS-CoV-2 Isolation and Propagation from Turkish COVID-19 patients
abstract: The novel coronavirus pneumonia, which was named later as Coronavirus Disease 2019 (COVID-19), is caused by the Severe Acute Respiratory Syndrome Coronavirus 2, namely SARS-CoV-2. It is a positive-strand RNA virus that is the seventh coronavirus known to infect humans. The COVID-19 outbreak presents enormous challenges for global health behind the pandemic outbreak. The first diagnosed patient in Turkey has been reported by the Republic of Turkey Ministry of Health on March 11, 2020. Today, over ninety thousand cases in Turkey, and two million cases around the world have been declared. Due to the urgent need for vaccine and anti-viral drug, isolation of the virus is crucial. Here, we report one of the first isolation and characterization studies of SARS-CoV-2 from nasopharyngeal and oropharyngeal specimens of diagnosed patients in Turkey. This study provides an isolation and replication methodology, and cell culture tropism of the virus that will be available to the research communities.  Scientists have isolated virus from Turkish COVID-19 patients. The isolation, propagation, and plaque and immune response assays of the virus described here will serve in following drug discovery and vaccine testing.

title: Localization to the nucleolus is a common feature of coronavirus nucleoproteins, and the protein may disrupt host cell division.
abstract: The subcellular localization of transmissible gastroenteritis virus (TGEV) and mouse hepatitis virus (MHV) (group I and group II coronaviruses, respectively) nucleoproteins (N proteins) were examined by confocal microscopy. The proteins were shown to localize either to the cytoplasm alone or to the cytoplasm and a structure in the nucleus. This feature was confirmed to be the nucleolus by using specific antibodies to nucleolin, a major component of the nucleolus, and by confocal microscopy to image sections through a cell expressing N protein. These findings are consistent with our previous report for infectious bronchitis virus (group III coronavirus) (J. A. Hiscox et al.,  2001), indicating that nucleolar localization of the N protein is a common feature of the coronavirus family and is possibly of functional significance. Nucleolar localization signals were identified in the domain III region of the N protein from all three coronavirus groups, and this suggested that transport of N protein to the nucleus might be an active process. In addition, our results suggest that the N protein might function to disrupt cell division. Thus, we observed that approximately 30% of cells transfected with the N protein appeared to be undergoing cell division. The most likely explanation for this is that the N protein induced a cell cycle delay or arrest, most likely in the G(2)/M phase. In a fraction of transfected cells expressing coronavirus N proteins, we observed multinucleate cells and dividing cells with nucleoli (which are only present during interphase). These findings are consistent with the possible inhibition of cytokinesis in these cells.

title: iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking
abstract: Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called “iGPCR-drug”, was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug – target interaction networks.

title: COVID-19 pneumonia manifestations at the admission on chest ultrasound, radiographs, and CT: single-center study and comprehensive radiologic literature review
abstract: To investigate the imaging features of emerging COVID-19 pneumonia on chest ultrasound (US), radiographs (CXR) and computed tomography (CT) examinations performed at admission and to provide a comprehensive radiological literature review on ongoing radiological data from recent publications.  In this retrospective single-center study, we enrolled consecutive patients from February 15, 2020, to March 15, 2020, with laboratory-confirmed SARS-CoV-2 hospitalized in Valduce Hospital (Como, Italy). Multi-modality imaging findings were evaluated and compared. Literature research was conducted through a methodical search on Pubmed and Embase databases.  Fifty-eight patients (36 men, 22 women; age range, 18–98 years) were included in the study. Among these, chest US, CXR, and CT were performed respectively in twenty-two, thirty-two and forty-two patients. Lung US findings were consistent with diffuse B lines (100%) and subpleural consolidations (27.3%). CXR showed prevalent manifestations of consolidations (46.9%) and hazy increased opacities (37.5%). Typical CT features included bilateral and multilobar ground-glass opacities (GGO) with (59.5%) and without (35.7%) consolidations having a predominantly peripheral distribution (64.3%). Other imaging features included crazy paving pattern (57.1%), fibrous stripes (50%), subpleural lines (35.7%), architectural distortion (28.6%), air bronchogram sign (26.2%), vascular thickening (23.8%) and nodules (2.4%). Also, enlarged lymph nodes (14.3 %) and pleural effusion (7.1%) were observed. The literature review identified twenty-six original studies supporting our imaging chest findings.  The spectrum of chest imaging manifestations of COVID-19 pneumonia upon admission includes B-lines and consolidations on US, consolidations and hazy increased opacities on CXR, and multifocal GGO with consolidations on CT.

title: Make Data Sharing Routine to Prepare for Public Health Emergencies
abstract: Jean-Paul Chretien and colleagues argue that recent Ebola and Zika virus outbreaks highlight the importance of data sharing in scientific research.

title: Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis
abstract: In the beginning of 2020, an unexpected outbreak due to a new corona virus made the headlines all over the world. Exponential growth in the number of those affected makes this virus such a threat. The current meta-analysis aimed to estimate the prevalence of underlying disorders in hospitalized COVID-19 patients.  A comprehensive systematic search was performed on PubMed, Scopus, Web of science, and Google scholar, to find articles published until 15 February 2020. All relevant articles that reported clinical characteristics and epidemiological information of hospitalized COVID-19 patients were included in the analysis.  The data of 76993 patients presented in 10 articles were included in this study. According to the meta-analysis, the pooled prevalence of hypertension, cardiovascular disease, smoking history and diabetes in people infected with SARS-CoV-2 were estimated as 16.37% (95%CI: 10.15%-23.65%), 12.11% (95%CI 4.40%-22.75%), 7.63% (95%CI 3.83%-12.43%) and 7.87% (95%CI 6.57%-9.28%), respectively.  According to the findings of the present study, hypertension, cardiovascular diseases, diabetes mellitus, smoking, chronic obstructive pulmonary disease (COPD), malignancy, and chronic kidney disease were among the most prevalent underlying diseases among hospitalized COVID-19 patients, respectively.

title: Internationally lost COVID-19 cases
abstract: With its epicenter in Wuhan, China, the COVID-19 outbreak was declared a pandemic by the World Health Organization (WHO). While many countries have implemented flight restrictions to China, an increasing number of cases with or without travel background to China are confirmed daily. These developments support concerns on possible unidentified and unreported international COVID-19 cases, which could lead to new local disease epicenters.  We have analyzed all available data on the development of international COVID-19 cases from January 20th, 2020 until February 18th, 2020. COVID-19 cases with and without travel history to China were divided into cohorts according to the Healthcare Access and Quality Index (HAQ-Index) of each country. Chi-square and Post-hoc testing were performed.  While COVID-19 cases with travel history to China seem to peak for each HAQ-cohort, the number of non-travel related COVID-19 cases seem to continuously increase in the HAQ-cohort of countries with higher medical standards. Further analyses demonstrate a significantly lower proportion of reported COVID-19 cases without travel history to China in countries with lower HAQ (HAQ I vs. HAQ II, posthoc p < 0.01).  Our data indicate that countries with lower HAQ-index may either underreport COVID-19 cases or are unable to adequately detect them. Although our data may be incomplete and must be interpreted with caution, inconsistencies in reporting COVID-19 cases is a serious problem which might sabotage efforts to contain the virus.

title: Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
abstract: Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. There have been increasing efforts on developing deep learning methods to diagnose COVID-19 based on CT scans. However, these works are difficult to reproduce and adopt since the CT data used in their studies are not publicly available. Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain. In this paper, we aim to address these two problems. We build a publicly-available dataset containing hundreds of CT scans positive for COVID-19 and develop sample-efficient deep learning methods that can achieve high diagnosis accuracy of COVID-19 from CT scans even when the number of training CT images are limited. Specifically, we propose an Self-Trans approach, which synergistically integrates contrastive self-supervised learning with transfer learning to learn powerful and unbiased feature representations for reducing the risk of overfitting. Extensive experiments demonstrate the superior performance of our proposed Self-Trans approach compared with several state-of-the-art baselines. Our approach achieves an F1 of 0.85 and an AUC of 0.94 in diagnosing COVID-19 from CT scans, even though the number of training CTs is just a few hundred.

title: [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China].
abstract: An outbreak of 2019 novel coronavirus diseases (COVID-19) in Wuhan, China has spread quickly nationwide. Here, we report results of a descriptive, exploratory analysis of all cases diagnosed as of February 11, 2020.  All COVID-19 cases reported through February 11, 2020 were extracted from China's Infectious Disease Information System. Analyses included: 1) summary of patient characteristics; 2) examination of age distributions and sex ratios; 3) calculation of case fatality and mortality rates; 4) geo-temporal analysis of viral spread; 5) epidemiological curve construction; and 6) subgroup analysis.  A total of 72 314 patient records-44 672 (61.8%) confirmed cases, 16 186 (22.4%) suspected cases, 10567 (14.6%) clinical diagnosed cases (Hubei only), and 889 asymptomatic cases (1.2%)-contributed data for the analysis. Among confirmed cases, most were aged 30-79 years (86.6%), diagnosed in Hubei (74.7%), and considered mild (80.9%). A total of 1 023 deaths occurred among confirmed cases for an overall case-fatality rate of 2.3%. The COVID-19 spread outward from Hubei sometime after December 2019 and by February 11, 2020, 1 386 counties across all 31 provinces were affected. The epidemic curve of onset of symptoms peaked in January 23-26, then began to decline leading up to February 11. A total of 1 716 health workers have become infected and 5 have died (0.3%).  The COVID-19 epidemic has spread very quickly. It only took 30 days to expand from Hubei to the rest of Mainland China. With many people returning from a long holiday, China needs to prepare for the possible rebound of the epidemic.

title: Update: Public Health Response to the Coronavirus Disease 2019 Outbreak - United States, February 24, 2020.
abstract: An outbreak of coronavirus disease 2019 (COVID-19) caused by the 2019 novel coronavirus (SARS-CoV-2) began in Wuhan, Hubei Province, China in December 2019, and has spread throughout China and to 31 other countries and territories, including the United States (1). As of February 23, 2020, there were 76,936 reported cases in mainland China and 1,875 cases in locations outside mainland China (1). There have been 2,462 associated deaths worldwide; no deaths have been reported in the United States. Fourteen cases have been diagnosed in the United States, and an additional 39 cases have occurred among repatriated persons from high-risk settings, for a current total of 53 cases within the United States. This report summarizes the aggressive measures (2,3) that CDC, state and local health departments, multiple other federal agencies, and other partners are implementing to slow and try to contain transmission of COVID-19 in the United States. These measures require the identification of cases and contacts of persons with COVID-19 in the United States and the recommended assessment, monitoring, and care of travelers arriving from areas with substantial COVID-19 transmission. Although these measures might not prevent widespread transmission of the virus in the United States, they are being implemented to 1) slow the spread of illness; 2) provide time to better prepare state and local health departments, health care systems, businesses, educational organizations, and the general public in the event that widespread transmission occurs; and 3) better characterize COVID-19 to guide public health recommendations and the development and deployment of medical countermeasures, including diagnostics, therapeutics, and vaccines. U.S. public health authorities are monitoring the situation closely, and CDC is coordinating efforts with the World Health Organization (WHO) and other global partners. Interim guidance is available at https://www.cdc.gov/coronavirus/index.html. As more is learned about this novel virus and this outbreak, CDC will rapidly incorporate new knowledge into guidance for action by CDC, state and local health departments, health care providers, and communities.

title: Epidemiology, virology, and clinical features of severe acute respiratory syndrome -coronavirus-2 (SARS-CoV-2; Coronavirus Disease-19)
abstract: A cluster of severe pneumonia of unknown etiology in Wuhan City, Hubei province in China emerged in December 2019. A novel coronavirus named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was isolated from lower respiratory tract sample as the causative agent. The current outbreak of infections with SARS-CoV-2 is termed Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 rapidly spread into at least 114 countries and killed more than 4,000 people by March 11 2020. WHO officially declared COVID-19 a pandemic on March 11, 2020. There have been 2 novel coronavirus outbreaks in the past 2 decades. The outbreak of severe acute respiratory syndrome (SARS) in 2002–2003 caused by SARS-CoV had a case fatality rate of around 10% (8,098 confirmed cases and 774 deaths), while Middle East respiratory syndrome (MERS) caused by MERS-CoV killed 861 people out of a total 2,502 confirmed cases between 2012 and 2019. The purpose of this review is to summarize known-to-date information about SARS-CoV-2, transmission of SARS-CoV-2, and clinical features.

title: Epidemiological data from the COVID-19 outbreak, real-time case information
abstract: Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.

title: Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT
abstract: Coronavirus disease 2019 (COVID-19) has surprised the world since the beginning of 2020, and the rapid growth of COVID-19 is beyond the capability of doctors and hospitals that could deal in many areas. The chest computed tomography (CT) could be serve as an effective tool in detection of COVID-19. Its valuable to develop automatic detection of COVID-19.  The collected dataset consisted of 521 COVID-19 chest CT images, 397 normal chest CT images, 76 bacterial pneumonia chest CT images and 48 SARS chest CT images obtained by exhaustively searching available data on the Internet. Then, these data are divided into three sets, referred to training set, validation set and testing set. Sixteen data augmentation operations are designed to enrich the training set in deep learning training phase. Multiple experiments were conducted to analyze the performance of the model in the detection of COVID-19 both in case of no noisy labels and noisy labels. The performance was assessed by the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy.  The data augmentation operations on the training set is effective for improvement of the model performance. The area under the receiver operating characteristic curve is 0.9689 with (95% CI: 0.9308, 1) in case of no noisy labels, while the per-exam sensitivity, specificity and accuracy for detecting COVID-19 in the independent testing set are 90.52%, 91.58% and 91.21%, respectively. Besides, the model is robust when part of the training samples is marked incorrectly. The AUC is 92.23% in the case of noisy labels of 10% in the training set.  A deep learning model with insufficient samples can be developed by using data augmentation in assisting medical workers in making quick and correct diagnosis of COVID-19.

title: Ten challenging questions about SARS-CoV-2 and COVID-19.
abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) recently introduced as a global public health problem by the World Health Organization (WHO). The virus outbreak has been documented around the world. Updating data in different aspects of the virus could force us to revise our idea about the main questions concerning coronavirus disease-19 (COVID-19). AREAS COVERED Although our knowledge about the SARS-CoV-2 and COVID-19 is largely based on the very limited data, the information is growing rapidly. The renewed answers to the specific research questions concerning updating data not only reveal gaps for future research but also re-categorized our information. Here, we attempt to briefly discuss ten important questions about SARS-CoV-2 and COVID-19.  Since our knowledge about different aspects of SARS-CoV-2 appears to be in its infancy and is rapidly changing, the provision of the right data is more difficult in this regard. However, we try to rely on results from more extensive research to answer the main questions about this new virus. Therefore, further studies, particularly in the context of the virus pathogenesis, diagnosis, treatment, and vaccine development, are warranted.

title: ViralMSA: Massively scalable reference-guided multiple sequence alignment of viral genomes
abstract: In molecular epidemiology, the identification of clusters of transmissions typically requires the alignment of viral genomic sequence data. However, existing methods of multiple sequence alignment scale poorly with respect to the number of sequences.  ViralMSA is a user-friendly reference-guided multiple sequence alignment tool that leverages the algorithmic techniques of read mappers to enable the multiple sequence alignment of ultra-large viral genome datasets. It scales linearly with the number of sequences, and it is able to align tens of thousands of full viral genomes in seconds. Availability ViralMSA is freely available at https://github.com/niemasd/ViralMSA as an open-source software project.

title: Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media during the COVID-19 Crisis
abstract: The spread of COVID-19 has sparked racism, hate, and xenophobia in social media targeted at Chinese and broader Asian communities. However, little is known about how racial hate spreads during a pandemic and the role of counterhate speech in mitigating the spread. Here we study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterhate spanning three months, containing over 30 million tweets, and a social network with over 87 million nodes. By creating a novel hand-labeled dataset of 2,400 tweets, we train a text classifier to identify hate and counterhate tweets that achieves an average AUROC of 0.852. We identify 891,204 hate and 200,198 counterhate tweets in COVID-HATE. Using this data to conduct longitudinal analysis, we find that while hateful users are less engaged in the COVID-19 discussions prior to their first anti-Asian tweet, they become more vocal and engaged afterwards compared to counterhate users. We find that bots comprise 10.4% of hateful users and are more vocal and hateful compared to non-bot users. Comparing bot accounts, we show that hateful bots are more successful in attracting followers compared to counterhate bots. Analysis of the social network reveals that hateful and counterhate users interact and engage extensively with one another, instead of living in isolated polarized communities. Furthermore, we find that hate is contagious and nodes are highly likely to become hateful after being exposed to hateful content. Importantly, our analysis reveals that counterhate messages can discourage users from turning hateful in the first place. Overall, this work presents a comprehensive overview of anti-Asian hate and counterhate content during a pandemic. The COVID-HATE dataset is available at http://claws.cc.gatech.edu/covid.

title: Coronavirus Disease 2019 (COVID-19): An Evidence Map of Medical Literature
abstract: Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of May 2020, gaps in the existing literature remain unidentified and, hence, unaddressed. In this paper, we summarise the medical literature on COVID-19 between 1 January and 24 March 2020 using evidence maps and bibliometric analysis in order to systematically identify gaps and propose areas for valuable future research. The examined COVID-19 medical literature originated primarily from Asia and focussed mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health research, the use of novel technologies and artificial intelligence, research on the pathophysiology of COVID-19 within different body systems, and research on indirect effects of COVID-19 on the care of non-COVID-19 patients. Research collaboration at the international level was limited although improvements may aid global containment efforts.

title: Repositioning of 8565 Existing Drugs for COVID-19
abstract: see text] The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 7.1 million people and led to over 0.4 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than 10 years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental data set for SARS-CoV-2 or SARS-CoV 3CL (main) protease inhibitors. On the basis of this data set, we develop validated machine learning models with relatively low root-mean-square error to screen 1553 FDA-approved drugs as well as another 7012 investigational or off-market drugs in DrugBank. We found that many existing drugs might be potentially potent to SARS-CoV-2. The druggability of many potent SARS-CoV-2 3CL protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning.

title: Dataset for country profile and mobility analysis in the assessment of COVID-19 pandemic
abstract: Understanding the COVID-19 pandemic is a multidisciplinary effort that requires a significant number of variables. This dataset comprises (i) sociodemographic characteristics, compiled from 35 datasets obtained at UN Data; (ii) mobility metrics that can assist the analysis of social distancing, from Google Community Mobility Reports and; (iii) daily counts of cases and deaths by COVID-19, from the European Centre for Disease Prevention and Control and the Johns Hopkins University Center for Systems Science and Engineering. This unified dataset ranges from February 15, 2020 to April 26, 2020, a total of 72 days, and is provided as a collection of time series for 131 countries with 192 variables. The pipeline to preprocess and generate the dataset, along with the dataset itself, are versioned with the Data Version Control tool (DVC) and are thus easily reproducible.

title: SARS-CoV-2-specific T cells exhibit unique features characterized by robust helper function, lack of terminal differentiation, and high proliferative potential
abstract: Convalescing COVID-19 patients mount robust T cell responses against SARS-CoV-2, suggesting an important role for T cells in viral clearance. To date, the phenotypes of SARS-CoV-2-specific T cells remain poorly defined. Using 38-parameter CyTOF, we phenotyped longitudinal specimens of SARS-CoV-2-specific CD4+ and CD8+ T cells from four individuals who recovered from mild COVID-19. SARS-CoV-2-specific CD4+ T cells were exclusively Th1 cells, and predominantly Tcm with phenotypic features of robust helper function. SARS-CoV-2-specific CD8+ T cells were predominantly atypical Temra cells in a state of less terminal differentiation and therefore capable of expansion. Subsets of SARS-CoV-2-specific T cells exhibit features of being long-lived and capable of homeostatic proliferation consistent with their persistence for over two months. Our results suggest that long-lived and robust T cell immunity is generated following natural SARS-CoV-2 infection, and support an important role for SARS-CoV-2-specific T cells in host control of COVID-19.

title: Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions
abstract: Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71–7.23). Sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.

title: Availability and quality of publicly available health workforce data sources in Australia: a scoping review protocol.
abstract: The health workforce is an integral component of the healthcare system. Comprehensive, high-quality data on the health workforce are essential to identifying gaps in health service provision, as well as informing future health workforce and health services planning, and health policy. While many data sources are used in Australia for these purposes, the quality of the data sources with respect to relevance, accessibility and accuracy is not clear.  This scoping review aims to identify and appraise publicly available data sources describing the Australian health workforce. The review will include any data source (eg, registry, administrative database and survey) or document reporting a data source (eg, journal article, report) on the Australian health workforce, which is publicly available and describes the characteristics of the workforce. The search will be conducted in 10 bibliographic databases and the grey literature using an iterative process. Screening of titles and abstracts will be undertaken by two investigators, independently, using Covidence software. Any disagreement between investigators will be resolved by a third investigator. Documents/data sources identified as potentially eligible will be retrieved in full text and reviewed following the same process. Data will be extracted using a customised data extraction tool. A customised appraisal tool will be used to assess the relevance, accessibility and accuracy of included data sources. ETHICS AND DISSEMINATION The scoping review is a secondary analysis of existing, publicly available data sources and does not require ethics approval. The findings of this scoping review will further our understanding of the quality and availability of data sources used for health workforce and health services planning in Australia. The results will be submitted for publication in peer-reviewed journals and presented at conferences targeted at health workforce and public health topics.

title: Clinical analysis of 23 cases of steroid-associated osteonecrosis of the femoral head with normal initial magnetic resonance imaging presentation
abstract: To explore the clinical characteristics of steroid-associated osteonecrosis of the femoral head (ONFH) presenting initially normal magnetic resonance imaging (MRI) results. This retrospective study examined data from 23 cases that suffered from ONFH but presented a normal image at the first MRI examination after corticosteroid therapy from June 2005 to December 2013. Data on protopathy, age, sex, time of pain onset, MRI examination, and initial diagnosis were collected and analyzed. Average time from steroid therapy to first MRI examination was 45.7 ± 25.5 days (range, 10–94 days). Average time to final diagnosis was 199.9 ± 165.8 days (range, 32–762 days). Of the 23 cases, 21 cases complained of discomfort and were misdiagnosed because of a normal initial MRI scan. Twelve hips progressed to collapse and 1 hip received lumbar discectomy when got the final diagnosis. Cases with continuous pain (9/21) presented with pain at a later time than those with intermittent pain (12/21), although the continuous pain cases were diagnosed earlier. MRI performed 2 to 3 months after steroid therapy may present normal images. Another MRI examination is necessary to make a definite diagnosis.

title: Accounting for Symptomatic and Asymptomatic in a SEIR-type model of COVID-19
abstract: A mathematical model was developed describing the dynamic of the COVID-19 virus over a population considering that the infected can either be symptomatic or not. The model was calibrated using data on the confirmed cases and death from several countries like France, Philippines, Italy, Spain, United Kingdom, China, and the USA. First, we derived the basic reproduction number, $\mathcal{R}_0$, and estimated the effective reproduction $\mathcal{R}_{\rm eff}$ for each country. Second, we were interested in the merits of interventions, either by distancing or by treatment. Results revealed that total and partial containment is effective in reducing the transmission. However, its duration may be long to eradicate the disease (104 days for France). By setting the end of intervention as the day when hospital capacity is reached, numerical simulations showed that the duration can be reduced (up to only 39 days for France if the capacity is 1000 patients). Further, results pointed out that the effective reproduction number remains large after containment. Therefore, testing and isolation are necessary to stop the disease.

title: Mega-COV: A Billion-Scale Dataset of 65 Languages For COVID-19
abstract: We describe Mega-COV, a billion-scale dataset from Twitter for studying COVID-19. The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets (~32M tweets). We release tweet IDs from the dataset, hoping it will be useful for studying various phenomena related to the ongoing pandemic and accelerating viable solutions to associated problems.

title: Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection
abstract: Deep Learning has achieved state of the art performance in medical imaging. However, these methods for disease detection focus exclusively on improving the accuracy of classification or predictions without quantifying uncertainty in a decision. Knowing how much confidence there is in a computer-based medical diagnosis is essential for gaining clinicians trust in the technology and therefore improve treatment. Today, the 2019 Coronavirus (SARS-CoV-2) infections are a major healthcare challenge around the world. Detecting COVID-19 in X-ray images is crucial for diagnosis, assessment and treatment. However, diagnostic uncertainty in the report is a challenging and yet inevitable task for radiologist. In this paper, we investigate how drop-weights based Bayesian Convolutional Neural Networks (BCNN) can estimate uncertainty in Deep Learning solution to improve the diagnostic performance of the human-machine team using publicly available COVID-19 chest X-ray dataset and show that the uncertainty in prediction is highly correlates with accuracy of prediction. We believe that the availability of uncertainty-aware deep learning solution will enable a wider adoption of Artificial Intelligence (AI) in a clinical setting.

title: COVID-19 Guideline Modifications as CMS Announces “Opening Up America Again”: Comments from the Society of Surgical Oncology

title: COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images
abstract: The Coronavirus Disease 2019 (COVID-19) outbreak has a tremendous impact on global health and the daily life of people still living in more than two hundred countries. The crucial action to gain the force in the fight of COVID-19 is to have powerful monitoring of the site forming infected patients. Most of the initial tests rely on detecting the genetic material of the coronavirus, and they have a poor detection rate with the time-consuming operation. In the ongoing process, radiological imaging is also preferred where chest X-rays are highlighted in the diagnosis. Early studies express the patients with an abnormality in chest X-rays pointing to the presence of the COVID-19. On this motivation, there are several studies cover the deep learning-based solutions to detect the COVID-19 using chest X-rays. A part of the existing studies use non-public datasets, others perform on complicated Artificial Intelligent (AI) structures. In our study, we demonstrate an AI-based structure to outperform the existing studies. The SqueezeNet that comes forward with its light network design is tuned for the COVID-19 diagnosis with Bayesian optimization additive. Fine-tuned hyperparameters and augmented dataset make the proposed network perform much better than existing network designs and to obtain a higher COVID-19 diagnosis accuracy.

title: Preparedness and response against diseases with epidemic potential in the European Union: a qualitative case study of Middle East Respiratory Syndrome (MERS) and poliomyelitis in five member states
abstract: EU Decision 1082/2013/EU on serious cross-border health threats provides a legal basis for collaboration between EU Member States, and between international and European level institutions on preparedness, prevention, and mitigation in the event of a public health emergency. The Decision provides a context for the present study, which aims to identify good practices and lessons learned in preparedness and response to Middle East Respiratory Syndrome (MERS) (in UK, Greece, and Spain) and poliomyelitis (in Poland and Cyprus).  Based on a documentary review, followed by five week-long country visits involving a total of 61 interviews and group discussions with experts from both the health and non-health sectors, this qualitative case study has investigated six issues related to preparedness and response to MERS and poliomyelitis: national plans and overall preparedness capacity; training and exercises; risk communication; linking policy and implementation; interoperability between the health and non-health sectors; and cross-border collaboration.  Preparedness and response plans for MERS and poliomyelitis were in place in the participating countries, with a high level of technical expertise available to implement them. Nevertheless, formal evaluation of the responses to previous public health emergencies have sometimes been limited, so lessons learned may not be reflected in updated plans, thereby risking mistakes being repeated in future. The nature and extent of inter-sectoral collaboration varied according to the sectors involved, with those sectors that have traditionally had good collaboration (e.g. animal health and food safety), as well as those that have a financial incentive for controlling infectious diseases (e.g. agriculture, tourism, and air travel) seen as most likely to have integrated public health preparedness and response plans. Although the formal protocols for inter-sectoral collaboration were not always up to date, good personal relations were reported within the relevant professional networks, which could be brought into play in the event of a public health emergency. Cross-border collaboration was greatly facilitated if the neighbouring country was a fellow EU Member State.  Infectious disease outbreaks remain as an ongoing threat. Efforts are required to ensure that core public health capacities for the full range of preparedness and response activities are sustained.  contains supplementary material, which is available to authorized users.

title: Health Education for Parents During the COVID-19 OutbreakPublic Health Education for Parents During the Outbreak of COVID-19: A Rapid Review
abstract: It is well-known that public health education plays a crucial role in the prevention and control of emerging infectious diseases, but how health providers should advise families and parents to obtain health education information is a challenging question. With COVID-19 (Coronavirus disease 2019) spreading around the world, this rapid review aims to answer that question and thus to promote evidence-based decision making in health education policy and practice.  We systematically searched the literature on health education during COVID-19, SARS (severe acute respiratory syndrome) and MERS (middle east respiratory syndrome) epidemics in Medline (via PubMed), Cochrane Library, EMBASE, Web of Science, CBM (China Biology Medicine disc), CNKI (China National Knowledge Infrastructure), and Wanfang Data from their inception until March 31, 2020. The potential bias of the studies was assessed by Joanna Briggs Institute Prevalence Critical Appraisal Tool.  Of 1067 papers found, 24 cross-sectional studies with a total of 35,967 participants were included in this review. The general public lacked good knowledge of SARS and MERS at the early stage of epidemics. Some people's knowledge, attitude and practice (KAP) of COVID-19 had been improved, but the health behaviors of some special groups including children and their parents need to be strengthened. Negative emotions including fear and stigmatization occurred during the outbreaks. Reliable health information was needed to improve public awareness and mental health for infectious diseases. Health information from nonprofit, government and academic websites was more accurate than privately owned commercial websites and media websites.  For educating and cultivating children, parents should obtain information from the official websites of authorities such as the World Health Organization (WHO) and national Centers for Disease Control, or from other sources endorsed by these authorities, rather than from a general search of the internet or social media.

title: News from the front: Excess mortality and life expectancy in two major epicentres of the COVID-19 pandemic in Italy
abstract: Existing studies commonly rely on national official reports to estimate the impact of COVID-19 on population health and human life. However, relying on national reports is problematic because classification and estimation of COVID-19 mortality are not consistent across countries. Likewise, delay coronavirus test results and shortage of testing kits can result in undercounting of coronavirus deaths. To overcome these problems, this study exploits all cause daily death registrations data provide by the Italian Statistical Office (ISTAT) from 1st January to 4th April 2020. This allows us to: 1) calculate excess mortality in 2020 compared to the years 2015 to 2019; and 2) estimate life expectancy on a seasonal and annual basis. We focus our analysis on Bergamo and Brescia, the two hardest hit provinces in Lombardy, northern Italy. Given the clustering nature of the epidemic, focusing on the areas with high concentration of severe illness and deaths allows us to capture the true impact of COVID-19 on mortality and life expectancy, which are likely to be underestimated in the national level data. We find that on the period 1 Jan to 4 April 2020, seasonal life expectancy in Bergamo reduced by around 8.1 and 6.5 years compared to 2019 for men and women respectively (4.5 and 3.4 years in Brescia). The drop in period life expectancy for 2020 may total up to 3 years in the case of men and 2 years in the case of women. Such a sharp decrease in life expectancy has not been experienced in modern history since the Second World War. This study shows that, in the absence of public health interventions to reduce the spread of the virus, COVID-19 has set life expectancy in Bergamo and Brescia back to the Italian life expectancy of 15 years ago.

title: Rapid community-driven development of a SARS-CoV-2 tissue simulator
abstract: The 2019 novel coronavirus, SARS-CoV-2, is an emerging pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors like diabetes. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable “choke points” for pharmacologic interactions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving rapid refinements with a two-to-four week release cycle. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health.

title: A First Instagram Dataset on COVID-19
abstract: The novel coronavirus (COVID-19) pandemic outbreak is drastically shaping and reshaping many aspects of our life, with a huge impact on our social life. In this era of lockdown policies in most of the major cities around the world, we see a huge increase in people and professional engagement in social media. Social media is playing an important role in news propagation as well as keeping people in contact. At the same time, this source is both a blessing and a curse as the coronavirus infodemic has become a major concern, and is already a topic that needs special attention and further research. In this paper, we provide a multilingual coronavirus (COVID-19) Instagram dataset that we have been continuously collected since March 30, 2020. We are making our dataset available to the research community at Github. We believe that this contribution will help the community to better understand the dynamics behind this phenomenon in Instagram, as one of the major social media. This dataset could also help study the propagation of misinformation related to this outbreak.

title: Mapping the Landscape of Artificial Intelligence Applications against COVID-19
abstract: COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, with over 2.5 million confirmed cases as of April 23, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, clinical, and societal applications. We also review datasets, tools, and resources needed to facilitate AI research. Finally, we discuss strategic considerations related to the operational implementation of projects, multidisciplinary partnerships, and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

title: Nonessential Research in the New Normal: The Impact of COVID-19
abstract: Nonessential Research in the New Normal: The Impact of Novel Coronavirus Disease (COVID-19).

title: Potential fecal transmission of SARS-CoV-2: Current evidence and implications for public health
abstract: Coronavirus disease 2019 (COVID-19) emerged in Hubei Province, China in December 2019 and has since become a global pandemic, with hundreds of thousands of cases and over 165 countries affected. Primary routes of transmission of the causative virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are through respiratory droplets and close person-to-person contact. While information about other potential modes of transmission are relatively sparse, evidence supporting the possibility of a fecally mediated mode of transmission has been accumulating. Here, current knowledge on the potential for fecal transmission is briefly reviewed and the possible implications are discussed from a public health perspective.

title: MsFcNET: Multi-scale Feature-Crossing Attention Network for Multi-field Sparse Data
abstract: Feature engineering usually needs to excavate dense-and-implicit cross features from multi-filed sparse data. Recently, many state-of-the-art models have been proposed to achieve low-order and high-order feature interactions. However, most of them ignore the importance of cross features and fail to suppress the negative impact of useless features. In this paper, a novel multi-scale feature-crossing attention network (MsFcNET) is proposed to extract dense-and-implicit cross features and learn their importance in the different scales. The model adopts the DIA-LSTM units to construct a new attention calibration architecture, which can adaptively adjust the weights of features in the process of feature interactions. On the other hand, it also integrates a multi-scale feature-crossing module to strengthen the representation ability of cross features from multi-field sparse data. The extensive experimental results on three real-world prediction datasets demonstrate that our proposed model yields superior performance compared with the other state-of-the-art models.

title: Global Data Science Project for COVID-19 Summary Report
abstract: This paper aims at providing the summary of the Global Data Science Project (GDSC) for COVID-19. as on May 31 2020. COVID-19 has largely impacted on our societies through both direct and indirect effects transmitted by the policy measures to counter the spread of viruses. We quantitatively analysed the multifaceted impacts of the COVID-19 pandemic on our societies including people's mobility, health, and social behaviour changes. People's mobility has changed significantly due to the implementation of travel restriction and quarantine measurements. Indeed, the physical distance has widened at international (cross-border), national and regional level. At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March. In particular, the number of flights connecting Europe dropped drastically in mid of March after the United States announced travel restrictions to Europe and the EU and participating countries agreed to close borders, at 84 percent decline compared to March 10th. Similarly, we examined the impacts of quarantine measures in the major city: Tokyo (Japan), New York City (the United States), and Barcelona (Spain). Within all three cities, we found the significant decline in traffic volume. We also identified the increased concern for mental health through the analysis of posts on social networking services such as Twitter and Instagram. Notably, in the beginning of April 2020, the number of post with #depression on Instagram doubled, which might reflect the rise in mental health awareness among Instagram users. Besides, we identified the changes in a wide range of people's social behaviors, as well as economic impacts through the analysis of Instagram data and primary survey data.

title: One-shot screening of potential peptide ligands on HR1 domain in COVID-19 glycosylated spike (S) protein with deep siamese network
abstract: The novel coronavirus (2019-nCoV) has been declared to be a new international health emergence and no specific drug has been yet identified. Several methods are currently being evaluated such as protease and glycosylated spike (S) protein inhibitors, that outlines the main fusion site among coronavirus and host cells. Notwithstanding, the Heptad Repeat 1 (HR1) domain on the glycosylated spike (S) protein is the region with less mutability and then the most encouraging target for new inhibitors drugs.The novelty of the proposed approach, compared to others, lies in a precise training of a deep neural network toward the 2019-nCoV virus. Where a Siamese Neural Network (SNN) has been trained to distingue the whole 2019-nCoV protein sequence amongst two different viruses family such as HIV-1 and Ebola. In this way, the present deep learning system has precise knowledge of peptide linkage among 2019-nCoV protein structure and differently, of other works, is not trivially trained on public datasets that have not been provided any ligand-peptide information for 2019-nCoV. Suddenly, the SNN shows a sensitivity of $83\%$ of peptide affinity classification, where $3027$ peptides on SATPdb bank have been tested towards the specific region HR1 of 2019-nCoV exhibiting an affinity of $93\%$ for the peptidyl-prolyl cis-trans isomerase (PPIase) peptide. This affinity between PPIase and HR1 can open new horizons of research since several scientific papers have already shown that CsA immunosuppression drug, a main inhibitor of PPIase, suppress the reproduction of different CoV virus included SARS-CoV and MERS-CoV. Finally, to ensure the scientific reproducibility, code and data have been made public at the following link: https://github.com/bionick87/2019-nCoV

title: Chaos game representation dataset of SARS-CoV-2 genome
abstract: As of April 16, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 142,000 deaths and more than 2,000,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream, digital signal processing, and machine learning techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical values representation. Thus, the dataset provides a chaos game representation (CGR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the CGR of 100 instances of SARS-CoV-2 virus, 11540 instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).

title: From SARS to COVID-19: A previously unknown SARS- related coronavirus (SARS-CoV-2) of pandemic potential infecting humans – Call for a One Health approach
abstract: Human coronaviruses continue to pose a threat to human health. The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019 which causes coronavirus disease-2019 (COVID-19), an acute respiratory disease marked the third introduction of a highly pathogenic coronavirus into the human population in the twenty-first century. This recent emergence of a previously unknown coronavirus in China leads to huge impacts on humans globally. Covid-19 is a challenge to global public health. Here, we discuss the COVID-19 outbreak in a one health context, highlighting the need for the implementation of one health measures and practices to improve human health and reduce the emergence of pandemic viruses.

title: Dataset on dynamics of Coronavirus on Twitter
abstract: In this data article, we provide a dataset of 8,982,694 Twitter posts around the coronavirus health global crisis. The data were collected through the Twitter REST API search. We used the rtweet R package to download raw data. The term searched was "Coronavirus" which included the word itself and its hashtag version. We collected the data over 23 days, from January 21 to February 12, 2020. The dataset is multilingual, prevailing English, Spanish, and Portuguese. We include a new variable created from other four variables; it is called "type" of tweets, which is useful for showing the diversity of tweets and the dynamics of users on Twitter. The dataset comprises seven databases which can be analysed separately. On the other hand, they can be crossed to set other researches, among them, trends and relevance of different topics, types of tweets, the embeddedness of users and their profiles, the retweets dynamics, hashtag analysis, as well as to perform social network analysis. This dataset can attract the attention of researchers related to different fields on knowledge, such as data science, social science, network science, health informatics, tourism, infodemiology, and others.

title: Open Data Resources for Fighting COVID-19
abstract: We provide an insight into the open data resources pertinent to the study of the spread of Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behaviour, regional mortality rates, and effectiveness of government measures. Open data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, at a world scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 data-sets at a country-wide level (i.e. China, Italy, Spain, France, Germany, U.S., etc.). In an attempt to facilitate the rapid response to the study of the seasonal behaviour of Covid-19, we enumerate the main open resources in terms of weather and climate variables.  The authors of this paper belong to the CONtrol COvid-19 Team, which is composed of different researches from universities of Spain, Italy, France, Germany, United Kingdom and Argentina. The main goal of CONCO-Team is to develop data-driven methods for the better understanding and control of the pandemic.

title: Dataset of Vietnamese teachers’ perspectives and perceived support during the COVID-19 pandemic
abstract: The COVID-19 pandemic has caused unprecedented damage to the educational system worldwide. Besides the measurable economic impacts in the short-term and long-term, there is intangible destruction within educational institutions. In particular, teachers – the most critical intellectual resources of any schools – have to face various types of financial, physical, and mental struggles due to COVID-19. To capture the current context of more than one million Vietnamese teachers during COVID-19, we distributed an e-survey to more than 2,500 randomly selected teachers from two major teacher communities on Facebook from 6th to 11th April 2020. From over 373 responses, we excluded the observations which violated our cross-check questions and retained 294 observations for further analysis.  Teachers' perspectives regarding the operation of teaching activities during the pandemic; (iii) Teachers' received support from their schools, government bodies, other stakeholders such as teacher unions, and parents' associations; and (iv) teachers' evaluation of school readiness toward digital transformation. Further, the dataset was supplemented with an additional question on the teachers' primary source of professional development activities during the pandemic.

title: Abstracts cont.

title: A UK-wide British Society of Thoracic Imaging COVID-19 imaging repository and database: design, rationale and implications for education and research

title: Insights into the Recent 2019 Novel Coronavirus (SARS-CoV-2) in Light of Past Human Coronavirus Outbreaks
abstract: Coronaviruses (CoVs) are RNA viruses that have become a major public health concern since the Severe Acute Respiratory Syndrome-CoV (SARS-CoV) outbreak in 2002. The continuous evolution of coronaviruses was further highlighted with the emergence of the Middle East Respiratory Syndrome-CoV (MERS-CoV) outbreak in 2012. Currently, the world is concerned about the 2019 novel CoV (SARS-CoV-2) that was initially identified in the city of Wuhan, China in December 2019. Patients presented with severe viral pneumonia and respiratory illness. The number of cases has been mounting since then. As of late February 2020, tens of thousands of cases and several thousand deaths have been reported in China alone, in addition to thousands of cases in other countries. Although the fatality rate of SARS-CoV-2 is currently lower than SARS-CoV, the virus seems to be highly contagious based on the number of infected cases to date. In this review, we discuss structure, genome organization, entry of CoVs into target cells, and provide insights into past and present outbreaks. The future of human CoV outbreaks will not only depend on how the viruses will evolve, but will also depend on how we develop efficient prevention and treatment strategies to deal with this continuous threat.

title: Antiviral Activity of Some Plants Used in Nepalese Traditional Medicine
abstract: Methanolic extracts of 41 plant species belonging to 27 families used in the traditional medicine in Nepal have been investigated for in vitro antiviral activity against Herpes simplex virus type 1 (HSV-1) and influenza virus A by dye uptake assay in the systems HSV-1/Vero cells and influenza virus A/MDCK cells. The extracts of Astilbe rivularis, Bergenia ciliata, Cassiope fastigiata and Thymus linearis showed potent anti-herpes viral activity. The extracts of Allium oreoprasum, Androsace strigilosa, Asparagus filicinus, Astilbe rivularis, Bergenia ciliata and Verbascum thapsus exhibited strong anti-influenza viral activity. Only the extracts of A. rivularis and B. ciliata demonstrated remarkable activity against both viruses.

title: Data sharing for novel coronavirus (COVID-19)

title: Phylogenetic analysis of SARS-CoV-2 genomes in Turkey
abstract: COVID-19 has effectively spread worldwide. As of May 2020, Turkey is among the top ten countries with the most cases. A comprehensive genomic characterization of the virus isolates in Turkey is yet to be carried out. Here, we built a phylogenetic tree with globally obtained 15,277 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes. We identified the subtypes based on the phylogenetic clustering in comparison with the previously annotated classifications. We performed a phylogenetic analysis of the first 30 SARS-CoV-2 genomes isolated and sequenced in Turkey. We suggest that the first introduction of the virus to the country is earlier than the first reported case of infection. Virus genomes isolated from Turkey are dispersed among most types in the phylogenetic tree. We find 2 of the seventeen subclusters enriched with the isolates of Turkey, which likely have spread expansively in the country. Finally, we traced virus genomes based on their phylogenetic placements. This analysis suggested multiple independent international introductions of the virus and revealed a hub for the inland transmission. We released a web application to track the global and interprovincial virus spread of the isolates from Turkey in comparison to thousands of genomes worldwide.

title: Neutralizing Antibodies against SARS-CoV-2 and Other Human Coronaviruses
abstract: Coronavirus (CoV) disease 2019 (COVID-19) caused by severe acute respiratory syndrome (SARS)-CoV-2 (also known as 2019-nCoV) is threatening global public health, social stability, and economic development. To meet this challenge, this article discusses advances in the research and development of neutralizing antibodies (nAbs) for the prevention and treatment of infection by SARS-CoV-2 and other human CoVs.

title: Estimating the early death toll of COVID-19 in the United States
abstract: Efforts to track the severity and public health impact of the novel coronavirus, COVID-19, in the US have been hampered by testing issues, reporting lags, and inconsistency between states. Evaluating unexplained increases in deaths attributed to broad outcomes, such as pneumonia and influenza (P&I) or all causes, can provide a more complete and consistent picture of the burden caused by COVID-19.  We evaluated increases in the occurrence of deaths due to P&I above a seasonal baseline (adjusted for influenza activity) or due to any cause across the United States in February and March 2020. These estimates are compared with reported deaths due to COVID-19 and with testing data.  There were notable increases in the rate of death due to P&I in February and March 2020. In a number of states, these deaths pre-dated increases in COVID-19 testing rates and were not counted in official records as related to COVID-19. There was substantial variability between states in the discrepancy between reported rates of death due to COVID-19 and the estimated burden of excess deaths due to P&I. The increase in all-cause deaths in New York and New Jersey is 1.5–3 times higher than the official tally of COVID-19 confirmed deaths or the estimated excess death due to P&I.  Excess P&I deaths provide a conservative estimate of COVID-19 burden and indicate that COVID-19-related deaths are missed in locations with inadequate testing or intense pandemic activity.

title: The Novel Coronavirus Disease (COVID-19): A PRISMA Systematic Review and Meta-analysis of Clinical and Paraclinical characteristics
abstract: An outbreak of pneumonia, caused by a novel coronavirus (COVID-19) was Identified in China in Dec 2019. This virus expanded worldwide, causing global concern. Clinical, laboratory and imaging features of this infection are characterized in some observational studies. We undertook a systematic review and meta-analysis to assess the frequency of clinical, laboratory, and CT features in COVID-19 patients. We did a systematic review and meta-analysis using three databases to identify clinical, laboratory, and CT features of rRT-PCR confirmed cases of COVID-19. Data for 3420 patients from 30 observational studies were included. Overall, the results showed that fever (84.2%, 95%CI 82.6-85.7), cough (62%, 95%CI 60-64), and fatigue (39.4%, 95%CI 37.2-41.6%) were the most prevalent symptoms in COVID-19 patients. Increased CRP level, decreased lymphocyte count, and increased D-dimer level were the most common laboratory findings. Among COVID-19 patients, 92% had a positive CT finding, most prevalently GGO (60%, 95%CI 58-62) and peripheral distribution (64%, 95%CI 60-69). These results demonstrate the clinical, paraclinical, and imaging features of COIVD-19.

title: Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review
abstract: The coronavirus disease (COVID-19) has been identified as the cause of an outbreak of respiratory illness in Wuhan, Hubei Province, China beginning in December 2019. As of 31 January 2020, this epidemic had spread to 19 countries with 11 791 confirmed cases, including 213 deaths. The World Health Organization has declared it a Public Health Emergency of International Concern.  A scoping review was conducted following the methodological framework suggested by Arksey and O’Malley. In this scoping review, 65 research articles published before 31 January 2020 were analyzed and discussed to better understand the epidemiology, causes, clinical diagnosis, prevention and control of this virus. The research domains, dates of publication, journal language, authors’ affiliations, and methodological characteristics were included in the analysis. All the findings and statements in this review regarding the outbreak are based on published information as listed in the references.  Most of the publications were written using the English language (89.2%). The largest proportion of published articles were related to causes (38.5%) and a majority (67.7%) were published by Chinese scholars. Research articles initially focused on causes, but over time there was an increase of the articles related to prevention and control. Studies thus far have shown that the virus’ origination is in connection to a seafood market in Wuhan, but specific animal associations have not been confirmed. Reported symptoms include fever, cough, fatigue, pneumonia, headache, diarrhea, hemoptysis, and dyspnea. Preventive measures such as masks, hand hygiene practices, avoidance of public contact, case detection, contact tracing, and quarantines have been discussed as ways to reduce transmission. To date, no specific antiviral treatment has proven effective; hence, infected people primarily rely on symptomatic treatment and supportive care.  There has been a rapid surge in research in response to the outbreak of COVID-19. During this early period, published research primarily explored the epidemiology, causes, clinical manifestation and diagnosis, as well as prevention and control of the novel coronavirus. Although these studies are relevant to control the current public emergency, more high-quality research is needed to provide valid and reliable ways to manage this kind of public health emergency in both the short- and long-term.

title: Studying the pathophysiology of coronavirus disease 2019: a protocol for the Berlin prospective COVID-19 patient cohort (Pa-COVID-19)
abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide causing a global health emergency. Pa-COVID-19 aims to provide comprehensive data on clinical course, pathophysiology, immunology and outcome of COVID-19, to identify prognostic biomarkers, clinical scores, and therapeutic targets for improved clinical management and preventive interventions.  Pa-COVID-19 is a prospective observational cohort study of patients with confirmed SARS-CoV-2 infection treated at Charité - Universitätsmedizin Berlin. We collect data on epidemiology, demography, medical history, symptoms, clinical course, and pathogen testing and treatment. Systematic, serial blood sampling will allow deep molecular and immunological phenotyping, transcriptomic profiling, and comprehensive biobanking. Longitudinal data and sample collection during hospitalization will be supplemented by long-term follow-up.  Outcome measures include the WHO clinical ordinal scale on day 15 and clinical, functional, and health-related quality-of-life assessments at discharge and during follow-up. We developed a scalable dataset to (i) suit national standards of care, (ii) facilitate comprehensive data collection in medical care facilities with varying resources, and (iii) allow for rapid implementation of interventional trials based on the standardized study design and data collection. We propose this scalable protocol as blueprint for harmonized data collection and deep phenotyping in COVID-19 in Germany.  We established a basic platform for harmonized, scalable data collection, pathophysiological analysis, and deep phenotyping of COVID-19, which enables rapid generation of evidence for improved medical care and identification of candidate therapeutic and preventive strategies. The electronic database accredited for interventional trials allows fast trial implementation for candidate therapeutic agents.

title: A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images
abstract: In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. We show that similar results can be obtained using X-Ray images that do not contain most of the lungs. We are able to remove the lungs from the images by turning to black the center of the X-Ray scan and training our classifiers only on the outer part of the images. Hence, we deduce that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the dataset that are not correlated to the presence of COVID-19. Finally, we show that creating a fair testing protocol is a challenging task, and we provide a method to measure how fair a specific testing protocol is. In the future research we suggest to check the fairness of a testing protocol using our tools and we encourage researchers to look for better techniques than the ones that we propose.

title: The outbreak of SARS-CoV-2 pneumonia calls for viral vaccines
abstract: The outbreak of 2019-novel coronavirus disease (COVID-19) that is caused by SARS-CoV-2 has spread rapidly in China, and has developed to be a Public Health Emergency of International Concern However, no specific antiviral treatments or vaccines are available yet This work aims to share strategies and candidate antigens to develop safe and effective vaccines against SARS-CoV-2 An outbreak of 2019-novel coronavirus (SARS-CoV-2) that causes atypical pneumonia (COVID-19) has raged in China since mid-December 2019 and has spread to 26 countries (February 20, 2020) The epidemic was identified by the first four cases confirmed on December 29, 2019 and was traced to the Huanan Seafood Wholesale Market, Wuhan city, Hubei Province, China1 A total of 75,465 cases with SARS-CoV-2 infections have been confirmed up to date (February 20, 2020), and 2,236 people have died in China2 COVID-19 spreads rapidly by human-to-human transmission with a median incubation period of 3 0 days (range, 0 to 24 0), and the time from symptom onset to developing pneumonia is 4 0 days (range, 2 0 to 7 0)3 Respiratory droplets and direct contact are conventional transmission routes for SARS-CoV-2, and fecal-to-oral transmission might also have a role3 Fever, dry cough, and fatigue are common symptoms at onset of COVID-194 Most patients have lymphopenia and bilateral ground-glass opacity changes on chest CT scans4,5 No specific antiviral treatments or vaccines are available because it is a new emerging viral disease Development of SARS-CoV-2-based vaccines is urgently required The entire virus particle-based preparation of vaccines, including inactivated and attenuated virus vaccines is advisable, because it is based on previous studies about the prevention and control of seasonal influenza vaccines6 The first SARS-CoV-2 (Wuhan-Hu-1) was successfully sequenced and its genomic sequence submitted to GenBank on January 5, 2020 (Accession no MN908947 3)7 Subsequently large-scale culture of SARS-CoV-2 was quickly performed, and an inactivated virus vaccine could be prepared through the employment of established physical and chemical methods such as UV light, formaldehyde, and β-propiolactone8 The development of attenuated-virus vaccines is also possible by carefully screening the serially propagated SARS-CoV-2 with reduced pathogenesis such as induced minimal lung injury, diminished limited neutrophil influx, and increased anti-inflammatory cytokine expressions compared with the wild-type virus9 Both inactivated and attenuated virus vaccines have their own disadvantages and side effects (Table 1) Alternatively, new vaccine designs based on the putative protective antigen/peptides derived from SARS-CoV-2 should be considered

title: Missing clinical trial data: the knowledge gap in the safety of potential COVID-19 drugs
abstract: (290/300 words)  Several drugs are being repurposed for the treatment of the coronavirus disease 2019 (COVID-19) pandemic based on in vitro or early clinical findings. As these drugs are being used in varied regimens and dosages, it is important to enable synthesis of existing safety data from clinical trials. However, availability of safety information is limited by a lack of timely reporting of clinical trial results on public registries or through academic publication. We aimed to analyse the knowledge gap in safety data by quantifying the number of missing clinical trial results for drugs potentially being repurposed for COVID-19.  ClinicalTrials.gov was searched for 19 drugs that have been identified as potential treatments for COVID-19. Relevant clinical trials for any prior indication were listed by identifier (NCT number) and checked for timely result reporting (within 395 days of the primary completion date). Additionally, PubMed and Google Scholar were searched using the NCT number to identify publications of results not listed on the registry. A second, blinded search of 10% of trials was conducted to assess reviewer concordance.  Of 3754 completed trials, 1516 (40.4%) did not post results on ClinicalTrials.gov or in the academic literature. 1172 (31.2%) completed trials had tabular results on ClinicalTrials.gov. A further 1066 (28.4%) completed trials had results from the literature search, but did not report results on ClinicalTrials.gov. Key drugs missing clinical trial results include hydroxychloroquine (37.0% completed trials unreported), favipiravir (77.8%) and lopinavir (40.5%).  There is an important evidence gap for the safety of drugs being repurposed for COVID-19. This uncertainty could cause a large burden of additional morbidity and mortality during the pandemic. We recommend caution in experimental drug use for non-severe disease and urge clinical trial sponsors to report missing results retrospectively.

title: COVID-19 randomised trial protocols: rapid publication without barriers

title: The Shifting Landscape of Genitourinary Oncology During the COVID-19 Pandemic and how Italian Oncologists Reacted: Results from a National Survey

title: Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
abstract: Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19 related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.

title: Where to buy face masks? Survey of applications using Taiwan's open data in the time of coronavirus disease 2019
abstract: The coronavirus disease 2019 (COVID-19) had spread rapidly since late December 2019. Personal protective equipment was essential to prevent transmission. Owing to shortage of face masks, Taiwan government began to implement quasi rationing on February 6, 2020, by allowing each resident to purchase two masks in seven days. Taiwan National Health Insurance Administration offered online data with real-time updates on face mask availability in all contracted pharmacies and selected local health centers. Based on the open data, numerous software applications quickly emerged to assist the public in finding sales locations efficiently.  Up until March 15, 2020, the Public Digital Innovation Space of Taiwan government had recorded 134 software applications of face mask availability, and 24 software applications were excluded due to defect, duplicate, and unavailability. These applications were analyzed according to platform, developer type, and display mode.  Of the 110 valid software applications, 67 (60.9%) applications were deployed on websites, followed by 21 (19.1%) on social networking sites, 19 (17.3%) as mobile applications, and 3 (2.7%) in other modes. Nearly two thirds (n = 70) of applications were developed by individuals, one third (n = 37) by commercial companies, only two applications by central and local governments, and one by a nongovernmental organization. With respect to the display mode, 47 (42.7%) applications adopted map-view only, 41 (37.3%) adopted table-view only, and 19 (17.3%) adopted both modes. Of the remaining three applications, two offered voice user interfaces and one used augmented reality.  Taiwan's open data strategy facilitated rapid development of software applications for information dissemination to the public during the COVID-19 crisis. The transparency of real-time data could help alleviate the panic of the public. The collaborative contributions from the grassroots in disasters were priceless treasures.

title: CoV2ID: Detection and Therapeutics Oligo Database for SARS-CoV-2
abstract: The ability to detect the SARS-CoV-2 in a widespread epidemic is crucial for screening of carriers and for the success of quarantine efforts. Methods based on real-time reverse transcription polymerase chain reaction (RT-qPCR) and sequencing are being used for virus detection and characterization. However, RNA viruses are known for their high genetic diversity which poses a challenge for the design of efficient nucleic acid-based assays. The first SARS-CoV-2 genomic sequences already showed novel mutations, which may affect the efficiency of available screening tests leading to false-negative diagnosis or inefficient therapeutics. Here we describe the CoV2ID (http://covid.portugene.com/), a free database built to facilitate the evaluation of molecular methods for detection of SARS-CoV-2 and treatment of COVID-19. The database evaluates the available oligonucleotide sequences (PCR primers, RT-qPCR probes, etc.) considering the genetic diversity of the virus. Updated sequences alignments are used to constantly verify the theoretical efficiency of available testing methods. Detailed information on available detection protocols are also available to help laboratories implementing SARS-CoV-2 testing.

title: Drivers of emerging risks and their interactions in the domain of biological risks to animal, plant and public health: a pilot study

title: Dashboard of sentiment in Austrian social media during COVID-19
abstract: To track online emotional expressions of the Austrian population close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources. This enables decision makers and the interested public to assess issues such as the attitude towards counter-measures taken during the pandemic and the possible emergence of a (mental) health crisis early on. We use web scraping and API access to retrieve data from the news platform derstandard.at, Twitter and a chat platform for students. We document the technical details of our workflow in order to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allows us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We use special word clouds to visualize that overall difference. Longitudinally, our time series show spikes in anxiety that can be linked to several events and media reporting. Additionally, we find a marked decrease in anger. The changes last for remarkably long periods of time (up to 12 weeks). We discuss these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online under http://www.mpellert.at/covid19_monitor_austria/. Our work has attracted media attention and is part of an web archive of resources on COVID-19 collected by the Austrian National Library.

title: Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study
abstract: The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease.  In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK–CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation.  We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41–4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0.  We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality.

title: Single-cell Analysis of ACE2 Expression in Human Kidneys and Bladders Reveals a Potential Route of 2019-nCoV Infection
abstract: Since December 2019, a novel coronavirus named 2019 coronavirus (2019-nCoV) has emerged in Wuhan of China and spread to several countries worldwide within just one month. Apart from fever and respiratory complications, acute kidney injury has been observed in some patients with 2019-nCoV. In a short period of time, angiotensin converting enzyme II (ACE2), have been proposed to serve as the receptor for the entry of 2019-nCoV, which is the same for severe acute respiratory syndrome coronavirus (SARS). To investigate the possible cause of kidney damage in 2019-nCoV patients, we used both published kidney and bladder cell atlas data and an independent unpublished kidney single cell RNA-Seq data generated in-house to evaluate ACE2 gene expressions in all cell types in healthy kidneys and bladders. Our results showed the enriched expression of all subtypes of proximal tubule cells of kidney and low but detectable levels of expression in bladder epithelial cells. These results indicated the urinary system is a potential route for 2019-nCoV infection, along with the respiratory system and digestion system. Our findings suggested the kidney abnormalities of SARS and 2019-nCoV patients may be due to proximal tubule cells damage and subsequent systematic inflammatory response induced kidney injury. Beyond that, laboratory tests of viruses and related indicators in urine may be needed in some special patients of 2019-nCoV.

title: Transmission and epidemiological characteristics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected Pneumonia (COVID-19): preliminary evidence obtained in comparison with 2003-SARS
abstract: Latest epidemic data of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected Pneumonia (COVID-19) was collected and a detailed statistical analysis was carried out to make comparison with 2003-SARS in order to provide scientific reference for the prevention and control of COVID-19.  The information of COVID-19 and 2003-SARS from websites of NHCPRC and the World Health Organization was collected, and then the transmission dynamics of the two kinds of infectious diseases were analyzed. The information of 853 confirmed COVID-19 patients obtained from the website of health committees of 18 provinces. A descriptive epidemiological analysis method was employed to carefully analyze the epidemic characteristics. Subsequently, the COVID-19 epidemic data in Wuhan and other inland regions of China was analyzed separately and compared. A multivariate function model was constructed based on the confirmed COVID-19 case data.  The growth rate of new cases and deaths of COVID-19 were significantly faster than those of 2003-SARS. The number of confirmed cases in Wuhan and other inland areas both showed increasing trends. 853 confirmed COVID-19 cases aged 1 months to 94 years and the average age was (45.05 ± 17.22) years. The gender ratio (M: F) was 1.12: 1.  The fatality rate of COVID-19 is lower than that of 2003-SARS and the cure rate is higher. The age of COVID-19 patients is mainly concentrated in the 30-50 years old (60.61%). The harm of the first-generation COVID-19 patients is higher than that of secondary cases.

title: Rapid implementation of mobile technology for real-time epidemiology of COVID-19
abstract: The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic (COVID-19) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (COPE) consortium to bring together scientists with expertise in big data research and epidemiology to develop a COVID-19 Symptom Tracker mobile application that we launched in the UK on March 24, 2020 and the US on March 29, 2020 garnering more than 2.25 million users to date. This mobile application offers data on risk factors, herald symptoms, clinical outcomes, and geographical hot spots. This initiative offers critical proof-of-concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis which is critical for a data-driven response to this public health challenge.

title: Big Data and the Global Public Health Intelligence Network (GPHIN).
abstract: Globalization and the potential for rapid spread of emerging infectious diseases have heightened the need for ongoing surveillance and early detection. The Global Public Health Intelligence Network (GPHIN) was established to increase situational awareness and capacity for the early detection of emerging public health events.  To describe how the GPHIN has used Big Data as an effective early detection technique for infectious disease outbreaks worldwide and to identify potential future directions for the GPHIN.  Every day the GPHIN analyzes over more than 20,000 online news reports (over 30,000 sources) in nine languages worldwide. A web-based program aggregates data based on an algorithm that provides potential signals of emerging public health events which are then reviewed by a multilingual, multidisciplinary team. An alert is sent out if a potential risk is identified. This process proved useful during the Severe Acute Respiratory Syndrome (SARS) outbreak and was adopted shortly after by a number of countries to meet new International Health Regulations that require each country to have the capacity for early detection and reporting. The GPHIN identified the early SARS outbreak in China, was credited with the first alert on MERS-CoV and has played a significant role in the monitoring of the Ebola outbreak in West Africa. Future developments are being considered to advance the GPHIN's capacity in light of other Big Data sources such as social media and its analytical capacity in terms of algorithm development.  The GPHIN's early adoption of Big Data has increased global capacity to detect international infectious disease outbreaks and other public health events. Integration of additional Big Data sources and advances in analytical capacity could further strengthen the GPHIN's capability for timely detection and early warning.

title: COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations
abstract: The ongoing Coronavirus Disease (COVID-19) pandemic highlights the interconnected-ness of our present-day globalized world. With social distancing policies in place, virtual communication has become an important source of (mis)information. As increasing number of people rely on social media platforms for news, identifying misinformation has emerged as a critical task in these unprecedented times. In addition to being malicious, the spread of such information poses a serious public health risk. To this end, we design a dashboard to track misinformation on popular social media news sharing platform - Twitter. The dashboard allows visibility into the social media discussions around Coronavirus and the quality of information shared on the platform, updated over time. We collect streaming data using the Twitter API from March 1, 2020 to date and identify false, misleading and clickbait contents from collected Tweets. We provide analysis of user accounts and misinformation spread across countries. In addition, we provide analysis of public sentiments on intervention policies such as"#socialdistancing"and"#workfromhome", and we track topics, and emerging hashtags and sentiments over countries. The dashboard maintains an evolving list of misinformation cascades, sentiments and emerging trends over time, accessible online at https://usc-melady.github.io/COVID-19-Tweet-Analysis.  COVID-19, Misinformation, Fake News, Social Media

title: Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan
abstract: A mysterious outbreak of atypical pneumonia in late 2019 was traced to a seafood wholesale market in Wuhan of China. Within a few weeks, a novel coronavirus tentatively named as 2019 novel coronavirus (2019-nCoV) was announced by the World Health Organization. We performed bioinformatics analysis on a virus genome from a patient with 2019-nCoV infection and compared it with other related coronavirus genomes. Overall, the genome of 2019-nCoV has 89% nucleotide identity with bat SARS-like-CoVZXC21 and 82% with that of human SARS-CoV. The phylogenetic trees of their orf1a/b, Spike, Envelope, Membrane and Nucleoprotein also clustered closely with those of the bat, civet and human SARS coronaviruses. However, the external subdomain of Spike’s receptor binding domain of 2019-nCoV shares only 40% amino acid identity with other SARS-related coronaviruses. Remarkably, its orf3b encodes a completely novel short protein. Furthermore, its new orf8 likely encodes a secreted protein with an alpha-helix, following with a beta-sheet(s) containing six strands. Learning from the roles of civet in SARS and camel in MERS, hunting for the animal source of 2019-nCoV and its more ancestral virus would be important for understanding the origin and evolution of this novel lineage B betacoronavirus. These findings provide the basis for starting further studies on the pathogenesis, and optimizing the design of diagnostic, antiviral and vaccination strategies for this emerging infection.

title: Association of inflammatory markers with the severity of COVID-19
abstract: The ongoing worldwide epidemic of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a huge threat to global public health. However, with regard to the effects of inflammatory markers on the severity of COVID-19, studies have reported associations that vary in strength and direction.  In the meta-analysis, we aimed to provide an overview of the association of inflammatory markers with severity of COVID-19.  The following databases were searched: PubMed, Embase, Cochrane Library, Wanfang database and CNKI (China National Knowledge Infrastructure) database until March 20, 2020. Weighted mean difference (WMD) and 95% confidence intervals (CIs) were pooled using random or fixed-effects models.  A total of 16 studies were included in our analysis comprising of 3962 patients with COVID-19. Random-effects results demonstrated that patients with COVID-19 in non-severe group had lower levels for CRP (WMD = -41.78 mg/l, 95% CI = [-52.43, -31.13], P < 0.001), PCT (WMD = -0.13 ng/ml, 95% CI = [-0.20, -0.05], P < 0.001), IL-6 (WMD = -21.32 ng/l, 95% CI = [-28.34, -14.31], P < 0.001), ESR (WMD = -8.40 mm/h, 95% CI = [-14.32, -2.48], P = 0.005), SAA (WMD = -43.35 μg/ml, 95% CI = [-80.85, -5.85], P = 0.020) and serum ferritin (WMD = -398.80 mg/l, 95% CI = [-625.89, -171.71], P < 0.001), compared with those in severe group. Moreover, survivors had lower level for IL-6 than non-survivors with COVID-19 (WMD = -4.80 ng/ml, 95% CI = [-5.87, -3.73], P < 0.001). These results were consistent through sensitivity analysis and publication bias assessment.  The meta-analysis highlights the association of inflammatory markers with the severity of COVID-19. Measurement of inflammatory markers might help clinicians to monitor and evaluate the severity and prognosis of COVID-19.

title: Which type of cancer patients are more susceptible to the SARS-COX-2: evidence from a meta-analysis and bioinformatics analysis
abstract: Numerous studies have reported that patients with cancer seem to be more likely to be diagnosed with COVID-19. However, it is still unknown the prevalence of different types of cancer in patients with COVID-19. Therefore, this study will explore which type of cancer patients are more susceptible to the SARS-COX-2.  Eligible studies were identified by searching several electronic databases for relevant studies published before April 28, 2020. The language was restricted to English or Chinese. The meta-analysis was performed using Stata 12.0. The GEPIA database evaluated the expression of SARS-COX-2 infection key genes in different types of normal and tumor samples.  A total of 6 studies, including 205 patients, were identified to be eligible for this meta-analysis. Among the cancer patients infected by SARS-COX-2, the proportion of patients with the lung, colorectal,breast,esophagus,bladder,pancreatic and cervical cancer were 24.7%, 20.5%, 13.0%, 7.6%,7.3%,6.1%,and 6.0%, respectively. These findings were also corroborated by the results of the GEPIA database.  Compared with other types of cancer, lung cancer and colorectal cancer are more susceptible to SARS-COX-2 infection.

title: Cancer datasets and the SARS-CoV-2 pandemic: establishing principles for collaboration

title: Novel 2019 coronavirus: Genome structure, clinical trials, and outstanding questions
abstract: IMPACT STATEMENT: Early availability of the sequence, the genetic material of SARS-CoV-2 (the virus that causes COVID-19), has prompted efforts towards identifying a safe and effective vaccine in the current public health emergency. To that end, understanding the pathophysiology of disease is crucial for scientists around the world. Since conventional vaccine development and manufacturing may take several years, it is important to think about alternative strategies that we could use to mitigate imminent catastrophe. We hope that this article will open up new avenues and provide insights that could potentially save hundreds of lives affected by COVID-19.

title: Can free open access resources strengthen knowledge-based emerging public health priorities, policies and programs in Africa?
abstract: Tackling emerging epidemics and infectious diseases burden in Africa requires increasing unrestricted open access and free use or reuse of regional and global policies reforms as well as timely communication capabilities and strategies. Promoting, scaling up data and information sharing between African researchers and international partners are of vital importance in accelerating open access at no cost. Free Open Access (FOA) health data and information acceptability, uptake tactics and sustainable mechanisms are urgently needed. These are critical in establishing real time and effective knowledge or evidence-based translation, proven and validated approaches, strategies and tools to strengthen and revamp health systems. As such, early and timely access to needed emerging public health information is meant to be instrumental and valuable for policy-makers, implementers, care providers, researchers, health-related institutions and stakeholders including populations when guiding health financing, and planning contextual programs.

title: SARS-CoV-2 and viral sepsis: observations and hypotheses
abstract: Since the outbreak of coronavirus disease 2019 (COVID-19), clinicians have tried every effort to understand the disease, and a brief portrait of its clinical features have been identified. In clinical practice, we noticed that many severe or critically ill COVID-19 patients developed typical clinical manifestations of shock, including cold extremities and weak peripheral pulses, even in the absence of overt hypotension. Understanding the mechanism of viral sepsis in COVID-19 is warranted for exploring better clinical care for these patients. With evidence collected from autopsy studies on COVID-19 and basic science research on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and SARS-CoV, we have put forward several hypotheses about SARS-CoV-2 pathogenesis after multiple rounds of discussion among basic science researchers, pathologists, and clinicians working on COVID-19. We hypothesise that a process called viral sepsis is crucial to the disease mechanism of COVID-19. Although these ideas might be proven imperfect or even wrong later, we believe they can provide inputs and guide directions for basic research at this moment.

title: The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak – an update on the status
abstract: An acute respiratory disease, caused by a novel coronavirus (SARS-CoV-2, previously known as 2019-nCoV), the coronavirus disease 2019 (COVID-19) has spread throughout China and received worldwide attention. On 30 January 2020, World Health Organization (WHO) officially declared the COVID-19 epidemic as a public health emergency of international concern. The emergence of SARS-CoV-2, since the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 and Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, marked the third introduction of a highly pathogenic and large-scale epidemic coronavirus into the human population in the twenty-first century. As of 1 March 2020, a total of 87,137 confirmed cases globally, 79,968 confirmed in China and 7169 outside of China, with 2977 deaths (3.4%) had been reported by WHO. Meanwhile, several independent research groups have identified that SARS-CoV-2 belongs to β-coronavirus, with highly identical genome to bat coronavirus, pointing to bat as the natural host. The novel coronavirus uses the same receptor, angiotensin-converting enzyme 2 (ACE2) as that for SARS-CoV, and mainly spreads through the respiratory tract. Importantly, increasingly evidence showed sustained human-to-human transmission, along with many exported cases across the globe. The clinical symptoms of COVID-19 patients include fever, cough, fatigue and a small population of patients appeared gastrointestinal infection symptoms. The elderly and people with underlying diseases are susceptible to infection and prone to serious outcomes, which may be associated with acute respiratory distress syndrome (ARDS) and cytokine storm. Currently, there are few specific antiviral strategies, but several potent candidates of antivirals and repurposed drugs are under urgent investigation. In this review, we summarized the latest research progress of the epidemiology, pathogenesis, and clinical characteristics of COVID-19, and discussed the current treatment and scientific advancements to combat the epidemic novel coronavirus.

title: Protein structure analysis of the interactions between SARS-CoV-2 spike protein and the human ACE2 receptor: from conformational changes to novel neutralizing antibodies
abstract: The recent severe acute respiratory syndrome, known as Corona Virus Disease 2019 (COVID-19) has spread so much rapidly and severely to induce World Health Organization (WHO) to declare state of emergency over the new coronavirus SARS-CoV-2 pandemic. While several countries have chosen the almost complete lock-down for slowing down SARS-CoV-2 spread, scientific community is called to respond to the devastating outbreak by identifying new tools for diagnosis and treatment of the dangerous COVID-19. With this aim we performed an in silico comparative modeling analysis, which allows to gain new insights about the main conformational changes occurring in the SARS-CoV-2 spike protein, at the level of the receptor binding domain (RBD), along interactions with human cells angiotensin converting enzyme 2 (ACE2) receptor, that favour human cell invasion. Furthermore, our analysis provides i) an ideal pipeline to identify already characterized antibodies that might target SARS-CoV-2 spike RBD, for preventing interactions with the human ACE2, and ii) instructions for building new possible neutralizing antibodies, according to chemical/physical space restraints and complementary determining regions (CDR) mutagenesis of the identified existing antibodies. The proposed antibodies show in silico a high affinity for SARS-CoV-2 spike RBD and can be used as reference antibodies also for building new high affinity antibodies against present and future coronavirus able to invade human cells through interactions of their spike proteins with the human ACE2. More in general, our analysis provides indications for the set-up of the right biological molecular context for investigating spike RBD-ACE2 interactions for the development of new vaccines, diagnosis kits and other treatments based on the usage or the targeting of SARS-CoV-2 spike protein.

title: Ecologic correlation between underlying population level morbidities and COVID-19 case fatality rate among countries infected with SARS-CoV-2
abstract: The ongoing Coronavirus disease 2019 (COVID-19) pandemic is unprecedented in scope. High income countries (HIC) seemingly account for the majority of the mortalities considering that these countries have screened more persons. Low middle income countries (LMIC) countries may experience far worse mortalities considering the existence of a weaker health care system and the several underlying population level morbidities. As a result, it becomes imperative to understand the ecological correlation between critical underlying population level morbidities and COVID-19 case fatality rates (CFR).  This is an ecological study using data on COVID-19 cases, prevalence of COPD, prevalence of tobacco use, adult HIV prevalence, quality of air and life expectancy. We plotted a histogram, performed the Shapiro-Wilk normality test and used spearman correlation to assess the degree of correlation between COVID-19 case fatality rate (CFR) and other covariates mentioned above.  As at the 31st of March 2020, there were a total of 846,281 cases of COVID-19 from 204 countries and a global case fatality rate of 5% (range 0% to 29%). Angola and Sudan both had the highest CFR of 29%, while Italy had the highest number of deaths (i.e. 12,428) as at 31st of March 2020. Adult HIV prevalence has a significant but weak negative correlation with CFR (correlation coefficient = - 0.24, p value =0.01) while all the other variables have positive correlation with CFR due to COVID-19 though not statistically significant. Of the 204 countries analyzed, only 11 countries (i.e. 5%) had complete datasets across all 5 population level morbidities (i.e. prevalence of COPD, prevalence of tobacco use, life expectancy, quality of air, and adult HIV prevalence variables). Correlations of CFR from these 11 countries were similar to that from the 204 countries except for the correlation with quality of air and prevalence of tobacco use.  While we interpret our data with caution given the fact that this is an ecological study, our findings suggest that population level factors such as prevalence of COPD, prevalence of tobacco use, life expectancy and quality of air are positively correlated with CFR from COVID-19 but, adult HIV prevalence has a weak and negative correlation with COVID-19 CFR and would require extensive research.

title: What We Know So Far (As of March 26, 2020) About COVID-19 – An MRT Point of View

title: A study of non-prescription usage of antibiotics in the upper respiratory tract infections in the urban population

title: Severe respiratory illness associated with a nationwide outbreak of enterovirus D68 in the USA (2014): a descriptive epidemiological investigation
abstract: Enterovirus D68 (EV-D68) has been infrequently reported historically, and is typically associated with isolated cases or small clusters of respiratory illness. Beginning in August, 2014, increases in severe respiratory illness associated with EV-D68 were reported across the USA. We aimed to describe the clinical, epidemiological, and laboratory features of this outbreak, and to better understand the role of EV-D68 in severe respiratory illness.  We collected regional syndromic surveillance data for epidemiological weeks 23 to 44, 2014, (June 1 to Nov 1, 2014) and hospital admissions data for epidemiological weeks 27 to 44, 2014, (June 29 to Nov 1, 2014) from three states: Missouri, Illinois and Colorado. Data were also collected for the same time period of 2013 and 2012. Respiratory specimens from severely ill patients nationwide, who were rhinovirus-positive or enterovirus-positive in hospital testing, were submitted between Aug 1, and Oct 31, 2014, and typed by molecular sequencing. We collected basic clinical and epidemiological characteristics of EV-D68 cases with a standard data collection form submitted with each specimen. We compared patients requiring intensive care with those who did not, and patients requiring ventilator support with those who did not. Mantel-Haenszel χ(2) tests were used to test for statistical significance.  Regional and hospital-level data from Missouri, Illinois, and Colorado showed increases in respiratory illness between August and September, 2014, compared with in 2013 and 2012. Nationwide, 699 (46%) of 1529 patients tested were confirmed as EV-D68. Among the 614 EV-D68-positive patients admitted to hospital, age ranged from 3 days to 92 years (median 5 years). Common symptoms included dyspnoea (n=513 [84%]), cough (n=500 [81%]), and wheezing (n=427 [70%]); 294 (48%) patients had fever. 338 [59%] of 574 were admitted to intensive care units, and 145 (28%) of 511 received ventilator support; 322 (52%) of 614 had a history of asthma or reactive airway disease; 200 (66%) of 304 patients with a history of asthma or reactive airway disease required intensive care compared with 138 (51%) of 270 with no history of asthma or reactive airway disease (p=0·0004). Similarly, 89 (32%) of 276 patients with a history of asthma or reactive airway disease required ventilator support compared with 56 (24%) of 235 patients with no history of asthma or reactive airway disease (p=0·039).  In 2014, EV-D68 caused widespread severe respiratory illness across the USA, disproportionately affecting those with asthma. This unexpected event underscores the need for robust surveillance of enterovirus types, enabling improved understanding of virus circulation and disease burden.

title: Information Mining for COVID-19 Research From a Large Volume of Scientific Literature
abstract: The year 2020 has seen an unprecedented COVID-19 pandemic due to the outbreak of a novel strain of coronavirus in 180 countries. In a desperate effort to discover new drugs and vaccines for COVID-19, many scientists are working around the clock. Their valuable time and effort may benefit from computer-based mining of a large volume of health science literature that is a treasure trove of information. In this paper, we have developed a graph-based model using abstracts of 10,683 scientific articles to find key information on three topics: transmission, drug types, and genome research related to coronavirus. A subgraph is built for each of the three topics to extract more topic-focused information. Within each subgraph, we use a betweenness centrality measurement to rank order the importance of keywords related to drugs, diseases, pathogens, hosts of pathogens, and biomolecules. The results reveal intriguing information about antiviral drugs (Chloroquine, Amantadine, Dexamethasone), pathogen-hosts (pigs, bats, macaque, cynomolgus), viral pathogens (zika, dengue, malaria, and several viruses in the coronaviridae virus family), and proteins and therapeutic mechanisms (oligonucleotide, interferon, glycoprotein) in connection with the core topic of coronavirus. The categorical summary of these keywords and topics may be a useful reference to expedite and recommend new and alternative directions for COVID-19 research.

title: Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis
abstract: The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription polymerase chain reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and violates social distancing. Also, as the pandemic is expected to stay for a while, there is a need for an alternate diagnosis tool which overcomes these limitations, and is deployable at a large scale. The prominent symptoms of COVID-19 include cough and breathing difficulties. We foresee that respiratory sounds, when analyzed using machine learning techniques, can provide useful insights, enabling the design of a diagnostic tool. Towards this, the paper presents an early effort in creating (and analyzing) a database, called Coswara, of respiratory sounds, namely, cough, breath, and voice. The sound samples are collected via worldwide crowdsourcing using a website application. The curated dataset is released as open access. As the pandemic is evolving, the data collection and analysis is a work in progress. We believe that insights from analysis of Coswara can be effective in enabling sound based technology solutions for point-of-care diagnosis of respiratory infection, and in the near future this can help to diagnose COVID-19.

title: A Conceptual Framework for the Evaluation of Emergency Risk Communications.
abstract: To articulate a conceptual framework in support of evaluation activities in emergency risk communications (ERC).  The framework proposed is based on a systematic review of the scientific literature (2001-2016) combined with data derived from a series of semistructured interviews with experts and practitioners in ERC, and it is designed to support local, national, and international public health organizations in implementing evaluation studies in ERC.  We identified a list of ERC outcomes from the full-text review of 152 articles and categorized these into 3 groups, depending upon the level at which the outcome was measured: (1) information environment, (2) population, and (3) public health system. We analyzed interviewees' data from 18 interviews to identify practices and processes related to the effectiveness of ERC and included these as key structural components and processes in the developed evaluation framework.  Researchers and public health practitioners interested in the evaluation of ERC can use the conceptual framework described in this article to guide the development of evaluation studies and methods for assessing communication outcomes related to public health emergencies.

title: Candidate drugs against SARS-CoV-2 and COVID-19
abstract: Outbreak and pandemic of coronavirus SARS-CoV-2 in 2019/2020 will challenge global health for the future. Because a vaccine against the virus will not be available in the near future, we herein try to offer a pharmacological strategy to combat the virus. There exists a number of candidate drugs that may inhibit infection with and replication of SARS-CoV-2. Such drugs comprise inhibitors of TMPRSS2 serine protease and inhibitors of angiotensin-converting enzyme 2 (ACE2). Blockade of ACE2, the host cell receptor for the S protein of SARS-CoV-2 and inhibition of TMPRSS2, which is required for S protein priming may prevent cell entry of SARS-CoV-2. Further, chloroquine and hydroxychloroquine, and off-label antiviral drugs, such as the nucleotide analogue remdesivir, HIV protease inhibitors lopinavir and ritonavir, broad-spectrum antiviral drugs arbidol and favipiravir as well as antiviral phytochemicals available to date may prevent spread of SARS-CoV-2 and morbidity and mortality of COVID-19 pandemic.

title: Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis
abstract: Understanding the temporal dynamics of COVID-19 patient phenotypes is necessary to derive fine-grained resolution of pathophysiology. Here we use state-of-the-art deep neural networks over an institution-wide machine intelligence platform for the augmented curation of 15.8 million clinical notes from 30,494 patients subjected to COVID-19 PCR diagnostic testing. By contrasting the Electronic Health Record (EHR)-derived clinical phenotypes of COVID-19-positive (COVIDpos, n=635) versus COVID-19-negative (COVIDneg, n=29,859) patients over each day of the week preceding the PCR testing date, we identify anosmia/dysgeusia (37.4-fold), myalgia/arthralgia (2.6-fold), diarrhea (2.2-fold), fever/chills (2.1-fold), respiratory difficulty (1.9-fold), and cough (1.8-fold) as significantly amplified in COVIDpos over COVIDneg patients. The specific combination of cough and diarrhea has a 3.2-fold amplification in COVIDpos patients during the week prior to PCR testing, and along with anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19 (4-7 days prior to typical PCR testing date). This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional knowledge captured in EHRs. The platform holds tremendous potential for scaling up curation throughput, with minimal need for retraining underlying neural networks, thus promising EHR-powered early diagnosis for a broad spectrum of diseases.

title: Coronavirus research before 2020 is more relevant than ever, especially when interpreted for COVID-19
abstract: The speed with which biomedical researchers were able to identify and characterise COVID-19 was clearly due to prior research with other coronaviruses. Early epidemiological comparisons with two previous coronaviruses, Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), also made it easier to predict COVID-19's likely spread and lethality. This article assesses whether academic interest in prior coronavirus research has translated into interest in the primary source material, using Mendeley reader counts for early academic impact evidence. The results confirm that SARS and MERS research 2008-2017 experienced anomalously high increases in Mendeley readers in April-May 2020. Nevertheless, studies learning COVID-19 lessons from SARS and MERS or using them as a benchmark for COVID-19 have generated much more academic interest than primary studies of SARS or MERS. Thus, research that interprets prior relevant research for new diseases when they are discovered seems to be particularly important to help researchers to understand its implications in the new context.

title: COVID-19 Image Data Collection: Prospective Predictions Are the Future
abstract: Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main imaging tools, chest X-rays (CXRs) are common, fast, non-invasive, relatively cheap, and potentially bedside to monitor the progression of the disease. This paper describes the first public COVID-19 image data collection as well as a preliminary exploration of possible use cases for the data. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. It was manually aggregated from publication figures as well as various web based repositories into a machine learning (ML) friendly format with accompanying dataloader code. We collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. We present multiple possible use cases for the data such as predicting the need for the ICU, predicting patient survival, and understanding a patient's trajectory during treatment. Data can be accessed here: https://github.com/ieee8023/covid-chestxray-dataset

title: Exploration and Discovery of the COVID-19 Literature through Semantic Visualization
abstract: We are developing semantic visualization techniques in order to enhance exploration and enable discovery over large datasets of complex networks of relations. Semantic visualization is a method of enabling exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. This involves (i) NLP to extract named entities, relations and knowledge graphs from the original data; (ii) indexing the output and creating representations for all relevant entities and relations that can be visualized in many different ways, e.g., as tag clouds, heat maps, graphs, etc.; (iii) applying parameter reduction operations to the extracted relations, creating"relation containers", or functional entities that can also be visualized using the same methods, allowing the visualization of multiple relations, partial pathways, and exploration across multiple dimensions. Our hope is that this will enable the discovery of novel inferences over relations in complex data that otherwise would go unnoticed. We have applied this to analysis of the recently released CORD-19 dataset.

title: COVID-19: Real-time dissemination of scientific information to fight a public health emergency of international concern.
abstract: Rapidly sharing scientific information is an effective way to reduce public panic about COVID-19, and doing so is the key to providing real-time guidance to epidemiologists working to contain the outbreak, clinicians managing patients, and modelers helping to understand future developments and the possible effectiveness of various interventions. This issue has rapidly reviewed and published articles describing COVID-19, including the drug treatment options for SARS-CoV-2, its clinical characteristics, and therapies involving a combination of Chinese and Western medicine, the efficacy of chloroquine phosphate in the treatment of COVID-19 associated pneumonia according to clinical studies, and reflections on the system of reserve medical supplies for public health emergencies. As an academic journal, we will continue to quickly and transparently share data with frontline healthcare workers who need to know the epidemiological and clinical features of COVID-19.

title: A Systematic Review of COVID-19 Epidemiology Based on Current Evidence
abstract: As the novel coronavirus (SARS-CoV-2) continues to spread rapidly across the globe, we aimed to identify and summarize the existing evidence on epidemiological characteristics of SARS-CoV-2 and the effectiveness of control measures to inform policymakers and leaders in formulating management guidelines, and to provide directions for future research. We conducted a systematic review of the published literature and preprints on the coronavirus disease (COVID-19) outbreak following predefined eligibility criteria. Of 317 research articles generated from our initial search on PubMed and preprint archives on 21 February 2020, 41 met our inclusion criteria and were included in the review. Current evidence suggests that it takes about 3-7 days for the epidemic to double in size. Of 21 estimates for the basic reproduction number ranging from 1.9 to 6.5, 13 were between 2.0 and 3.0. The incubation period was estimated to be 4-6 days, whereas the serial interval was estimated to be 4-8 days. Though the true case fatality risk is yet unknown, current model-based estimates ranged from 0.3% to 1.4% for outside China. There is an urgent need for rigorous research focusing on the mitigation efforts to minimize the impact on society.

title: Scrutinising the COVID-19 data on 590.000 cases. A retrospective, population-based descriptive study for data quality surveillance and a review at 4.540.000 cases.
abstract: Reports on the detected positive patients with COVID-19 are as per today the best estimation of a country spread of the pandemic. In order to evaluate the early indicators for true lethality and recovery time, the data where the model is built must be quality checked. Each country sets different procedures and criteria for fatality count due to COVID-19 and the health system is stressed by having insufficient testing, untracked patients and premature discharge. In this paper the dynamics behind such data quality issues are discussed throughout the disease course to support better modeling and decision-making processes in a stressed healthcare system. From 593.291 cases in the sample, and its 7 representative groups, the recovery time and the local CFR are negatively correlated, having the highest fatality rates (21%, Spain) the countries with shorter recovery time (11 days, Spain). Also, CFR can be an indicator of Infection inconsistencies (i.e. South Korea, CFR 1%, Time to recovery 25 days). At the review part, focus is made on the inconsistencies detected in Germany and South Korea datasets as well as the potential misfits on China and Spain. Overall, the Time to Fatality ranges between 4 and 8 days, and the mean is of 6 days (South Korea, 7 days; Japan, 6days). Only Germany and France are detecting earlier than other countries and admit 10 days before fatality occurs. One simple explanation for the local CFR and Recovery time correlation is to define such rate as a measure of the healthcare system overload. Anomalous CFR indexes point to a stressed healthcare system. The higher the overload, the more focus on critical cases and hence the higher local CFR. The COVID-19 intrinsic CFR is unlikely to change by a factor of 10x from countries with similar lifestyle, GDP per capita and health services (i.e. the Mediterranean Basin, Northern Europe, etc.). Because of this fact, early CFR measured before Healthcare system overwhelming (COVID-19 free flow) are considered to be more accurate than the measured CFR while the outbreak is still ongoing, Finally, the synthetic Infection indexes may be a helpful indirect measure of the real population infection rate and also used for data quality audit. Any model built upon inconsistent data will be complex to explain and justify.

title: Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study
abstract: Concerns regarding potential neurological complications of COVID-19 are being increasingly reported, primarily in small series. Larger studies have been limited by both geography and specialty. Comprehensive characterisation of clinical syndromes is crucial to allow rational selection and evaluation of potential therapies. The aim of this study was to investigate the breadth of complications of COVID-19 across the UK that affected the brain.  During the exponential phase of the pandemic, we developed an online network of secure rapid-response case report notification portals across the spectrum of major UK neuroscience bodies, comprising the Association of British Neurologists (ABN), the British Association of Stroke Physicians (BASP), and the Royal College of Psychiatrists (RCPsych), and representing neurology, stroke, psychiatry, and intensive care. Broad clinical syndromes associated with COVID-19 were classified as a cerebrovascular event (defined as an acute ischaemic, haemorrhagic, or thrombotic vascular event involving the brain parenchyma or subarachnoid space), altered mental status (defined as an acute alteration in personality, behaviour, cognition, or consciousness), peripheral neurology (defined as involving nerve roots, peripheral nerves, neuromuscular junction, or muscle), or other (with free text boxes for those not meeting these syndromic presentations). Physicians were encouraged to report cases prospectively and we permitted recent cases to be notified retrospectively when assigned a confirmed date of admission or initial clinical assessment, allowing identification of cases that occurred before notification portals were available. Data collected were compared with the geographical, demographic, and temporal presentation of overall cases of COVID-19 as reported by UK Government public health bodies.  The ABN portal was launched on April 2, 2020, the BASP portal on April 3, 2020, and the RCPsych portal on April 21, 2020. Data lock for this report was on April 26, 2020. During this period, the platforms received notification of 153 unique cases that met the clinical case definitions by clinicians in the UK, with an exponential growth in reported cases that was similar to overall COVID-19 data from UK Government public health bodies. Median patient age was 71 years (range 23–94; IQR 58–79). Complete clinical datasets were available for 125 (82%) of 153 patients. 77 (62%) of 125 patients presented with a cerebrovascular event, of whom 57 (74%) had an ischaemic stroke, nine (12%) an intracerebral haemorrhage, and one (1%) CNS vasculitis. 39 (31%) of 125 patients presented with altered mental status, comprising nine (23%) patients with unspecified encephalopathy and seven (18%) patients with encephalitis. The remaining 23 (59%) patients with altered mental status fulfilled the clinical case definitions for psychiatric diagnoses as classified by the notifying psychiatrist or neuropsychiatrist, and 21 (92%) of these were new diagnoses. Ten (43%) of 23 patients with neuropsychiatric disorders had new-onset psychosis, six (26%) had a neurocognitive (dementia-like) syndrome, and four (17%) had an affective disorder. 18 (49%) of 37 patients with altered mental status were younger than 60 years and 19 (51%) were older than 60 years, whereas 13 (18%) of 74 patients with cerebrovascular events were younger than 60 years versus 61 (82%) patients older than 60 years.  To our knowledge, this is the first nationwide, cross-specialty surveillance study of acute neurological and psychiatric complications of COVID-19. Altered mental status was the second most common presentation, comprising encephalopathy or encephalitis and primary psychiatric diagnoses, often occurring in younger patients. This study provides valuable and timely data that are urgently needed by clinicians, researchers, and funders to inform immediate steps in COVID-19 neuroscience research and health policy.

title: The impact of ethnicity on clinical outcomes in COVID-19: A systematic review
abstract: The relationship between ethnicity and COVID-19 is uncertain. We performed a systematic review to assess whether ethnicity has been reported in patients with COVID-19 and its relation to clinical outcomes.  We searched EMBASE, MEDLINE, Cochrane Library and PROSPERO for English-language citations on ethnicity and COVID-19 (1(st) December 2019-15(th) May 2020). We also reviewed: COVID-19 articles in NEJM, Lancet, BMJ, JAMA, clinical trial protocols, grey literature, surveillance data and  articles on COVID-19 in MedRxiv to evaluate if the association between ethnicity and clinical outcomes were reported and what they showed. PROSPERO:180654.  Of 207 articles in the database search, five reported ethnicity; two reported no association between ethnicity and mortality. Of 690 articles identified from medical journals, 12 reported ethnicity; three reported no association between ethnicity and mortality. Of 209 preprints, 34 reported ethnicity – 13 found Black, Asian and Minority Ethnic (BAME) individuals had an increased risk of infection with SARS-CoV-2 and 12 reported worse clinical outcomes, including ITU admission and mortality, in BAME patients compared to White patients. Of 12 grey literature reports, seven with original data reported poorer clinical outcomes in BAME groups compared to White groups.  Data on ethnicity in patients with COVID-19 in the published medical literature remains limited. However, emerging data from the grey literature and  articles suggest BAME individuals are at an increased risk of acquiring SARS-CoV-2 infection compared to White individuals and also worse clinical outcomes from COVID-19. Further work on the role of ethnicity in the current pandemic is of urgent public health importance.

title: Aggregated mobility data could help fight COVID-19.

title: Features, Evaluation and Treatment Coronavirus (COVID-19)
abstract: According to the World Health Organization (WHO), viral diseases continue to emerge and represent a serious issue to public health In the last twenty years, several viral epidemics such as the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 to 2003, and H1N1 influenza in 2009, have been recorded Most recently, the Middle East respiratory syndrome coronavirus (MERS-CoV) was first identified in Saudi Arabia in 2012 In a timeline that reaches the present day, an epidemic of cases with unexplained low respiratory infections detected in Wuhan, the largest metropolitan area in China's Hubei province, was first reported to the WHO Country Office in China, on December 31, 2019 Published literature can trace the beginning of symptomatic individuals back to the beginning of December 2019 As they were unable to identify the causative agent, these first cases were classified as "pneumonia of unknown etiology " The Chinese Center for Disease Control and Prevention (CDC) and local CDCs organized an intensive outbreak investigation program The etiology of this illness is now attributed to a novel virus belonging to the coronavirus (CoV) family, COVID-19 On February 11, 2020, the WHO Director-General, Dr Tedros Adhanom Ghebreyesus, announced that the disease caused by this new CoV was a "COVID-19," which is the acronym of "coronavirus disease 2019" In the past twenty years, two additional coronavirus epidemics have occurred SARS-CoV provoked a large-scale epidemic beginning in China and involving two dozen countries with approximately 8000 cases and 800 deaths, and the MERS-CoV that began in Saudi Arabia and has approximately 2,500 cases and 800 deaths and still causes as sporadic cases This new virus seems to be very contagious and has quickly spread globally In a meeting on January 30, 2020, per the International Health Regulations (IHR, 2005), the outbreak was declared by the WHO a Public Health Emergency of International Concern (PHEIC) as it had spread to 18 countries with four countries reporting human-to-human transmission An additional landmark occurred on February 26, 2020, as the first case of the disease, not imported from China, was recorded in the United States Initially, the new virus was called 2019-nCoV Subsequently, the task of experts of the International Committee on Taxonomy of Viruses (ICTV) termed it the SARS-CoV-2 virus as it is very similar to the one that caused the SARS outbreak (SARS-CoVs) The CoVs have become the major pathogens of emerging respiratory disease outbreaks They are a large family of single-stranded RNA viruses (+ssRNA) that can be isolated in different animal species For reasons yet to be explained, these viruses can cross species barriers and can cause, in humans, illness ranging from the common cold to more severe diseases such as MERS and SARS Interestingly, these latter viruses have probably originated from bats and then moving into other mammalian hosts — the Himalayan palm civet for SARS-CoV, and the dromedary camel for MERS-CoV — before jumping to humans The dynamics of SARS-Cov-2 are currently unknown, but there is speculation that it also has an animal origin The potential for these viruses to grow to become a pandemic worldwide seems to be a serious public health risk Concerning COVID-19, the WHO raised the threat to the CoV epidemic to the "very high" level, on February 28, 2020 Probably, the effects of the epidemic caused by the new CoV has yet to emerge as the situation is quickly evolving World governments are at work to establish countermeasures to stem possible devastating effects Health organizations coordinate information flows and issues directives and guidelines to best mitigate the impact of the threat At the same time, scientists around the world work tirelessly, and information about the transmission mechanisms, the clinical spectrum of disease, new diagnostics, and prevention and therapeutic strategies are rapidly developing Many uncertainties remain with regard to both the virus-host interac ion and the evolution of the epidemic, with specific reference to the times when the epidemic will reach its peak At the moment, the therapeutic strategies to deal with the infection are only supportive, and prevention aimed at reducing transmission in the community is our best weapon Aggressive isolation measures in China have led to a progressive reduction of cases in the last few days In Italy, in geographic regions of the north of the peninsula, political and health authorities are making incredible efforts to contain a shock wave that is severely testing the health system In the midst of the crisis, the authors have chosen to use the "Statpearls" platform because, within the PubMed scenario, it represents a unique tool that may allow them to make updates in real-time The aim, therefore, is to collect information and scientific evidence and to provide an overview of the topic that will be continuously updated

title: Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
abstract: The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such major viral outbreaks demand early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 virus genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 virus genomes. The proposed method combines supervised machine learning with digital signal processing (MLDSP) for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp, including the 29 COVID-19 virus sequences available on January 27, 2020. Our results support a hypothesis of a bat origin and classify the COVID-19 virus as Sarbecovirus, within Betacoronavirus. Our method achieves 100% accurate classification of the COVID-19 virus sequences, and discovers the most relevant relationships among over 5000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

title: Coronavirus disease 2019 in pregnant women: a report based on 116 cases
abstract: The coronavirus disease 2019, caused by severe acute respiratory syndrome coronavirus 2, is a global public health emergency. Data on the effect of coronavirus disease 2019 in pregnancy are limited to small case series.  To evaluate the clinical characteristics and outcomes in pregnancy and the vertical transmission potential of severe acute respiratory syndrome coronavirus 2 infection.  Clinical records were retrospectively reviewed for 116 pregnant women with coronavirus disease 2019 pneumonia from 25 hospitals in China between January 20, 2020, and March 24, 2020. Evidence of vertical transmission was assessed by testing for severe acute respiratory syndrome coronavirus 2 in amniotic fluid, cord blood, and neonatal pharyngeal swab samples.  The median gestational age on admission was 38+0 (interquartile range, 36+0-39+1) weeks. The most common symptoms were fever (50.9%, 59/116) and cough (28.4%, 33/116); 23.3% (27/116) patients presented without symptoms. Abnormal radiologic findings were found in 96.3% (104/108) of cases. Of the 116 cases, there were 8 cases (6.9%) of severe pneumonia but no maternal deaths. One of 8 patients who presented in the first trimester and early second trimester had a missed spontaneous abortion. Of 99 patients, 21 (21.2%) who delivered had preterm birth, including 6 with preterm premature rupture of membranes. The rate of spontaneous preterm birth before 37 weeks' gestation was 6.1% (6/99). One case of severe neonatal asphyxia resulted in neonatal death. Furthermore, 86 of the 100 neonates tested for severe acute respiratory syndrome coronavirus 2 had negative results; of these, paired amniotic fluid and cord blood samples from 10 neonates used to test for severe acute respiratory syndrome coronavirus 2 had negative results.  Severe acute respiratory syndrome coronavirus 2 infection during pregnancy is not associated with an increased risk of spontaneous abortion and spontaneous preterm birth. There is no evidence of vertical transmission of severe acute respiratory syndrome coronavirus 2 infection when the infection manifests during the third trimester of pregnancy.

title: Policy and Science for Global Health Security: Shaping the Course of International Health
abstract: The global burden of infectious diseases and the increased attention to natural, accidental, and deliberate biological threats has resulted in significant investment in infectious disease research. Translating the results of these studies to inform prevention, detection, and response efforts often can be challenging, especially if prior relationships and communications have not been established with decision-makers. Whatever scientific information is shared with decision-makers before, during, and after public health emergencies is highly dependent on the individuals or organizations who are communicating with policy-makers. This article briefly describes the landscape of stakeholders involved in information-sharing before and during emergencies. We identify critical gaps in translation of scientific expertise and results, and biosafety and biosecurity measures to public health policy and practice with a focus on One Health and zoonotic diseases. Finally, we conclude by exploring ways of improving communication and funding, both of which help to address the identified gaps. By leveraging existing scientific information (from both the natural and social sciences) in the public health decision-making process, large-scale outbreaks may be averted even in low-income countries.

title: Infants Born to Mothers With a New Coronavirus (COVID-19)
abstract: A novel viral respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is responsible for an epidemic of the coronavirus disease 2019 (COVID-19) in cases in China and worldwide. Four full-term, singleton infants were born to pregnant women who tested positive for COVID-19 in the city of Wuhan, the capital of Hubei province, China, where the disease was first identified. Of the three infants, for who consent to be diagnostically tested was provided, none tested positive for the virus. None of the infants developed serious clinical symptoms such as fever, cough, diarrhea, or abnormal radiologic or hematologic evidence, and all four infants were alive at the time of hospital discharge. Two infants had rashes of unknown etiology at birth, and one had facial ulcerations. One infant had tachypnea and was supported by non-invasive mechanical ventilation for 3 days. One had rashes at birth but was discharged without parental consent for a diagnostic test. This case report describes the clinical course of four live born infants, born to pregnant women with the COVID-19 infection.

title: COVID-19 Diagnostic and Management Protocol for Pediatric Patients
abstract: This review aims to verify the main epidemiologic, clinical, laboratory-related, and therapeutic aspects of coronavirus disease 2019 (COVID-19) in critically ill pediatric patients. An extensive review of the medical literature on COVID-19 was performed, mainly focusing on the critical care of pediatric patients, considering expert opinions and recent reports related to this new disease. Experts from a large Brazilian public university analyzed all recently published material to produce a report aiming to standardize the care of critically ill children and adolescents. The report emphasizes on the clinical presentations of the disease and ventilatory support in pediatric patients with COVID-19. It establishes a flowchart to guide health practitioners on triaging critical cases. COVID-19 is essentially an unknown clinical condition for the majority of pediatric intensive care professionals. Guidelines developed by experts can help all practitioners standardize their attitudes and improve the treatment of COVID-19.

title: VirusDIP: Virus Data Integration Platform
abstract: The Coronavirus Disease 2019 (COVID-19) pandemic poses a huge threat to human public health. Viral sequence data plays an important role in the scientific prevention and control of epidemics. A comprehensive virus database will be vital useful for virus data retrieval and deep analysis. To promote sharing of virus data, several virus databases and related analyzing tools have been created.  To facilitate virus research and promote the global sharing of virus data, we present here VirusDIP, a one-stop service platform for archive, integration, access, analysis of virus data. It accepts the submission of viral sequence data from all over the world and currently integrates data resources from the National GeneBank Database (CNGBdb), Global initiative on sharing all influenza data (GISAID), and National Center for Biotechnology Information (NCBI). Moreover, based on the comprehensive data resources, BLAST sequence alignment tool and multi-party security computing tools are deployed for multi-sequence alignment, phylogenetic tree building and global trusted sharing. VirusDIP is gradually establishing cooperation with more databases, and paving the way for the analysis of virus origin and evolution. All public data in VirusDIP are freely available for all researchers worldwide. Availability https://db.cngb.org/virus/ Contact weixiaofeng@cngb.org

title: When Darkness Becomes a Ray of Light in the Dark Times: Understanding the COVID-19 via the Comparative Analysis of the Dark Proteomes of SARS-CoV-2, Human SARS and Bat SARS-Like Coronaviruses
abstract: Recently emerged coronavirus designated as SARS-CoV-2 (also known as 2019 novel coronavirus (2019-nCoV) or Wuhan coronavirus) is a causative agent of coronavirus disease 2019 (COVID-19), which is rapidly spreading throughout the world now. More than 9,00,000 cases of SARS-CoV-2 infection and more than 47,000 COVID-19-associated mortalities have been reported worldwide till the writing of this article, and these numbers are increasing every passing hour. World Health Organization (WHO) has declared the SARS-CoV-2 spread as a global public health emergency and admitted that the COVID-19 is a pandemic now. The multiple sequence alignment data correlated with the already published reports on the SARS-CoV-2 evolution and indicated that this virus is closely related to the bat Severe Acute Respiratory Syndrome-like coronavirus (bat SARS-like CoV) and the well-studied Human SARS coronavirus (SARS CoV). The disordered regions in viral proteins are associated with the viral infectivity and pathogenicity. Therefore, in this study, we have exploited a set of complementary computational approaches to examine the dark proteomes of SARS-CoV-2, bat SARS-like, and human SARS CoVs by analysing the prevalence of intrinsic disorder in their proteins. According to our findings, SARS-CoV-2 proteome contains very significant levels of structural order. In fact, except for Nucleocapsid, Nsp8, and ORF6, the vast majority of SARS-CoV-2 proteins are mostly ordered proteins containing less intrinsically disordered protein regions (IDPRs). However, IDPRs found in SARS-CoV-2 proteins are functionally important. For example, cleavage sites in its replicase 1ab polyprotein are found to be highly disordered, and almost all SARS-CoV-2 proteins were shown to contain molecular recognition features (MoRFs), which are intrinsic disorder-based protein-protein interaction sites that are commonly utilized by proteins for interaction with specific partners. The results of our extensive investigation of the dark side of the SARS-CoV-2 proteome will have important implications for the structural and non-structural biology of SARS or SARS-like coronaviruses.  The infection caused by a novel coronavirus (SARS-CoV-2) that causes severe respiratory disease with pneumonia-like symptoms in humans is responsible for the current COVID-19 pandemic. No in-depth information on structures and functions of SARS-CoV-2 proteins is currently available in the public domain, and no effective anti-viral drugs and/or vaccines are designed for the treatment of this infection. Our study provides the first comparative analysis of the order- and disorder-based features of the SARS-CoV-2 proteome relative to human SARS and bat CoV that may be useful for structure-based drug discovery.

title: Prevention is Better than Cure: An Application of Big Data and Geospatial Technology in Mitigating Pandemic
abstract: COVID-19 has been a severe threat to city’s dense ecosystem and has collapsed the urban life. These spread has raised question on urban health infrastructure and coping capacity. To maintain social distance and providing community with essentials is a challenging task for government and city planners in dense urban area. Spatial mapping of resources, patients, medical infrastructure, potential open space, places which can be turned to quarantine centers, etc. has been done using GIS for city of Surat. Larger open spaces within the urban fabric are identified to implement emergency services and evacuation protocols. This tool will defiantly be helpful to the city administration in maintaining the cluster, quarantine tracking of the infected community and flow of daily essential, supply chain in cities with restriction in the infected cluster. Even having better technology to fight against any pandemic, geospatial technology will help to prevent the pandemic to spread further and hence, it is known as prevention is better then cure.

title: Early data on the performance of a combined SARS-CoV-2 spike-nucleocapsid antibody lateral flow device compared to a nucleocapsid-only device
abstract: There is a critical need for reliable antibody detection methods in order to study and evaluate the public health and clinical response to the ongoing COVID-19 pandemic. Lateral flow immunoassay (LFIA) devices offer the prospect of rapid point-of-care testing (POCT), but the performance of these devices must be evaluated for robustness before they can be adopted for routine clinical and public health use.  Plasma and serum specimens from SARS-CoV-2 RNA-positive (n = 131) and RNA-negative (n = 16) patients were taken from various time points with respect to the onset of symptoms. All 147 anonymised specimens were tested for IgM and IgG using the Hangzhou AllTest 2019-nCoV IgG/IgM Rapid Test Cassette and the Abbexa COVID-19 IgG/IgM Rapid Test Kit.  IgM sensitivity ranged from 13% to 68%, depending on the date of symptom onset and the device. Regarding IgG, the Abbexa device outperformed the Hangzhou device at all cumulative timeline brackets, with sensitivity of 97·87% (Abbexa) versus 68·09% (Hangzhou) for samples beyond 21 days from symptom onset. Day 21 was therefore chosen as the cut-off for ascertaining test performance characteristics, beyond which the specificity was 100% for both devices and negative predictive value was 0·94 (Abbexa) versus 0·50 (Hangzhou).  Based on this limited dataset, the performance characteristics of the Abbexa LFIA device were substantially better than those of the Hangzhou device. Applying a 21-day cut-off for the Abbexa device meets the minimum (98%) sensitivity and specificity thresholds set by the UK Medicine & Healthcare products Regulatory Agency. The Abbexa device captures antibodies against both SARS-CoV-2 spike and nucleocapsid proteins, as opposed to Hangzhou that targets only the nucleocapsid protein. We therefore propose that spike glycoprotein antibodies be considered as part of the standard diagnostic approach towards SARS-CoV-2 antibody profiling to improve clinical sensitivity and potentially specificity, pending follow-up studies to confirm this approach.

title: Shortlisting SARS‐CoV‐2 Peptides for Targeted Studies from Experimental Data‐Dependent Acquisition Tandem Mass Spectrometry Data
abstract: Detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a crucial tool for fighting the COVID‐19 pandemic. This dataset brief presents the exploration of a shotgun proteomics dataset acquired on SARS‐CoV‐2 infected Vero cells. Proteins from inactivated virus samples were extracted, digested with trypsin, and the resulting peptides were identified by data‐dependent acquisition tandem mass spectrometry. The 101 peptides reporting for six viral proteins were specifically analyzed in terms of their analytical characteristics, species specificity and conservation, and their proneness to structural modifications. Based on these results, a shortlist of 14 peptides from the N, S, and M main structural proteins that could be used for targeted mass‐spectrometry method development and diagnostic of the new SARS‐CoV‐2 is proposed and the best candidates are commented. This article is protected by copyright. All rights reserved

title: There are asymptomatic and pre-symptomatic patients infected with COVID-19. So what? Pandemic response implications
abstract: Asymptomatic but infectious people have been reported in many infectious diseases. Asymptomatic and pre-symptomatic carriers would be a hidden reservoir of COVID-19.  This review identifies primary empirical evidence about the ability of asymptomatic carriers to infect others with COVID-19 pandemic and reflects on the implications for control measures.  A systematic review is followed by a narrative report and commentary inclusion criteria were: studies reporting primary data on asymptomatic or pre-symptomatic patients, who were considered to have passed on COVID-19 infection; and published in indexed journals or in peer review between January 1 and March 31, 2020.  Nine articles reported on 83 asymptomatic or pre-symptomatic persons.  The evidence confirms COVID-19 transmission from people who were asymptomatic at the time. A series of implications for health service response are laid out.  Covid-19, Asymptomatic, Pre-symptomatic, Public Health

title: Improved health and survival of FIV-infected cats is associated with the presence of autoantibodies to the primary receptor, CD134.
abstract: We analyzed antibody responses in sera from feline immunodeficiency virus (FIV)-infected and uninfected cats. A strong antiviral response to the viral surface glycoprotein (SU) was noted in both natural and experimental infections. In addition, 143 of 226 FIV-infected animals (63%) also expressed antibodies to the primary binding receptor, CD134, whereas cats infected with other feline RNA viruses, including calicivirus, coronavirus, herpesvirus, and feline leukemia virus, did not. Both affinity-purified anti-CD134 and anti-SU antibodies blocked FIV infection ex vivo. FACS analyses revealed that the anti-CD134 antibodies bound to a cryptic epitope on the receptor that was only exposed when SU bound to CD134. Anti-CD134 binding caused displacement of SU from the surface of the cell and inhibition of infection. The presence of antibodies to CD134 correlated with lower virus loads and a better overall health status in FIV(+) cats, whereas anti-SU antibodies were present independent of health status. The findings are consistent with a role for antireceptor antibodies in protection from virus spread and disease progression.

title: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks
abstract: On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.

title: A citizen science initiative for open data and visualization of COVID-19 outbreak in Kerala, India
abstract: India, the second most populated country in the world, reported its first COVID-19 case in the state of Kerala with a travel history from Wuhan. Subsequently, a surge of cases was observed in the state mainly through the individuals who traveled from Europe and the Middle East to Kerala, thus initiating an outbreak. Since public awareness through dissemination of reliable information plays a significant role in controlling the spread of the disease, the Department of Health Services, Government of Kerala initially released daily updates through daily textual bulletins. However, this unstructured data requires refinement and enrichment for upstream applications, such as visualization, and/or analysis. Here we reported a citizen science initiative that leveraged publicly available and crowd-verified data on COVID-19 outbreak in Kerala from the government bulletins, supplemented with the information from media outlets to generate reusable datasets. This data was further used to provide real-time analysis, and daily updates of COVID-19 cases in Kerala, through a user-friendly bilingual dashboard (https://covid19kerala.info/) for non-specialists. We ensured longevity and reusability of the dataset by depositing it in a public repository, aligning with open source principles for future analytical efforts. Finally, to show the scope of the sourced data, we also provided a snapshot of outbreak trends and demographic characteristics of the individuals affected with COVID-19 in Kerala during the first 99 days of the outbreak.

title: Protease inhibitors broadly effective against feline, ferret and mink coronaviruses
abstract: Ferret and mink coronaviruses typically cause catarrhal diarrhea in ferrets and minks, respectively. In recent years, however, systemic fatal coronavirus infection has emerged in ferrets, which resembles feline infectious peritonitis (FIP) in cats. FIP is a highly fatal systemic disease caused by a virulent feline coronavirus infection in cats. Despite the importance of coronavirus infections in these animals, there are no effective commercial vaccines or antiviral drugs available for these infections. We have previously reported the efficacy of a protease inhibitor in cats with FIP, demonstrating that a virally encoded 3C-like protease (3CLpro) is a valid target for antiviral drug development for coronavirus infections. In this study, we extended our previous work on coronavirus inhibitors and investigated the structure-activity relationships of a focused library of protease inhibitors for ferret and mink 3CLpro. Using the fluorescence resonance energy transfer assay, we identified potent inhibitors broadly effective against feline, ferret and mink coronavirus 3CLpro. Multiple amino acid sequence analysis and modelling of 3CLpro of ferret and mink coronaviruses were conducted to probe the structural basis for these findings. The results of this study provide support for further research to develop broad-spectrum antiviral agents for multiple coronavirus infections. To the best of our knowledge, this is the first report on small molecule inhibitors of ferret and mink coronaviruses.

title: QAnswer KG: Designing a Portable Question Answering System over RDF Data
abstract: While RDF was designed to make data easily readable by machines, it does not make data easily usable by end-users.  Answering (QA) over Knowledge Graphs (KGs) is seen as the technology which is able to bridge this gap. It aims to build systems which are capable of extracting the answer to a user’s natural language question from an RDF dataset. In recent years, many approaches were proposed which tackle the problem of QA over KGs. Despite such efforts, it is hard and cumbersome to create a Question Answering system on top of a new RDF dataset. The main open challenge remains portability, i.e., the possibility to apply a QA algorithm easily on new and previously untested RDF datasets. In this publication, we address the problem of portability by presenting an architecture for a portable QA system. We present a novel approach called QAnswer KG, which allows the construction of on-demand QA systems over new RDF datasets. Hence, our approach addresses non-expert users in QA domain. In this paper, we provide the details of QA system generation process. We show that it is possible to build a QA system over any RDF dataset while requiring minimal investments in terms of training. We run experiments using 3 different datasets. To the best of our knowledge, we are the first to design a process for non-expert users. We enable such users to efficiently create an on-demand, scalable, multilingual, QA system on top of any RDF dataset.

title: Missense variants in ACE2 are predicted to encourage and inhibit interaction with SARS-CoV-2 Spike and contribute to genetic risk in COVID-19
abstract: SARS-CoV-2 invades host cells via an endocytic pathway that begins with the interaction of the SARS-CoV-2 Spike glycoprotein (S-protein) and human Angiotensin-converting enzyme 2 (ACE2). Genetic variability in ACE2 may be one factor that mediates the broad-spectrum severity of SARS-CoV-2 infection and COVID-19 outcomes. We investigated the capacity of ACE2 variation to influence SARS-CoV-2 infection with a focus on predicting the effect of missense variants on the ACE2 SARS-CoV-2 S-protein interaction. We validated the mCSM-PPI2 variant effect prediction algorithm with 26 published ACE2 mutant SARS-CoV S-protein binding assays and found it performed well in this closely related system (True Positive Rate = 0.7, True Negative Rate = 1). Application of mCSM-PPI2 to ACE2 missense variants from the Genome Aggregation Consortium Database (gnomAD) identified three that are predicted to strongly inhibit or abolish the S-protein ACE2 interaction altogether (p.Glu37Lys, p.Gly352Val and p.Asp355Asn) and one that is predicted to promote the interaction (p.Gly326Glu). The S-protein ACE2 inhibitory variants are expected to confer a high degree of resistance to SARS-CoV-2 infection whilst the S-protein ACE2 affinity enhancing variant may lead to additional susceptibility and severity. We also performed in silico saturation mutagenesis of the S-protein ACE2 interface and identified a further 38 potential missense mutations that could strongly inhibit binding and one more that is likely to enhance binding (Thr27Arg). A conservative estimate places the prevalence of the strongly protective variants between 12-70 per 100,000 population but there is the possibility of higher prevalence in local populations or those underrepresented in gnomAD. The probable interplay between these ACE2 affinity variants and ACE2 expression polymorphisms is highlighted as well as gender differences in penetrance arising from ACE2’s situation on the X-chromosome. It is also described how our data can help power future genetic association studies of COVID-19 phenotypes and how the saturation mutant predictions can help design a mutant ACE2 with tailored S-protein affinity, which may be an improvement over a current recombinant ACE2 that is undergoing clinical trial. Key results 1 ACE2 gnomAD missense variant (p.Gly326Glu) and one unobserved missense mutation (Thr27Arg) are predicted to enhance ACE2 binding with SARS-CoV-2 Spike protein, which could result in increased susceptibility and severity of COVID-19 3 ACE2 missense variants in gnomAD plus another 38 unobserved missense mutations are predicted to inhibit Spike binding, these are expected to confer a high degree of resistance to infection The prevalence of the strongly protective variants is estimated between 12-70 per 100,000 population but higher prevalence may exist in local populations or those underrepresented in gnomAD A strategy to design a recombinant ACE2 with tailored affinity towards Spike and its potential therapeutic value is presented The predictions were extensively validated against published ACE2 mutant binding assays for SARS-CoV Spike protein

title: Current Understanding of COVID-19 Clinical Course and Investigational Treatments
abstract: Currently, there is no unified framework linking disease progression to established viral levels, clinical tests, inflammatory markers, and investigational treatment options.  It may take many weeks or months to establish a standard treatment approach. Given the growing morbidity and mortality with respect to COVID-19, we present a treatment approach based on a thorough review of scholarly articles and clinical reports. Our focus is on staged progression, clinical algorithms, and individualized treatment.  We followed the protocol for a quality review article proposed by Heyn et.  A literature search was conducted to find all relevant studies related to COVID-19. The search was conducted between April 1, 2020 and April 13, 2020 using the following electronic databases: PubMed (1809 to present), Google Scholar (1900 to present), MEDLINE (1946 to present), CINAHL (1937 to present), and Embase (1980 to present). Keywords used included COVID-19, 2019-nCov, SARS-CoV-2, SARS-CoV, and MERS-CoV, with terms such as efficacy, seroconversion, microbiology, pathophysiology, viral levels, inflammation, survivability, and treatment and pharmacology. No language restriction was placed on the search. Reference lists were manually scanned for additional studies.  Of the articles found in the literature search, 70 were selected for inclusion in this study (67 cited in the body of the manuscript and 3 additional unique references in the Figures). The articles represent work from China, Japan, Taiwan, Vietnam, Rwanda, Israel, France, the United Kingdom, the Netherlands, Canada, and the United States. Most of the articles were cohort or case studies, but we also drew upon information found in guidelines from hospitals and clinics instructing their staff on procedures to follow. In addition, we based some decisions on data collected by agencies such as the CDC, FDA, IHME, ISDA, and Worldometer. None of the case studies or cohort studies used a large number of participants. The largest group of participants numbered less than 500 and some case studies had fewer than 30 patients. However, the review of the literature revealed the need for individualized treatment protocols due to the variability of patient clinical presentation and survivability. A number of factors appear to influence mortality: the stage at which the patient first presented for care, pre-existing health conditions, age, and the viral load the patient carried.  COVID-19 can be divided into three distinct Stages, beginning at the time of infection (Stage I), sometimes progressing to pulmonary involvement (Stage II, with or without hypoxemia) and less frequently to systemic inflammation (Stage III). In addition to modeling the stages of disease progression, we have also created a treatment algorithm which considers age, comorbidities, clinical presentation, and disease progression to suggest drug classes or treatment modalities. This paper presents the first evidence-based recommendations for individualized treatment for COVID-19.

title: A dynamic modeling tool for estimating healthcare demand from the COVID19 epidemic and evaluating population-wide interventions
abstract: Public health officials need tools to assist with anticipating the healthcare resources required to confront the SARS-COV-2 pandemic. We built a modeling tool to aid practicing public health officials with estimating healthcare demand from the pandemic in their jurisdictions and to evaluate the potential impacts of population-wide social-distancing interventions.  The tool uses a SEIR compartmental model to project the local spread of the pandemic. Users input case counts, healthcare resources, and select intervention strategies to evaluate. Outputs include the number of infections and deaths with and without intervention, and the demand for hospital and critical care beds and ventilators relative to existing capacity. We illustrate the tool using data from three regions of Chile.  Our scenarios indicate a surge in COVID-19 patients could overwhelm Chilean hospitals by June, peaking in July or August at 6 to 50 times the current supply of beds and ventilators. A lockdown strategy or combination of case isolation, home quarantine, social distancing of individuals greater than 70 years, and telework interventions may keep treatment demand below capacity.  Aggressive interventions can avert substantial morbidity and mortality from COVID-19. Our tool permits rapid evaluation of locally-applicable policy scenarios and updating of results as new data become available.

title: Intrinsic growth rules of patients infected, dead and recovered with 2019 novel coronavirus in mainland China
abstract: An outbreak of a novel coronavirus (SARS-CoV-2)-infected pneumonia (COVID-19) was first diagnosed in Wuhan, China, in December 2019 and then spread rapidly to other regions. We collected the time series data of the cumulative number of confirmed infected, dead, and cured cases from the health commissions in 31 provinces in mainland China. A descriptive model in a logistic form was formulated to infer the intrinsic epidemic rules of COVID-19, which illustrates robustness spatially and temporally. Our model is robust (R2>0.95) to depict the intrinsic growth rule for the cumulative number of confirmed infected, dead, and cured cases in 31 provinces in mainland China. Furthermore, we compared the intrinsic epidemic rules of COVID-19 in Hubei with that of severe acute respiratory syndrome (SARS) in Beijing, which was obtained from the Ministry of Public Health of China in 2003. We found that the infected case is the earliest to be saturated and has the lowest semi-saturation period compared with deaths and cured cases for both COVID-19 and SARS. All the three types of SARS cases are later to saturate and have longer semi-saturation period than that of COVID-19. Despite the virus caused SARS (SARS-CoV) and the virus caused COVID-19 (SARS-CoV-2) are homologous, the duration of the outbreak would be shorter for COVID-19.

title: The C-Terminal Tail of TRIM56 Dictates Antiviral Restriction of Influenza A and B Viruses by Impeding Viral RNA Synthesis.
abstract: Accumulating data suggest that tripartite-motif-containing (TRIM) proteins participate in host responses to viral infections, either by acting as direct antiviral restriction factors or through regulating innate immune signaling of the host. Of >70 TRIMs, TRIM56 is a restriction factor of several positive-strand RNA viruses, including three members of the family Flaviviridae(yellow fever virus, dengue virus, and bovine viral diarrhea virus) and a human coronavirus (OC43), and this ability invariably depends upon the E3 ligase activity of TRIM56. However, the impact of TRIM56 on negative-strand RNA viruses remains unclear. Here, we show that TRIM56 puts a check on replication of influenza A and B viruses in cell culture but does not inhibit Sendai virus or human metapneumovirus, two paramyxoviruses. Interestingly, the anti-influenza virus activity was independent of the E3 ligase activity, B-box, or coiled-coil domain. Rather, deletion of a 63-residue-long C-terminal-tail portion of TRIM56 abrogated the antiviral function. Moreover, expression of this short C-terminal segment curtailed the replication of influenza viruses as effectively as that of full-length TRIM56. Mechanistically, TRIM56 was found to specifically impede intracellular influenza virus RNA synthesis. Together, these data reveal a novel antiviral activity of TRIM56 against influenza A and B viruses and provide insights into the mechanism by which TRIM56 restricts these medically important orthomyxoviruses.  Options to treat influenza are limited, and drug-resistant influenza virus strains can emerge through minor genetic changes. Understanding novel virus-host interactions that alter influenza virus fitness may reveal new targets/approaches for therapeutic interventions. We show here that TRIM56, a tripartite-motif protein, is an intrinsic host restriction factor of influenza A and B viruses. Unlike its antiviral actions against positive-strand RNA viruses, the anti-influenza virus activity of TRIM56 was independent of the E3 ligase activity. Rather, expression of a short segment within the very C-terminal tail of TRIM56 inhibited the replication of influenza viruses as effectively as that of full-length TRIM56 by specifically targeting viral RNA synthesis. These data reveal the remarkable multifaceted activity of TRIM56, which has developed multiple domains to inhibit multiple viral families. They also raise the possibility of developing a broad-spectrum, TRIM56-based antiviral approach for addition to influenza prophylaxis and/or control strategies.

title: An Updated Systematic Review of the Therapeutic Role of Hydroxychloroquine in Coronavirus Disease-19 (COVID-19)
abstract: The world is currently experiencing the Coronavirus Disease-19 (COVID-19) pandemic. There is no approved drug for the definitive treatment of the disease. Various drugs are being tried for the treatment of COVID-19, including hydroxychloroquine (HCQ). This study was performed to systematically review the therapeutic role of HCQ in COVID-19 from the available literature.  PubMed, Embase, ClinicalTrials.gov, ICTRP (WHO), Cochrane Library databases, and two pre-print servers (medRxiv.org and Research Square) were searched for clinical studies that evaluated the therapeutic role of HCQ on COVID-19 until 10 May 2020. The available studies were critically analyzed and the data were extracted.  A total of 663 articles were screened and 12 clinical studies (seven peer-reviewed and published studies and five non-peer-reviewed studies from pre-print servers) with a total sample size of 3543 patients were included. Some of the clinical studies demonstrated good virological and clinical outcomes with HCQ alone or in combination with azithromycin in COVID-19 patients, although the studies had major methodological limitations. Some of the other studies showed negative results with HCQ therapy along with the risk of adverse reactions.  The results of efficacy and safety of HCQ in COVID-19, as obtained from the clinical studies, are not satisfactory, although many of these studies had major methodological limitations. Stronger evidence from well-designed robust randomized clinical trials is required before conclusively determining the role of HCQ in the treatment of COVID-19. Clinical prudence is required in advocating HCQ as a therapeutic armamentarium in COVID-19.

title: Global reporting of cases of COVID‐19 in psoriasis and atopic dermatitis: an opportunity to inform care during a pandemic
abstract: We wish to bring your attention to the PsoPROTECT (Psoriasis Patient Registry for Outcomes, Therapy and Epidemiology of Covid‐19 ) and SECURE‐AD (Surveillance Epidemiology of Coronavirus Under Research Exclusion‐Atopic Dermatitis) registries; two urgent global initiatives that address an unmet need for delineating the determinants of COVID‐19 outcomes in the common cutaneous immune‐mediated inflammatory diseases (IMIDs) psoriasis and atopic dermatitis.

title: Artificial intelligence and the future of global health
abstract: Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A series of fundamental questions have been raised about AI-driven health interventions, and whether the tools, methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but most use some form of machine learning or signal processing. Several types of machine learning methods are frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning. However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent, AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of developing and deploying these interventions might not be unique to these settings, the global health community will need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research agenda to facilitate equitable and ethical use.

title: Response to Commentary on “The neuroinvasive potential of SARS‐CoV‐2 may play a role in the respiratory failure of COVID‐19 patients”
abstract: In a recent review, we have suggested a neuroinvasive potential of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and its possible role in the causation of acute respiratory failure of coronavirus disease 2019 (COVID‐19) patients (J Med Viol doi: 10.1002/jmv.25728), based upon the clinical and experimental data available on the past SARS‐CoV‐1 and the recent SARS‐CoV‐2 pandemic. In this article, we provide new evidence recently reported regarding the neurotropic potential of SARS‐CoV‐2 and respond to several comments on our previously published article. In addition, we also discuss the peculiar manifestations of respiratory failure in COVID‐19 patients and the possible involvement of nervous system.

title: Consensus study of risk factors and symptoms of SARS-CoV-2 (COVID-19) using biomedical literature and social media data
abstract: In December 2019, Coronavirus disease 2019 (COVID-19) outbreak started in China and rapidly spread around the world. Lack of any vaccine or optimized intervention raised the importance of characterizing risk factors and symptoms for the early identification and successful treatment of COVID-19 patients.  We systematically integrated and analyzed published biomedical literature and public social media data to expand our landscape of clinical and demographic variables of COVID-19. Through semantic analysis, 45 retrospective cohort studies, which evaluated 303 clinical and demographic variables across 13 different outcomes of COVID-19, and 84,140 tweet posts from 1,036 COVID-19 positive users were collected. In total, 59 symptoms were identified across both datasets.  90% of clinical and demographic variables showed inconsistency across outcomes of COVID-19. From the consensus analysis, we identified clinical and demographic variables that were specific for individual outcomes of COVID-19. Also, 25 novel symptoms that have been not previously well characterized, but were mentioned in social media. Furthermore, we observed that there were certain combinations of symptoms that were frequently mentioned together among COVID-19 patients.  Identified outcome-specific clinical and demographic variables, symptoms, and combinations of symptoms may serve as surrogate indicators to identify COVID-19 patients and predict their clinical outcomes providing appropriate treatments.

title: COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter
abstract: In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. The largest improvements are on the target domain. Pretrained transformer models, such as CT-BERT, are trained on a specific target domain and can be used for a wide variety of natural language processing tasks, including classification, question-answering and chatbots. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.

title: Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells
abstract: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis.

title: COVID-19: more evidence emerges

title: Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks
abstract: From May to July 2015, there was a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea. MERS is caused by MERS-CoV, an enveloped, positive-sense, single-stranded RNA virus belonging to the family Coronaviridae. Despite expert opinions that the danger of MERS might be exaggerated, there was an overreaction by the public according to the Korean mass media, which led to a noticeable reduction in social and economic activities during the outbreak. To explain this phenomenon, we presumed that machine learning-based analysis of media outlets would be helpful and collected a number of Korean mass media articles and short-text comments produced during the 10-week outbreak. To process and analyze the collected data (over 86 million words in total) effectively, we created a methodology composed of machine-learning and information-theoretic approaches. Our proposal included techniques for extracting emotions from emoticons and Internet slang, which allowed us to significantly (approximately 73%) increase the number of emotion-bearing texts needed for robust sentiment analysis of social media. As a result, we discovered a plausible explanation for the public overreaction to MERS in terms of the interplay between the disease, mass media, and public emotions.

title: Advances in Visualization Tools for Phylogenomic and Phylodynamic Studies of Viral Diseases
abstract: Genomic and epidemiological monitoring have become an integral part of our response to emerging and ongoing epidemics of viral infectious diseases. Advances in high-throughput sequencing, including portable genomic sequencing at reduced costs and turnaround time, are paralleled by continuing developments in methodology to infer evolutionary histories (dynamics/patterns) and to identify factors driving viral spread in space and time. The traditionally static nature of visualizing phylogenetic trees that represent these evolutionary relationships/processes has also evolved, albeit perhaps at a slower rate. Advanced visualization tools with increased resolution assist in drawing conclusions from phylogenetic estimates and may even have potential to better inform public health and treatment decisions, but the design (and choice of what analyses are shown) is hindered by the complexity of information embedded within current phylogenetic models and the integration of available meta-data. In this review, we discuss visualization challenges for the interpretation and exploration of reconstructed histories of viral epidemics that arose from increasing volumes of sequence data and the wealth of additional data layers that can be integrated. We focus on solutions that address joint temporal and spatial visualization but also consider what the future may bring in terms of visualization and how this may become of value for the coming era of real-time digital pathogen surveillance, where actionable results and adequate intervention strategies need to be obtained within days.

title: COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
abstract: Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

title: Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
abstract: Cluster of viral pneumonia occurrences during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention, particularly when other chest imaging modalities are less available. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images. The evolution of viruses and the emergence of novel mutated viruses further result in substantial dataset shift, which greatly limits the performance of classification approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough or the confidence score estimated by the confidence prediction module is small enough, we accept the input as an anomaly case (i.e., viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to reinforce the one-class model. The proposed model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 18,619 non-viral pneumonia cases, and 18,774 healthy controls.

title: CoV-Seq: SARS-CoV-2 Genome Analysis and Visualization
abstract: Summary COVID-19 has become a global pandemic not long after its inception in late 2019. SARS-CoV-2 genomes are being sequenced and shared on public repositories at a fast pace. To keep up with these updates, scientists need to frequently refresh and reclean datasets, which is ad hoc and labor-intensive. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes. In order to address these challenges, we developed CoV-Seq, a webserver to enable simple and rapid analysis of SARS-CoV-2 genomes. Given a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are presented in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is also available for high-throughput processing. Availability and Implementation CoV-Seq is implemented in Python and Javascript. The webserver is available at http://covseq.baidu.com/ and the source code is available from https://github.com/boxiangliu/covseq. Contact jollier.liu@gmail.com Supplementary information Supplementary information are available at bioRxiv online.

title: COVID-19 in a tertiary hospital from Romania: Epidemiology, preparedness and clinical challenges

title: Bayesian Monitoring of Emerging Infectious Diseases
abstract: We define data analyses to monitor a change in R, the average number of secondary cases caused by a typical infected individual. The input dataset consists of incident cases partitioned into outbreaks, each initiated from a single index case. We split the input dataset into two successive subsets, to evaluate two successive R values, according to the Bayesian paradigm. We used the Bayes factor between the model with two different R values and that with a single R value to justify that the change in R is statistically significant. We validated our approach using simulated data, generated using known R. In particular, we found that claiming two distinct R values may depend significantly on the number of outbreaks. We then reanalyzed data previously studied by Jansen et al. [Jansen et al. Science 301 (5634), 804], concerning the effective reproduction number for measles in the UK, during 1995–2002. Our analyses showed that the 1995–2002 dataset should be divided into two separate subsets for the periods 1995–1998 and 1999–2002. In contrast, Jansen et al. take this splitting point as input of their analysis. Our estimated effective reproduction numbers R are in good agreement with those found by Jansen et al. In conclusion, our methodology for detecting temporal changes in R using outbreak-size data worked satisfactorily with both simulated and real-world data. The methodology may be used for updating R in real time, as surveillance outbreak data become available.

title: $\alpha$-Satellite: An AI-driven System and Benchmark Datasets for Hierarchical Community-level Risk Assessment to Help Combat COVID-19
abstract: The novel coronavirus and its deadly outbreak have posed grand challenges to human society: as of March 26, 2020, there have been 85,377 confirmed cases and 1,293 reported deaths in the United States; and the World Health Organization (WHO) characterized coronavirus disease (COVID-19) - which has infected more than 531,000 people with more than 24,000 deaths in at least 171 countries - a global pandemic. A growing number of areas reporting local sub-national community transmission would represent a significant turn for the worse in the battle against the novel coronavirus, which points to an urgent need for expanded surveillance so we can better understand the spread of COVID-19 and thus better respond with actionable strategies for community mitigation. By advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and real-time data generated from heterogeneous sources (e.g., disease related data from official public health organizations, demographic data, mobility data, and user geneated data from social media), in this work, we propose and develop an AI-driven system (named $\alpha$-Satellite}, as an initial offering, to provide hierarchical community-level risk assessment to assist with the development of strategies for combating the fast evolving COVID-19 pandemic. More specifically, given a specific location (either user input or automatic positioning), the developed system will automatically provide risk indexes associated with it in a hierarchical manner (e.g., state, county, city, specific location) to enable individuals to select appropriate actions for protection while minimizing disruptions to daily life to the extent possible. The developed system and the generated benchmark datasets have been made publicly accessible through our website. The system description and disclaimer are also available in our website.

title: Delayed prophylaxis with unfractionated heparin increases the risk of venous thromboembolic events in patients with moderate to severe traumatic brain injury: a retrospective analysis.
abstract: Venous thromboembolism (VTE) is a recognized complication in patients with traumatic brain injury (TBI) and is associated with increased morbidity and mortality. Currently, no standard exists for optimal timing or a pharmacological agent for VTE prophylaxis (pharmacological thromboprophylaxis - PTP) in patients with TBI. PTP is often delayed out of fear of causing extension of intracranial hemorrhage (ICH). The purpose of this study was to report the frequency of VTE and ICH progression after initiation of PTP with a continuous infusion of unfractionated heparin in patients with moderate to severe TBI, and to identify risk factors associated with development of VTE.  In this single-center retrospective study, patients with moderate to severe TBI admitted to the ICU of a Swiss Level I Trauma Center over a three-year period were analyzed.  In 23 (13%) of the 177 patients included in the study a VTE episode occurred during the hospital stay. ICH progression after initiation of PTP occurred in 7 (4%) patients. In a multivariable logistic regression model, only the timing of initiation of PTP was identified as an independent predictor of VTE.  In this study population, the risk of developing VTE increased with the delay of initiation of a pharmacological VTE prophylaxis, while ICH progression after initiation of PTP was a rare event.

title: Outcome of Coronavirus spectrum infections (SARS, MERS, COVID 1 -19) during pregnancy: a systematic review and meta-analysis
abstract: The aim of this systematic review was to report pregnancy and perinatal outcomes of Coronavirus (CoV) spectrum infections, and particularly COVID-19 disease due to SARS-COV-2 infection during pregnancy.  Medline, Embase, Cinahl and Clinicaltrials.gov databases were searched electronically utilizing combinations of word variants for “coronavirus” or “severe acute respiratory syndrome” or “SARS” or “Middle East respiratory syndrome” or “MERS” or “COVID-19” and “pregnancy”. The search and selection criteria were restricted to English language.  Inclusion criteria were pregnant women with a confirmed Coronavirus related illness, defined as either SARS, MERS or COVID-19. Study appraisal and synthesis methods We used meta-analyses of proportions to combine data and reported pooled proportions. The pregnancy outcomes observed included miscarriage, preterm birth, pre-eclampsia, preterm prelabor rupture of membranes, fetal growth restriction, and mode of delivery. The perinatal outcomes observed were fetal distress, Apgar score < 7 at five minutes, neonatal asphyxia, admission to neonatal intensive care unit, perinatal death, and evidence of vertical transmission.  19 studies including 79 women were eligible for this systematic review: 41 pregnancies (51.9%) affected by COVID-19, 12 (15.2%) by MERS, and 26 (32.9%) by SARS. An overt diagnosis of pneumonia was made in 91.8% and the most common symptoms were fever (82.6%), cough (57.1%) and dyspnea (27.0%). For all CoV infections, the rate of miscarriage was 39.1% (95% CI 20.2-59.8); the rate of preterm birth < 37 weeks was 24.3% (95% CI 12.5-38.6); premature prelabor rupture of membranes occurred in 20.7% (95% CI 9.5-34.9), preeclampsia in 16.2% (95% CI 4.2-34.1), and fetal growth restriction in 11.7% (95% CI 3.2-24.4); 84% were delivered by cesarean; the rate of perinatal death was 11.1% (95% CI 84.8-19.6) and 57.2% (95% CI 3.6-99.8) of newborns were admitted to the neonatal intensive care unit. When focusing on COVID-19, the most common adverse pregnancy outcome was preterm birth < 37 weeks, occurring in 41.1% (95% CI 25.6-57.6) of cases, while the rate of perinatal death was 7.0% (95% CI 1.4-16.3). None of the 41 newborns assessed showed clinical signs of vertical transmission.  In mothers infected with coronavirus infections, including COVID-19, >90% of whom also had pneumonia, PTB is the most common adverse pregnancy outcome. Miscarriage, preeclampsia, cesarean, and perinatal death (7-11%) were also more common than in the general population. There have been no published cases of clinical evidence of vertical transmission. Evidence is accumulating rapidly, so these data may need to be updated soon. The findings from this study can guide and enhance prenatal counseling of women with COVID-19 infection occurring during pregnancy.

title: Online COVID-19 diagnosis with chest CT images: Lesion-attention deep neural networks
abstract: Chest (computed tomography) CT scanning is one of the most important technologies for COVID-19 diagnosis in the current clinical practice, which motivates more concerted efforts in developing AI-based diagnostic tools to alleviate the enormous burden on the medical system. We develop a lesion-attention deep neural network (LA-DNN) to predict COVID-19 positive or negative with a richly annotated chest CT image dataset. The CT image dataset contains 746 public chest CT images of COVID-19 patients collected from over 760 preprints, and the data annotations are accompanied with the textual radiology reports. We extract two types of important information from these annotations: One is the flag of whether an image indicates a positive or negative case of COVID-19, and the other is the description of five lesions on the CT images associated with the positive cases. The proposed data-driven LA-DNN model focuses on the primary task of binary classification for COVID-19 diagnosis, while an auxiliary multi-label learning task is implemented simultaneously to draw the model's attention to the five lesions of COVID-19 during the training. The joint task learning process makes it a highly sample-efficient deep model that can learn COVID-19 radiology features effectively with very limited samples. The experimental results show that the area under the curve (AUC) and sensitivity (recall) for the diagnosis of COVID-19 patients are 91.2% and 85.7% respectively, which reach the clinical standards for practical use. An online system has been developed for fast online diagnoses using CT images at the web address https://www.covidct.cn/.

title: Risk of COVID-19 importation to the Pacific islands through global air travel
abstract: On 30 January 2020, WHO declared coronavirus (COVID-19) a global public health emergency. As of 12 March 2020, 125 048 confirmed COVID-19 cases in 118 countries had been reported. On 12 March 2020, the first case in the Pacific islands was reported in French Polynesia; no other Pacific island country or territory has reported cases. The purpose of our analysis is to show how travellers may introduce COVID-19 into the Pacific islands and discuss the role robust health systems play in protecting health and reducing transmission risk. We analyse travel and Global Health Security Index data using a scoring tool to produce quantitative estimates of COVID-19 importation risk, by departing and arriving country. Our analysis indicates that, as of 12 March 2020, the highest risk air routes by which COVID-19 may be imported into the Pacific islands are from east Asian countries (specifically, China, Korea and Japan) to north Pacific airports (likely Guam, Commonwealth of the Northern Mariana Islands or, to a less extent, Palau); or from China, Japan, Singapore, the United States of America or France to south Pacific ports (likely, Fiji, Papua New Guinea, French Polynesia or New Caledonia). Other importation routes include from other east Asian countries to Guam, and from Australia, New Zealand and other European countries to the south Pacific. The tool provides a useful method for assessing COVID-19 importation risk and may be useful in other settings.

title: The Coming Coronavirus Crisis: What Can We Learn?
abstract: The coronavirus pandemic is bringing with it the prospect of severe financial and economic crises. The article investigates its economic consequences in terms of financial instability, economic recession, lower incomes and policy challenges at the national and European levels. What are some of the lessons that can be learned? This article argues that health is a global public good. Public health and welfare systems are crucial alternatives to the market and universal public health is a key element of an egalitarian policy.

title: Cardiac troponin I in patients with coronavirus disease 2019 (COVID-19): Evidence from a meta-analysis()

title: Single source of pangolin CoVs with a near identical Spike RBD to SARS-CoV-2
abstract: Multiple publications have independently described pangolin CoV genomes from the same batch of smuggled pangolins confiscated in Guangdong province in March, 2019. We analyzed the three metagenomic datasets that sampled this batch of pangolins and found that the two complete pangolin CoV genomes, GD_1 by Xiao et al. Nature and MP789 by Liu et al. PLoS Pathogens, were both built primarily using the 2019 dataset first described by Liu et al. Viruses. Other publications, such as Zhang et al. Current Biology and Lam et al. Nature, have also relied on this same dataset by Liu et al. Viruses for their assembly of pangolin CoV sequences and comparisons to SARS-CoV-2. To our knowledge, all of the published pangolin CoV genome sequences that share a nearly identical Spike receptor binding domain with SARS-CoV-2 originate from this single batch of smuggled pangolins. This raises the question of whether pangolins are truly reservoirs or hosts of SARS-CoV-2-related coronaviruses in the wild, or whether the pangolins may have contracted the CoV from another host species during trafficking. Our observations highlight the importance of requiring authors to publish their complete genome assembly pipeline and all contributing raw sequence data, particularly those supporting epidemiological investigations, in order to empower peer review and independent analysis of the sequence data.

title: SlideImages: A Dataset for Educational Image Classification
abstract: In the past few years, convolutional neural networks (CNNs) have achieved impressive results in computer vision tasks, which however mainly focus on photos with natural scene content. Besides, non-sensor derived images such as illustrations, data visualizations, figures, etc. are typically used to convey complex information or to explore large datasets. However, this kind of images has received little attention in computer vision. CNNs and similar techniques use large volumes of training data. Currently, many document analysis systems are trained in part on scene images due to the lack of large datasets of educational image data. In this paper, we address this issue and present SlideImages, a dataset for the task of classifying educational illustrations. SlideImages contains training data collected from various sources, e.g., Wikimedia Commons and the AI2D dataset, and test data collected from educational slides. We have reserved all the actual educational images as a test dataset in order to ensure that the approaches using this dataset generalize well to new educational images, and potentially other domains. Furthermore, we present a baseline system using a standard deep neural architecture and discuss dealing with the challenge of limited training data.

title: Coronavirus disease (COVID-19): a scoping review
abstract: In December 2019, a pneumonia caused by a novel coronavirus (SARS-CoV-2) emerged in Wuhan, China and has rapidly spread around the world since then.  This study aims to understand the research gaps related to COVID-19 and propose recommendations for future research.  We undertook a scoping review of COVID-19, comprehensively searching databases and other sources to identify literature on COVID-19 between 1 December 2019 and 6 February 2020. We analysed the sources, publication date, type and topic of the retrieved articles/studies.  We included 249 articles in this scoping review. More than half (59.0%) were conducted in China. Guidance/guidelines and consensuses statements (n = 56; 22.5%) were the most common. Most (n = 192; 77.1%) articles were published in peer-reviewed journals, 35 (14.1%) on preprint servers and 22 (8.8%) posted online. Ten genetic studies (4.0%) focused on the origin of SARS-CoV-2 while the topics of molecular studies varied. Nine of 22 epidemiological studies focused on estimating the basic reproduction number of COVID-19 infection (R(0)). Of all identified guidance/guidelines (n = 35), only ten fulfilled the strict principles of evidence-based practice. The number of articles published per day increased rapidly until the end of January.  The number of articles on COVID-19 steadily increased before 6 February 2020. However, they lack diversity and are almost non-existent in some study fields, such as clinical research. The findings suggest that evidence for the development of clinical practice guidelines and public health policies will be improved when more results from clinical research becomes available.

title: Communicable Disease Surveillance Ethics in the Age of Big Data and New Technology
abstract: Surveillance is essential for communicable disease prevention and control. Traditional notification of demographic and clinical information, about individuals with selected (notifiable) infectious diseases, allows appropriate public health action and is protected by public health and privacy legislation, but is slow and insensitive. Big data–based electronic surveillance, by commercial bodies and government agencies (for profit or population control), which draws on a plethora of internet- and mobile device–based sources, has been widely accepted, if not universally welcomed. Similar anonymous digital sources also contain syndromic information, which can be analysed, using customised algorithms, to rapidly predict infectious disease outbreaks, but the data are nonspecific and predictions sometimes misleading. However, public health authorities could use these online sources, in combination with de-identified personal health data, to provide more accurate and earlier warning of infectious disease events—including exotic or emerging infections—even before the cause is confirmed, and allow more timely public health intervention. Achieving optimal benefits would require access to selected data from personal electronic health and laboratory (including pathogen genomic) records and the potential to (confidentially) re-identify individuals found to be involved in outbreaks, to ensure appropriate care and infection control. Despite existing widespread digital surveillance and major potential community benefits of extending its use to communicable disease control, there is considerable public disquiet about allowing public health authorities access to personal health data. Informed public discussion, greater transparency and an ethical framework will be essential to build public trust in the use of new technology for communicable disease control.

title: Building a PubMed knowledge graph
abstract: PubMed is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguated, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID, and identifying fine-grained affiliation data from MapAffil. Through the integration of the credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving a F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities. The PKG is freely available on Figshare (https://figshare.com/s/6327a55355fc2c99f3a2, simplified version that exclude PubMed raw data) and TACC website (http://er.tacc.utexas.edu/datasets/ped.

title: Crisis-Critical Intellectual Property: Findings from the COVID-19 Pandemic
abstract: Within national and international innovation systems a pandemic calls for large-scale action by many actors across sectors, to mobilise resources, developing and manufacturing Crisis-Critical Products (CC-Products) efficiently and in the huge quantities needed. Nowadays, this also includes digital innovations from complex epidemiological models, AI, to open data platforms for prevention, diagnostic and treatment. Amongst the many challenges during a pandemic, innovation and manufacturing stakeholders find themselves engaged in new relationships, and are likely to face intellectual property (IP) related challenges. This paper adopts an IP perspective on the COVID-19 pandemic to identify pandemic related IP considerations and IP challenges. The focus is on challenges related to research, development and urgent upscaling of capacity to manufacture CC-Products in the huge volumes suddenly in demand. Its purpose is to provide a structure for steering clear of IP challenges to avoid delays in fighting a pandemic. We identify 4 stakeholder groups concerned with IP challenges: (i) governments, (ii) organisations owning existing Crisis-Critical IP, described as incumbents in Crisis-Critical Sectors (CC-Sectors), (iii) manufacturing firms from other sectors normally not producing CC-Products suddenly rushing into CC-Sectors to support the manufacturing of CC-Products (new entrants), and (iv) voluntary grassroot initiatives that are formed during a pandemic. This paper discusses IP challenges related to the development and manufacturing of technologies and products for (i) prevention (of spread), (ii) diagnosis of infected patients and (iii) the development of treatments. We offer an initial discussion of potential response measures to reduce IP associated risks among industrial stakeholders during a pandemic.

title: Impact of COVID‐19 Stay‐at‐Home Orders on Weight‐Related Behaviors Among Patients with Obesity
abstract: How the impact of the COVID‐19 stay‐at‐home orders are influencing physical, mental, and financial health among vulnerable populations, including those with obesity is unknown. The aim of the current study was to explore the health implications of COVID‐19 AMong a sample of adults with obesity.  A retrospective medical chart review identified patients with obesity from an obesity medicine clinic and a bariatric surgery (MBS) practice. Patients completed an online survey from April 15, 2020 to May 31, 2020 to assess COVID‐19 status and health behaviors during stay‐at‐home orders. Logistic regression models examined the impact of these orders on anxiety and depression by ethnic group.  A total of 123 patients (87% female, mean age 51.2 years [SD 13.0], mean BMI 40.2 [SD 6.7], 49.2% Non‐Hispanic white, 28.7% Non‐Hispanic black, 16.4% Hispanic, 7% other ethnicity, 33.1% completed MBS were included. Two patients tested positive for SARS‐CoV‐2 and 14.6% reported symptoms. 72.8% reported increased anxiety and 83.6% increased depression since stay‐at‐home orders were initiated. 69.6% reported more difficultly in achieving weight loss goals, less exercise time (47.9%) and intensity (55.8%), increased stockpiling of food (49.6%) and stress eating (61.2%). Hispanics were less likely to report anxiety vs non‐Hispanic whites (aOR 0.16; 95% CI, 0.05‐0.49; P = 0.009).  Results here showed the COVID‐19 pandemic is having a significant impact on patients with obesity regardless of infection status. These results can inform clinicians and healthcare professionals about effective strategies to minimize COVID‐19 negative outcomes for this vulnerable population now and in post‐COVID‐19 recovery efforts. This article is protected by copyright. All rights reserved.

title: Characteristics of scientific articles on COVID-19 published during the initial three months of the pandemic: a meta-epidemiological study
abstract: The COVID-19 pandemic has been characterized by an unprecedented amount of published scientific articles.  To assess the characteristics of articles published during the first 3 months of the COVID-19 pandemic and to compare it with articles published during 2009 H1N1 swine influenza pandemic.  Articles on COVID-19 and on H1N1 swine influenza indexed in PubMed (Medline) during the first 3 months of these pandemics.  Any article published in the respective study periods that included any terminology related to COVID-19 or H1N1 in the title, abstract or full-text was eligible for inclusion. Articles that did not present an English abstract, as well as correspondence to previous research and erratum were excluded.  Two operators conducted the selection of articles and data extraction procedures independently. The article is reported following STROBE guidelines for observational studies.  Prevalence of primary and secondary articles. Prevalence of reporting of limitations in the abstracts.  Of the 2482 articles retrieved, 1165 were included. Approximately half of them were secondary articles (575, 49.4%). Common primary articles were: human medical research (340, 59.1%), in silico studies (182, 31.7%) and in vitro studies (26, 4.5%). Of the human medical research, the vast majority were observational studies and cases series, followed by single case reports and one randomized controlled trial. Secondary articles were mainly reviews, viewpoints and editorials (373, 63.2%). The second largest category was guidelines or guidance articles, including 193 articles (32.7%), of which 169 were indications for specific departments, patients or procedures. Limitations were reported in 42 out of 1165 abstracts (3.6%), with 10 abstracts reporting actual methodological limitations. In a similar timeframe in 2009 there were 223 articles published on the H1N1 pandemic. As compared to that pandemic, during COVID-19 there were higher chances to publish reviews and guidance articles and lower chances to publish in vitro and animal research studies.  As compared to the most recent pandemic, there is an overwhelming amount of information published on COVID-19. However, the majority of the articles published do not add significant information, possibly diluting the original information published. Also, only a negligible number of published articles reports limitations in the abstracts, hindering a rapid interpretation of their shortcomings.

title: COVID-19 and mental health: A review of the existing literature
abstract: The COVID-19 pandemic is a major health crisis affecting several nations, with over 720,000 cases and 33,000 confirmed deaths reported to date. Such widespread outbreaks are associated with adverse mental health consequences. Keeping this in mind, existing literature on the COVID-19 outbreak pertinent to mental health was retrieved via a literature search of the PubMed database. Published articles were classified according to their overall themes and summarized. Preliminary evidence suggests that symptoms of anxiety and depression (16–28%) and self-reported stress (8%) are common psychological reactions to the COVID-19 pandemic, and may be associated with disturbed sleep. A number of individual and structural variables moderate this risk. In planning services for such populations, both the needs of the concerned people and the necessary preventive guidelines must be taken into account. The available literature has emerged from only a few of the affected countries, and may not reflect the experience of persons living in other parts of the world. In conclusion, subsyndromal mental health problems are a common response to the COVID-19 pandemic. There is a need for more representative research from other affected countries, particularly in vulnerable populations.

title: [An analysis of global research on SARS-CoV-2]
abstract: The SARS-CoV-2 has been spread to 26 countries around the world since its outbreak. By February 16, 2020, more than 68 000 people had been diagnosed with COVID-19. Researchers from all over the world have carried out timely studies on this public health emergency and produced a number of scientific publications. This review aims to re-analyze and summarize the current research findings in a timely manner to guide scholars in relevant fields to further SARS-CoV-2 research and assist healthcare professionals in their work and decision-making. The SARS-CoV-2 related terms were selected in both English and Chinese and were searched in several major databases, including Pubmed, Web of Science, CNKI, Wanfang, and VIP databases. The reference list of each search result was screened for relevance, which was further supplemented to the search results. The included studies were categorized by topics with key characteristics extracted, re-analyzed, and summarized. A total of 301 articles were finally included with 136 in Chinese and 165 in English. The number of publications has rapidly increased since mid-January, 2020, and a peak day was 6th February on which 50 articles were published. The top three countries publishing articles were China, the United States and the United Kingdom. The Lancet and its specialty journals have published the most articles, with contribution also from journals such as New England Journal of Medicine ( NEJM), The Journal of the American Medical Association ( JAMA), and Nature. All articles were categorized into epidemiology, clinical diagnosis and treatment, basic research, pregnant women and children, mental health, epidemic prevention & control, and others. The literatures related to SARS-CoV-2 are emerging rapidly. It is necessary to sort out and summarize the research topic in time, which has a good reference value for staff in different positions. At the same time, it is necessary to strengthen the judgment of the quality of literatures.

title: NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset
abstract: Since the outbreak of coronavirus disease 2019 (COVID-19) in the late 2019, it has affected over 200 countries and billions of people worldwide. This has affected the social life of people owing to enforcements, such as"social distancing"and"stay at home."This has resulted in an increasing interaction through social media. Given that social media can bring us valuable information about COVID-19 at a global scale, it is important to share the data and encourage social media studies against COVID-19 or other infectious diseases. Therefore, we have released a multilingual dataset of social media posts related to COVID-19, consisting of microblogs in English and Japanese from Twitter and those in Chinese from Weibo. The data cover microblogs from January 20, 2020, to March 24, 2020. This paper also provides a quantitative as well as qualitative analysis of these datasets by creating daily word clouds as an example of text-mining analysis. The dataset is now available on Github. This dataset can be analyzed in a multitude of ways and is expected to help in efficient communication of precautions related to COVID-19.

title: The BCG dilemma: linear versus non-linear correlation models over the time of the COVID-19 pandemic
abstract: In this work we have analysed the use of two different correlation metrics and two different datasets, detecting differences in the extent and directionality of the correlation between COVID-19 infection and BCG immunization. Correlation between COVID-19 infection, death rates and BCG immunization is strongly affected by the correlation metrics and so will be their biological interpretation

title: Student Academic Performance Prediction Using Deep Multi-source Behavior Sequential Network
abstract: Online education is becoming increasingly popular and often combined with traditional place-based study to improve learning efficiency for university students. Since students have left a large amount of online learning data, it provides an effective way to predict students’ academic performance and enable pre-intervention for at-risk students. Current data sources used to predict students’ performance are limited to data just from the corresponding learning platform, from which only learning behaviors on that course can be observed. However, students’ academic performance will be related to other behavioral factors, especially the patterns of using Internet. In this paper, we utilize two types of datasets from 505 university students, i.e., online learning records for a project-based course, and network logs of university campus network. A deep learning framework: Sequential Prediction based on Deep Network (SPDN) is proposed to predict students’ performance in the course. SPDN models students’ online behavioral sequences by utilizing multi-source fusion CNN technique, and incorporates static information based on bidirectional LSTM. Experiments demonstrate that the proposed SPDN model outperforms the baselines and has a significant improvement on early-warning. Furthermore, it can be learned that Internet access patterns even have a greater impact on students’ academic performance than online learning activities.

title: COVID19-Tracker: una aplicación Shiny para analizar datos de la epidemia de SARS-CoV-2 en España./ [COVID19-Tracker: a shiny app to analise data on SARS-CoV-2 epidemic in Spain]
abstract: Data visualization is an important tool for exploring and communicating findings in medical research, and specially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases and mortality, from February 24th, 2020 onwards. Three applications have already been developed: 1) to analyze data trends and estimating short-term projections; 2) to estimate the case fatality rate; and 3) to assess the effect of the lockdowns on the data trends. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain.

title: Leveraging Schema Labels to Enhance Dataset Search
abstract: A search engine’s ability to retrieve desirable datasets is important for data sharing and reuse. Existing dataset search engines typically rely on matching queries to dataset descriptions. However, a user may not have enough prior knowledge to write a query using terms that match with description text. We propose a novel schema label generation model which generates possible schema labels based on dataset table content. We incorporate the generated schema labels into a mixed ranking model which not only considers the relevance between the query and dataset metadata but also the similarity between the query and generated schema labels. To evaluate our method on real-world datasets, we create a new benchmark specifically for the dataset retrieval task. Experiments show that our approach can effectively improve the precision and NDCG scores of the dataset retrieval task compared with baseline methods. We also test on a collection of Wikipedia tables to show that the features generated from schema labels can improve the unsupervised and supervised web table retrieval task as well.

title: Deep Transfer Learning based Classification Model for COVID-19 Disease
abstract: The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography(CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.

title: Covid‐19 diffusion and its impact on dental practice in distant countries with similar ethnic background
abstract: Epidemiology of Covid‐19 and diffusion patterns of the novel coronavirus (SARS – CoV – 2) are still poorly understood, as they differ substantially among countries and even in neighboring regions within the same country. It is not clear if differences in viral spreading depends on population age, genetic influences, specific environmental factors (e.g. season of the year), socioeconomic conditions or a combination of these. Some Authors hypothesized that differences in models of SARS – CoV – 2 diffusion may be explained also on the basis of ethnic influences (Pareek, Bangash, Pareek, Pan, Sze, Minhas, Hanif,  2020). Under such perspective, it seems rational to compare the Italian experience to that of Uruguay.

title: Epidemiological Characteristics on the Clustering Nature of COVID-19 in Qingdao City, 2020: A Descriptive Analysis
abstract: As an emerging infectious disease, COVID-19 has involved many countries and regions. With the further development of the epidemic, the proportion of clusters has increased.  In our study, we collected information on COVID-19 clusters in Qingdao City. The epidemiological characteristics and clinical manifestations were analyzed.  Eleven clusters of COVID-19 were reported in Qingdao City between January 29, and February 23, 2020, involving 44 confirmed cases, which accounted for 73.33% of all confirmed cases. From January 19 to February 2, 2020, the cases mainly concentrated in the district that had many designated hospitals. Patients aged 20-59 y old accounted for the largest proportion (68.18%) of cases; the male-to-female sex ratio was 0.52:1. Three cases were infected from exposure to confirmed cases. The average incubation period was 6.28 d. The median number of cases per cluster was 4, and the median duration time was 6 d. The median cumulative number of exposed persons was 53.  More attention should be paid to the epidemic of clusters in prevention and control of COVID-19. In addition to isolating patients, it is essential to track, screen, and isolate those who have come in close contact with patients. Self-isolation is the key especially for healthy people in the epidemic area.

title: Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing
abstract: The outbreak of COVID-19 Coronavirus, namely SARS-CoV-2, has created a calamitous situation throughout the world. The cumulative incidence of COVID-19 is rapidly increasing day by day. Machine Learning (ML) and Cloud Computing can be deployed very effectively to track the disease, predict growth of the epidemic and design strategies and policy to manage its spread. This study applies an improved mathematical model to analyse and predict the growth of the epidemic. An ML-based improved model has been applied to predict the potential threat of COVID-19 in countries worldwide. We show that using iterative weighting for fitting Generalized Inverse Weibull distribution, a better fit can be obtained to develop a prediction framework. This can be deployed on a cloud computing platform for more accurate and real-time prediction of the growth behavior of the epidemic. A data driven approach with higher accuracy as here can be very useful for a proactive response from the government and citizens. Finally, we propose a set of research opportunities and setup grounds for further practical applications.

title: Masked Face Recognition Dataset and Application
abstract: In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on the masked faces. Most current advanced face recognition approaches are designed based on deep learning, which depend on a large number of face samples. However, at present, there are no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition Dataset (SMFRD). Among them, to the best of our knowledge, RMFRD is currently theworld's largest real-world masked face dataset. These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed. The multi-granularity masked face recognition model we developed achieves 95% accuracy, exceeding the results reported by the industry. Our datasets are available at: https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset.

title: IDseq – An Open Source Cloud-based Pipeline and Analysis Service for Metagenomic Pathogen Detection and Monitoring
abstract: Metagenomic next generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource limited environments.  We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics which are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences, and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2.  The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.

title: Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies
abstract: The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally-determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the 120 available SARS-CoV-2 sequences (as of 21 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.

title: SARS-CoV-2 (COVID-19): What do we know about children? A systematic review
abstract: Few paediatric cases of COVID-19 have been reported and we know little about the epidemiology in children, though more is known about other coronaviruses. We aimed to understand the infection rate, clinical presentation, clinical outcomes and transmission dynamics for SARS-CoV-2, in order to inform clinical and public health measures.  We undertook a rapid systematic review and narrative synthesis of all literature relating to SARS-CoV-2 in paediatric populations. The search terms also included SARS-CoV and MERS-CoV. We searched three databases and the COVID-19 resource centres of eleven major journals and publishers. English abstracts of Chinese language papers were included. Data were extracted and narrative syntheses conducted.  24 studies relating to COVID-19 were included in the review. Children appear to be less affected by COVID-19 than adults by observed rate of cases in large epidemiological studies. Limited data on attack rate indicate that children are just as susceptible to infection. Data on clinical outcomes are scarce but include several reports of asymptomatic infection and a milder course of disease in young children, though radiological abnormalities are noted. Severe cases are not reported in detail and there are little data relating to transmission.  Children appear to have a low observed case rate of COVID-19 but may have similar rates to adults of infection with SARS-CoV-2. This discrepancy may be because children are asymptomatic or too mildly infected to draw medical attention, be tested and counted in observed cases of COVID-19.

title: Community pharmacists and communication in the time of COVID-19: Applying the health belief model
abstract: The emergence of the novel coronavirus disease (COVID-19) pandemic presents an unprecedented health communications challenge. Healthcare providers should reinforce behaviors that limit the spread of the pandemic, including social distancing and remaining in the home whenever possible. Formal communications toolkits may not be prepared in a timely fashion. Community pharmacists can reinforce mitigation behaviors by applying the health belief model (HBM). This commentary provides an overview of the HBM and offers suggestions on how community pharmacists can use it as a guide to patient communication in these uncertain contexts.

title: Public Opinions towards COVID-19 in California and New York on Twitter
abstract: With the pandemic of COVID-19 and the release of related policies, discussions about the COVID-19 are widespread online. Social media becomes a reliable source for understanding public opinions toward this virus outbreak.  This study aims to explore public opinions toward COVID-19 on social media by comparing the differences in sentiment changes and discussed topics between California and New York in the United States.  A dataset with COVID-19-related Twitter posts was collected from March 5, 2020 to April 2, 2020 using Twitter streaming API. After removing any posts unrelated to COVID-19, as well as posts that contain promotion and commercial information, two individual datasets were created based on the geolocation tags with tweets, one containing tweets from California state and the other from New York state. Sentiment analysis was conducted to obtain the sentiment score for each COVID-19 tweet. Topic modeling was applied to identify top topics related to COVID-19.  While the number of COVID-19 cases increased more rapidly in New York than in California in March 2020, the number of tweets posted has a similar trend over time in both states. COVID-19 tweets from California had more negative sentiment scores than New York. There were some fluctuations in sentiment scores in both states over time, which might correlate with the policy changes and the severity of COVID-19 pandemic. The topic modeling results showed that the popular topics in both California and New York states are similar, with "protective measures" as the most prevalent topic associated with COVID-19 in both states.  Twitter users from California had more negative sentiment scores towards COVID-19 than Twitter users from New York. The prevalent topics about COVID-19 discussed in both states were similar with some slight differences.

title: Rapid implementation of real-time SARS-CoV-2 sequencing to investigate healthcare-associated COVID-19 infections
abstract: The burden and impact of healthcare-associated COVID-19 infections is unknown. We aimed to examine the utility of rapid sequencing of SARS-CoV-2 combined with detailed epidemiological analysis to investigate healthcare-associated COVID-19 infections and to inform infection control measures.  We set up rapid viral sequencing of SARS-CoV-2 from PCR-positive diagnostic samples using nanopore sequencing, enabling sample-to-sequence in less than 24 hours. We established a rapid review and reporting system with integration of genomic and epidemiological data to investigate suspected cases of healthcare-associated COVID-19.  Between 13 March and 24 April 2020 we collected clinical data and samples from 5191 COVID-19 patients in the East of England. We sequenced 1000 samples, producing 747 complete viral genomes. We conducted combined epidemiological and genomic analysis of 299 patients at our hospital and identified 26 genomic clusters involving 114 patients. 66 cases (57.9%) had a strong epidemiological link and 15 cases (13.2%) had a plausible epidemiological link. These results were fed back clinical, infection control and hospital management teams, resulting in infection control interventions and informing patient safety reporting.  We established real-time genomic surveillance of SARS-CoV-2 in a UK hospital and demonstrated the benefit of combined genomic and epidemiological analysis for the investigation of healthcare-associated COVID-19 infections. This approach enabled us to detect cryptic transmission events and identify opportunities to target infection control interventions to reduce further healthcare-associated infections.

title: Use of Available Data To Inform The COVID-19 Outbreak in South Africa: A Case Study
abstract: The coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization (WHO) in February 2020. Currently, there are no vaccines or treatments that have been approved after clinical trials. Social distancing measures, including travel bans, school closure, and quarantine applied to countries or regions are being used to limit the spread of the disease and the demand on the healthcare infrastructure. The seclusion of groups and individuals has led to limited access to accurate information. To update the public, especially in South Africa, announcements are made by the minister of health daily. These announcements narrate the confirmed COVID-19 cases and include the age, gender, and travel history of people who have tested positive for the disease. Additionally, the South African National Institute for Communicable Diseases updates a daily infographic summarising the number of tests performed, confirmed cases, mortality rate, and the regions affected. However, the age of the patient and other nuanced data regarding the transmission is only shared in the daily announcements and not on the updated infographic. To disseminate this information, the Data Science for Social Impact research group at the University of Pretoria, South Africa, has worked on curating and applying publicly available data in a way that is computer-readable so that information can be shared to the public - using both a data repository and a dashboard. Through collaborative practices, a variety of challenges related to publicly available data in South Africa came to the fore. These include shortcomings in the accessibility, integrity, and data management practices between governmental departments and the South African public. In this paper, solutions to these problems will be shared by using a publicly available data repository and dashboard as a case study.

title: A Guide to Benchmarking COVID‐19 Performance Data
abstract: If the COVID‐19 pandemic has already taught us anything, it is that policymakers, experts and public managers need to be capable of interpreting comparative data on their government's performance in a meaningful way. Simultaneously, they are confronted with different data sources (and measurements) surrounding COVID‐19 without necessarily having the tools to assess these sources strategically. Due to the speed with which decisions are required and the different data sources, it can be challenging for any policymaker, expert or public manager to make sense of how COVID‐19 has an impact, especially from a comparative perspective. Starting from the question “How can we benchmark COVID‐19 performance data across countries?”, this article presents important indicators, measurements, and their strengths and weaknesses, and concludes with practical recommendations. These include a focus on measurement equivalence, systems thinking, spatial and temporal thinking, multilevel governance and multimethod designs. This article is protected by copyright. All rights reserved.

title: Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China
abstract: An in-depth annotation of the newly discovered coronavirus (2019-nCoV) genome has revealed differences between 2019-nCoV and severe acute respiratory syndrome (SARS) or SARS-like coronaviruses. A systematic comparison identified 380 amino acid substitutions between these coronaviruses, which may have caused functional and pathogenic divergence of 2019-nCoV.

title: Recommended operating room practice during the COVID‐19 pandemic: systematic review
abstract: The COVID‐19 pandemic poses a critical global public health crisis. Operating room (OR) best practice in this crisis is poorly defined. This systematic review was performed to identify contemporary evidence relating to OR practice in the context of COVID‐19.  MEDLINE was searched systematically using PubMed (search date 19 March 2020) for relevant studies in accordance with PRISMA guidelines. Documented practices and guidance were assessed to determine Oxford Centre for Evidence‐Based Medicine (OCEBM) levels of evidence, and recommendations for practice within five domains were extracted: physical OR, personnel, patient, procedure, and other factors.  Thirty‐five articles were identified, of which 11 met eligibility criteria. Nine articles constituted expert opinion and two were retrospective studies. All articles originated from the Far East (China, 9; Singapore, 2); eight of the articles concerned general surgery. Common themes were identified within each domain, but all recommendations were based on low levels of evidence (median OCEBM level 5 (range 4–5)). The highest number of overlapping recommendations related to physical OR (8 articles) and procedural factors (13). Although few recommendations related to personnel factors, consensus was high in this domain, with all studies mandating the use of personal protective equipment.  There was little evidence to inform this systematic review, but there was consensus regarding many aspects of OR practice. Within the context of a rapidly evolving pandemic, timely amalgamation of global practice and experiences is needed to inform best practice.

title: EdNet: A Large-Scale Hierarchical Dataset in Education
abstract: Advances in Artificial Intelligence in Education (AIEd) and the ever-growing scale of Interactive Educational Systems (IESs) have led to the rise of data-driven approaches for knowledge tracing and learning path recommendation. Unfortunately, collecting student interaction data is challenging and costly. As a result, there is no public large-scale benchmark dataset reflecting the wide variety of student behaviors observed in modern IESs. Although several datasets, such as ASSISTments, Junyi Academy, Synthetic and STATICS are publicly available and widely used, they are not large enough to leverage the full potential of state-of-the-art data-driven models. Furthermore, the recorded behavior is limited to question-solving activities. To this end, we introduce EdNet, a large-scale hierarchical dataset of diverse student activities collected by Santa, a multi-platform self-study solution equipped with an artificial intelligence tutoring system. EdNet contains 131,417,236 interactions from 784,309 students collected over more than 2 years, making it the largest public IES dataset released to date. Unlike existing datasets, EdNet records a wide variety of student actions ranging from question-solving to lecture consumption to item purchasing. Also, EdNet has a hierarchical structure which divides the student actions into 4 different levels of abstractions. The features of EdNet are domain-agnostic, allowing EdNet to be easily extended to different domains. The dataset is publicly released for research purposes. We plan to host challenges in multiple AIEd tasks with EdNet to provide a common ground for the fair comparison between different state-of-the-art models and to encourage the development of practical and effective methods.

title: Applying Lexical Link Analysis to Discover Insights from Public Information on COVID-19
abstract: SARS-Cov-2, the deadly and novel virus, which has caused a worldwide pandemic and drastic loss of human lives and economic activities. An open data set called the COVID-19 Open Research Dataset or CORD-19 contains large set full text scientific literature on SARS-CoV-2. The Next Strain consists of a database of SARS-CoV-2 viral genomes from since 12/3/2019. We applied an unique information mining method named lexical link analysis (LLA) to answer the call to action and help the science community answer high-priority scientific questions related to SARS-CoV-2. We first text-mined the CORD-19. We also data-mined the next strain database. Finally, we linked two databases. The linked databases and information can be used to discover the insights and help the research community to address high-priority questions related to the SARS-CoV-2’s genetics, tests, and prevention.  In this paper, we show how to apply an unique information mining method lexical link analysis (LLA) to link unstructured (CORD-19) and structured (Next Strain) data sets to relevant publications, integrate text and data mining into a single platform to discover the insights that can be visualized, and validated to answer the high-priority questions of genetics, incubation, treatment, symptoms, and prevention of COVID-19.

title: Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
abstract: In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.

title: Cancer datasets and the SARS-CoV-2 pandemic: establishing principles for collaboration

title: TRENDS IN PERCEPTION OF COVID-19 IN POLISH INTERNET
abstract: Due to the spread of SARS-CoV-2 virus responsible for COVID-19 disease, there is an urgent need to analyse COVID-19 epidemic perception in Poland. This study aims to investigate social perception of coronavirus in the Internet media during the epidemic. It is a signal report highlighting the main issues in public perception and medical commutation in real time.  We study the perception of COVID-19 epidemic in Polish society using quantitative analysis of its digital footprints on the Internet on platforms: Google, Twitter, YouTube, Wikipedia and electronic media represented by Event Registry, from January 2020 to 29.04.2020 (before and after official introduction to Poland on 04.03.20). We present trend analysis with a support of natural language processing techniques.  We identified seven temporal major clusters of interest on the topic COVID-19: 1) Chinese, 2) Italian, 3) Waiting, 4) Mitigations, 5) Social distancing and Lockdown, 6) Anti-crisis shield, 7) Restrictions releasing. There was an exponential increase of interest when the Polish government declared war against disease around 11/12.03.20 with a massive mitigation program. Later on, there was a decay in interest with additional phases related to social distancing and an anti-crisis legislation act with local peaks. We have found that declarations of mitigation strategies by the Polish prime minister or the minister of health gathered the highest attention of Internet users. So enacted or in force events do not affect interest to such extent. Traditional news agencies were ahead of social media (mainly Twitter) in dissemination of information. We have observed very weak or even negative correlations between a colloquial searching term antiviral mask in Google, encyclopaedic definition in Wikipedia SARS-CoV-2 as well official incidence series, implying different mechanisms governing the search for knowledge, panic related behaviour and actual risk of acquiring infection.  Traditional and social media do not only reflect reality, but also create it. Risk perception in Poland is unrelated to actual physical risk of acquiring COVID-19. As traditional media are ahead of social media in time, we advise to choose traditional news media for a quick dissemination of information, however for a greater impact, social media should be used. Otherwise public information campaigns might have less impact on society than expected.

title: Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions
abstract: At the time of writing, the ongoing pandemic of coronavirus disease (COVID-19) has caused severe impacts on society, economy and people's daily lives. People constantly express their opinions on various aspects of the pandemic on social media, making user-generated content an important source for understanding public emotions and concerns. In this paper, we perform a comprehensive analysis on the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020. Specifically, by identifying people's sentiments, emotions (i.e., anger, disgust, fear, happiness, sadness, surprise) and the emotional triggers (e.g., what a user is angry/sad about) we are able to depict the dynamics of public affect in the time of COVID-19. By contrasting two very different countries, China and the Unites States, we reveal sharp differences in people's views on COVID-19 in different cultures. Our study provides a computational approach to unveiling public emotions and concerns on the pandemic in real-time, which would potentially help policy-makers better understand people's need and thus make optimal policy.

title: 3-D Printed Protective Equipment during COVID-19 Pandemic
abstract: While the number of coronavirus cases from 2019 continues to grow, hospitals are reporting shortages of personal protective equipment (PPE) for frontline healthcare workers. Furthermore, PPE for the eyes and mouth, such as face shields, allow for additional protection when working with aerosols. 3-D printing enables the easy and rapid production of lightweight plastic frameworks based on open-source data. The practicality and clinical suitability of four face shields printed using a fused deposition modeling printer were examined. The weight, printing time, and required tools for assembly were evaluated. To assess the clinical suitability, each face shield was worn for one hour by 10 clinicians and rated using a visual analogue scale. The filament weight (21–42 g) and printing time (1:40–3:17 h) differed significantly between the four frames. Likewise, the fit, wearing comfort, space for additional PPE, and protection varied between the designs. For clinical suitability, a chosen design should allow sufficient space for goggles and N95 respirators as well as maximum coverage of the facial area. Consequently, two datasets are recommended. For the final selection of the ideal dataset to be used for printing, scalability and economic efficiency need to be carefully balanced with an acceptable degree of protection.

title: End-to-end learning for semiquantitative rating of COVID-19 severity on Chest X-rays
abstract: In this work we designed an end-to-end deep learning architecture for predicting, on Chest X-rays images (CRX), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semiquantitative scoring system, namely Brixia-score, was applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score estimation) trained with a"from part to whole"procedure involving different datasets. In particular, we exploited a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our BS-Net demonstrated self-attentive behavior and a high degree of accuracy in all processing stages. Through inter-rater agreement tests and a gold standard comparison, we were able to show that our solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring. Highly resolved (super-pixel level) explainability maps were also generated, with an original technique, to visually help the understanding of the network activity on the lung areas. We eventually tested the performance robustness of our model on a variegated public COVID-19 dataset, for which we also provide Brixia-score annotations, observing good direct generalization and fine-tuning capabilities that favorably highlight the portability of BS-Net in other clinical settings.

title: A Data-informed Public Health Policy-Makers Platform
abstract: Hearing loss is a disease exhibiting a growing trend due to a number of factors, including but not limited to the mundane exposure to the noise and ever-increasing size of the older population. In the framework of a public health policymaking process, modeling of the hearing loss disease based on data is a key factor in alleviating the issues related to the disease and in issuing effective public health policies. First, the paper describes the steps of the data-driven policymaking process. Afterward, a scenario along with the part of the proposed platform responsible for supporting policymaking are presented. With the aim of demonstrating the capabilities and usability of the platform for the policy-makers, some initial results of preliminary analytics are presented in the framework of a policy-making process. Ultimately, the utility of the approach is validated throughout the results of the survey which was presented to the health system policy-makers involved in the policy development process in Croatia.

title: RIEMS: a software pipeline for sensitive and comprehensive taxonomic classification of reads from metagenomics datasets
abstract: Fuelled by the advent and subsequent development of next generation sequencing technologies, metagenomics became a powerful tool for the analysis of microbial communities both scientifically and diagnostically. The biggest challenge is the extraction of relevant information from the huge sequence datasets generated for metagenomics studies. Although a plethora of tools are available, data analysis is still a bottleneck.  To overcome the bottleneck of data analysis, we developed an automated computational workflow called RIEMS – Reliable Information Extraction from Metagenomic Sequence datasets. RIEMS assigns every individual read sequence within a dataset taxonomically by cascading different sequence analyses with decreasing stringency of the assignments using various software applications. After completion of the analyses, the results are summarised in a clearly structured result protocol organised taxonomically. The high accuracy and performance of RIEMS analyses were proven in comparison with other tools for metagenomics data analysis using simulated sequencing read datasets.  RIEMS has the potential to fill the gap that still exists with regard to data analysis for metagenomics studies. The usefulness and power of RIEMS for the analysis of genuine sequencing datasets was demonstrated with an early version of RIEMS in 2011 when it was used to detect the orthobunyavirus sequences leading to the discovery of Schmallenberg virus.  contains supplementary material, which is available to authorized users.

title: Revealing variants in SARS-CoV-2 interaction domain of ACE2 and loss of function intolerance through analysis of >200,000 exomes
abstract: Our researchers took a look at a sequence of DNA known as the ACE2 gene. This gene is most well known for its role in regulating blood pressure. But in recent times, it’s drawn a lot of attention from the scientific community because it may also serve as a doorway of sorts, enabling viruses like SARS-CoV-2 to infect cells. Our researchers looked at the ACE2 gene in more than 200,000 people, comparing their exact DNA sequences to see where there are differences among people. Variation in the DNA sequence of a gene is common and is sometimes meaningless. But other times, small changes in the DNA sequence can alter the protein that is made from that gene. In this case the ACE2 gene makes the ACE2 protein, which is what the SARS-CoV-2 virus interacts with. We found a lot of variation between individuals and checked to see if that variation coincided with any traits (i.e., people with variant X tend to have high blood pressure more often than people without variant X). All of the traits we looked at were non-COVID-19-related traits, meaning we haven’t asked these people anything about COVID-19 yet (this is because these DNA sequences were collected before the pandemic). We found that there are a number of variations observed among people in a specific part of the ACE2 gene. These variations are expected to alter the shape or functionality of a specific part of the ACE2 protein: The part that interacts with the SARS-CoV-2 virus. We don’t yet know what the real-life significance of this variation is, but it’s possible that these variants decrease the protein’s ability to interact with the SARS-CoV-2 virus, thus decreasing the person’s likelihood of being infected. We can speculate that there will be a spectrum of vulnerability to COVID-19 among people, where some people are more vulnerable than others, and that variants in this part of the ACE2 gene may be one of the reasons. The research we presented here shines a light on this part of the ACE2 gene and may give future researchers a direction to go in as they try to figure out what makes people vulnerable to COVID-19 and similar viruses.

title: Does SARS‐CoV‐2 has a longer incubation period than SARS and MERS?
abstract: The outbreak of a novel coronavirus (SARS‐CoV‐2) since December 2019 in Wuhan, the major transportation hub in central China, became an emergency of major international concern. While several etiological studies have begun to reveal the specific biological features of this virus, the epidemic characteristics need to be elucidated. Notably, a long incubation time was reported to be associated with SARS‐CoV‐2 infection, leading to adjustments in screening and control policies. To avoid the risk of virus spread, all potentially exposed subjects are required to be isolated for 14 days, which is the longest predicted incubation time. However, based on our analysis of a larger dataset available so far, we find there is no observable difference between the incubation time for SARS‐CoV‐2, severe acute respiratory syndrome coronavirus (SARS‐CoV), and middle east respiratory syndrome coronavirus (MERS‐CoV), highlighting the need for larger and well‐annotated datasets.

title: Vaccine against Middle East respiratory syndrome coronavirus

title: Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
abstract: This paper provides an early evaluation of Artificial Intelligence (AI) against COVID-19. The main areas where AI can contribute to the fight against COVID-19 are discussed. It is concluded that AI has not yet been impactful against COVID-19. Its use is hampered by a lack of data, and by too much data. Overcoming these constraints will require a careful balance between data privacy and public health, and rigorous human-AI interaction. It is unlikely that these will be addressed in time to be of much help during the present pandemic. In the meantime, extensive gathering of diagnostic data on who is infectious will be essential to save lives, train AI, and limit economic damages.

title: COVID19-Global: A shiny application to perform a global comparative data visualization for the SARS-CoV-2 epidemic
abstract: Data visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance. The COVID19-Global online web application systematically produces daily updated data visualization and analysis of the SARS-CoV-2 epidemic on a global scale. It collects automatically daily data on COVID-19 diagnosed cases and mortality worldwide from January 1st, 2020 onwards. We have implemented comparative data visualization between countries for the most common indicators in epidemiological surveillance to follow an epidemic: attack rate, population fatality rate, case fatality rate, and basic reproduction number. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.

title: Mapping the South African health landscape in response to COVID-19
abstract: When the COVID-19 disease pandemic infiltrated the world, there was an immediate need for accurate information. As with any outbreak, the outbreak follows a clear trajectory, and subsequently, the supporting information for that outbreak needs to address the needs associated with that stage of the outbreak. At first, there was a need to inform the public of the information related to the initial situation related to the"who"of the COVID-19 disease. However, as time continued, the"where","when"and"how to"related questions started to emerge in relation to the public healthcare system themselves. Questions surrounding the health facilities including COVID-19 hospital bed capacity, locations of designated COVID-19 facilities, and general information related to these facilities were not easily accessible to the general public. Furthermore, the available information was found to be outdated, fragmented across several platforms, and still had gaps in the data related to these facilities. To rectify this problem, a group of volunteers working on the covid19za project stepped in to assist. Each member leading a part of the project chose to focus on one of four problems related to the challenges associated with the Hospital information including: data quality, data completeness, data source validation and data visualisation capacity. As the project developed, so did the sophistication of the data, visualisation and core function of the project. The future prospects of this project relate to a Progressive Web Application that will avail this information for the public as well as healthcare workers through comprehensive mapping and data quality.

title: Scientific Abstracts

title: Cleaning and Disinfectant Chemical Exposures and Temporal Associations with COVID-19 — National Poison Data System, United States, January 1, 2020–March 31, 2020
abstract: On January 19, 2020, the state of Washington reported the first U.S. laboratory-confirmed case of coronavirus disease 2019 (COVID-19) caused by infection with SARS-CoV-2 (1). As of April 19, a total of 720,630 COVID-19 cases and 37,202 associated deaths* had been reported to CDC from all 50 states, the District of Columbia, and four U.S. territories (2). CDC recommends, with precautions, the proper cleaning and disinfection of high-touch surfaces to help mitigate the transmission of SARS-CoV-2 (3). To assess whether there might be a possible association between COVID-19 cleaning recommendations from public health agencies and the media and the number of chemical exposures reported to the National Poison Data System (NPDS), CDC and the American Association of Poison Control Centers surveillance team compared the number of exposures reported for the period January-March 2020 with the number of reports during the same 3-month period in 2018 and 2019. Fifty-five poison centers in the United States provide free, 24-hour professional advice and medical management information regarding exposures to poisons, chemicals, drugs, and medications. Call data from poison centers are uploaded in near real-time to NPDS. During January-March 2020, poison centers received 45,550 exposure calls related to cleaners (28,158) and disinfectants (17,392), representing overall increases of 20.4% and 16.4% from January-March 2019 (37,822) and January-March 2018 (39,122), respectively. Although NPDS data do not provide information showing a definite link between exposures and COVID-19 cleaning efforts, there appears to be a clear temporal association with increased use of these products.

title: Inclusive Costs of NPI Measures for COVID-19 Pandemic: Three Approaches
abstract: The paper evaluates total inclusive costs of three public health approaches to address the COVID-19 epidemic in the US based on epidemiological projections in Ferguson et al (2020). We calculate and add costs of lost productivity and costs of mortality measured through the value of statistical life. We find that the aggressive approach which involves strict suppression measures and a drastic reduction of economic activity for three months with extensive testing and case tracking afterwards results in the lowest total costs for the society. The approach of doing no non-pharmaceutical measures results in the lowest total costs if the infection fatality rate falls below 0.15%.

title: Survey data of COVID-19-related Knowledge, Risk Perceptions and Precautionary Behavior among Nigerians
abstract: In response to the global call for strategic information to understand the novel coronavirus, the dataset presented in this paper is an examination of COVID-19-related knowledge, risk perceptions and precautionary health behavior among Nigerians. The data were generated during the COVID-19 lockdown in the country through a survey distributed via an online questionnaire, assessing socio-demographic information (7 items), knowledge (5 items), information sources (1 item), risk perception (9 items), expected end of lockdown (1 item), and COVID-19 precautionary health behavior (10 items), from 28th March to 4th April, 2020, gathering a total of 1,357 responses. A combination of purposive and snowball techniques helped to select the respondents via Whatsapp and Facebook from 180 cities/towns in the 6 geopolitical zones of Nigeria. The survey data were analyzed using descriptive and inferential statistics. The entire dataset is stored in a Microsoft Excel Worksheet (xlsx) and the questionnaire is attached as a supplementary file. The data will assist in curbing the Coronavirus pandemic by offering evidence for strategic and targeted interventions as well as health policy formulations and implementation.

title: NetNCSP: Nonoverlapping closed sequential pattern mining
abstract: Sequential pattern mining (SPM) has been applied in many fields. However, traditional SPM neglects the pattern repetition in sequence. To solve this problem, gap constraint SPM was proposed and can avoid finding too many useless patterns. Nonoverlapping SPM, as a branch of gap constraint SPM, means that any two occurrences cannot use the same sequence letter in the same position as the occurrences. Nonoverlapping SPM can make a balance between efficiency and completeness. The frequent patterns discovered by existing methods normally contain redundant patterns. To reduce redundant patterns and improve the mining performance, this paper adopts the closed pattern mining strategy and proposes a complete algorithm, named Nettree for Nonoverlapping Closed Sequential Pattern (NetNCSP) based on the Nettree structure. NetNCSP is equipped with two key steps, support calculation and closeness determination. A backtracking strategy is employed to calculate the nonoverlapping support of a pattern on the corresponding Nettree, which reduces the time complexity. This paper also proposes three kinds of pruning strategies, inheriting, predicting, and determining. These pruning strategies are able to find the redundant patterns effectively since the strategies can predict the frequency and closeness of the patterns before the generation of the candidate patterns. Experimental results show that NetNCSP is not only more efficient but can also discover more closed patterns with good compressibility. Furtherly, in biological experiments NetNCSP mines the closed patterns in SARS-CoV-2 and SARS viruses. The results show that the two viruses are of similar pattern composition with different combinations.

title: The Converging Triangle of Cultural Content, Cognitive Science, and Behavioral Economics
abstract: How online cultural content is chosen based on conscious or subconscious criteria is an central question across a broad spectrum of sciences and for the entertainment industry, including content providers and distributors. To this end, a number of tailored analytics forming the backbone of recommendation engines specialized for retrieving cultural content are proposed. Their strength derives directly from well-established principles of cognitive science and behavioral economics, both scientific fields exploring aspects of human decision making. Another novel contribution of this conference paper is that these analytics are implemented in Neo4j expressed as Cypher queries. Various aspects of the cultural content and digital consumers can be naturally represented by appropriately configured vertices, whereas edges represent various connections indicating content delivery preferences. Early experiments conducted over a synthetic dataset mimicking the distributions of preferences and ratings of well-known movie datasets are encouraging as the proposed analytics outperformed the baseline of a multilayer feedforward neural network of various configurations. The synthetic dataset contains enriched preferences of mobile digital consumers of cultural content regarding literature of the Greek region of Ionian Islands.

title: Pro-Russian Biases in Anti-Chinese Tweets about the Novel Coronavirus
abstract: The recent COVID-19 pandemic, which was first detected in Wuhan, China, has been linked to increased anti-Chinese sentiment in the United States. Recently, Broniatowski et al. found that foreign powers, and especially Russia, were implicated in information operations using public health crises to promote discord -- including racial conflict -- in American society (Broniatowski, 2018). This brief considers the problem of automatically detecting changes in overall attitudes, that may be associated with emerging information operations, via artificial intelligence. Accurate analysis of these emerging topics usually requires laborious, manual analysis by experts to annotate millions of tweets to identify biases in new topics. We introduce extensions of the Word Embedding Association Test from Caliskan et. al to a new domain (Caliskan, 2017). This practical and unsupervised method is applied to quantify biases being promoted in information operations. Analyzing historical information operations from Russia's interference in the 2016 U.S. presidential elections, we quantify biased attitudes for presidential candidates, and sentiment toward Muslim groups. We next apply this method to a corpus of tweets containing anti-Chinese hashtags. We find that roughly 1% of tweets in our corpus reference Russian-funded news sources and use anti-Chinese hashtags and, beyond the expected anti-Chinese attitudes, we find that this corpus as a whole contains pro-Russian attitudes, which are not present in a control Twitter corpus containing general tweets. Additionally, 4% of the users in this corpus were suspended within a week. These findings may indicate the presence of abusive account activity associated with rapid changes in attitudes around the COVID-19 public health crisis, suggesting potential information operations.

title: Mobility Changes in Response to COVID-19
abstract: In response to the COVID-19 pandemic, both voluntary changes in behavior and administrative restrictions on human interactions have occurred. These actions are intended to reduce the transmission rate of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We use anonymized and/or de-identified mobile device locations to measure mobility, a statistic representing the distance a typical member of a given population moves in a day. Results indicate that a large reduction in mobility has taken place, both in the US and globally. In the United States, large mobility reductions have been detected associated with the onset of the COVID-19 threat and specific government directives. Mobility data at the US admin1 (state) and admin2 (county) level have been made freely available under a Creative Commons Attribution (CC BY 4.0) license via the GitHub repository https://github.com/descarteslabs/DL-COVID-19/

title: A Comparison Between Chinese Children Infected with COVID-19 and with SARS
abstract: To compare the clinical and laboratory features of severe acute respiratory syndrome 2003 (SARS) and coronavirus disease 2019 (COVID-19) in two Chinese pediatric cohorts, given that the causative pathogens and are biologically similar.,  This is a cross-sectional study reviewing paediatric patients with SARS (n = 43) and COVID-19 (n=244) who were admitted to the Princess Margaret Hospital in Hong Kong and Wuhan Children’s Hospital in Wuhan, respectively. Demographics, hospital length of stay, clinical and laboratory features were compared  Overall, 97.7% of patients with SARS and 85.2% of patients with COVID-19 had epidemiological associations with known cases. Significantly more patients with SARS developed fever, chills, myalgia, malaise, coryza, sore throat, sputum production, nausea, headache, and dizziness than patients COVID-19. No SARS patients were asymptomatic at the time of admission. 29.1% and 20.9% COVID-19 patients were asymptomatic on admission and throughout their hospital stay, respectively. More SARS patients required oxygen supplementation than COVID-19 patients (18.6 vs. 4.7%, P = 004). Only 1.6% COVID-19 and 2.3% SARS patients required mechanical ventilation. Leukopenia (37.2% vs. 18.6%, p=0.008), lymphopenia (95.4% versus 32.6%, p<0.01), and thrombocytopenia (41.9% vs 3.8%, p<0.001) were significantly more common in SARS than COVID-19 patients. The duration between positive and negative nasopharyngeal aspirate and the length in hospital stay were similar in COVID-19 patients regardless of whether they were asymptomatic or symptomatic, suggesting a similar duration of viral shedding.  Children with COVID-19 were less symptomatic and had more favorable hematological findings than children with SARS.

title: Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis
abstract: Understanding the temporal dynamics of COVID-19 patient phenotypes is necessary to derive fine-grained resolution of the pathophysiology. Here we use state-of-the-art deep neural networks over an institution-wide machine intelligence platform for the augmented curation of 8.2 million clinical notes from 14,967 patients subjected to COVID-19 PCR diagnostic testing. By contrasting the Electronic Health Record (EHR)-derived clinical phenotypes of COVID-19-positive (COVIDpos, n=272) versus COVID-19-negative (COVIDneg, n=14,695) patients over each day of the week preceding the PCR testing date, we identify diarrhea (2.8-fold), change in appetite (2-fold), anosmia/dysgeusia (28.6-fold), and respiratory failure (2.1-fold) as significantly amplified in COVIDpos over COVIDneg patients. The specific combination of cough and diarrhea has a 4-fold amplification in COVIDpos patients during the week prior to PCR testing, and along with anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19 (4-7 days prior to typical PCR testing date). This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional knowledge captured in EHRs. The platform holds tremendous potential for scaling up curation throughput, with minimal need for training underlying neural networks, thus promising EHR-powered early diagnosis for a broad spectrum of diseases.

title: Coronaviruses and people with intellectual disability: an exploratory data analysis
abstract: Corona virus disease 2019 (COVID‐19) has been announced as a new coronavirus disease by the World Health Organization. At the time of writing this article (April 2020), the world is drastically influenced by the COVID‐19. Recently, the COVID‐19 Open Research Dataset (CORD‐19) was published. For researchers on ID such as ourselves, it is of key interest to learn whether this open research dataset may be used to investigate the virus and its consequences for people with an ID.  From CORD‐19, we identified full‐text articles containing terms related to the ID care and applied a text mining technique, specifically the term frequency–inverse document frequency analysis in combination with K‐means clustering.  Two hundred fifty‐nine articles contained one or more of our specified terms related to ID. We were able to cluster these articles related to ID into five clusters on different topics, namely: mental health, viral diseases, diagnoses and treatments, maternal care and paediatrics, and genetics.  The CORD‐19 open research dataset consists of valuable information about not only COVID‐19 disease but also ID and the relationship between them. We suggest researchers investigate literature‐based discovery approaches on the CORD‐19 and develop a new dataset that addresses the intersection of these two fields for further research.

title: The estimations of the COVID-19 incubation period: a systematic review of the literature
abstract: to undertake a review and critical appraisal of all published/ reports that offer an estimation of incubation periods for novel coronavirus (COVID-19).  a rapid and systematic review/critical appraisal  COVID-19 Open Research Dataset supplied by Georgetown's Centre for Security and Emerging Technology as well as PubMed and Embase via Arxiv, medRxiv, and bioRxiv.  screening was undertaken 44,000 articles with a final selection of 25 studies referring to 18 different experimental projects related to the estimation of the incubation period of COVID-19.  The majority of extant published estimates offer empirical evidence showing that the incubation period for the virus is a mean of 7.8 days, with a median of 5.01 days, which falls into the ranges proposed by the WHO (0 to 14 days) and the ECDC (2 to 12 days). Nevertheless, a number of authors proposed that quarantine time should be a minimum of 14 days and that for estimates of mortality risks a median time delay of 13 days between illness and mortality should be under consideration. It is unclear as to whether any correlation exists between the age of patients and the length of time they incubate the virus. Finally, it is generally agreed that robust precautions must be put in place for the prevention and/or mitigation of asymptomatic transmission to high-risk patients caused by those incubating the virus.

title: Application of Telemedicine During the Coronavirus Disease Epidemics: A Rapid Review and Meta-Analysis
abstract: As COVID-19 has become a global pandemic, early prevention and control of the epidemic is extremely important. Telemedicine, which includes medical advice given over telephone, Internet, mobile phone applications or other similar ways, may be an efficient way to reduce transmission and pressure on medical institutions.  We searched MEDLINE, Web of science, Embase, Cochrane, CBM, CNKI and Wanfang databases for literature on the use of telemedicine for COVID-19, SARS and MERS. from their inception to March 31st, 2020. We included studies about the content of the consultation (such as symptoms, therapy and prevention, policy, public service), screening of suspected cases, the provision of advice given to those people who may have symptoms or contact history. We conducted meta-analyses on the main outcomes of the studies.  A total of 2041 articles were identified after removing duplicates. After reading the full texts, we finally included nine studies. People were most concerned about symptoms (64.2%), epidemic situation and public problems (14.5%), and psychological problems (10.3%) during COVID-19 epidemic. During the SARS epidemic, the proportions of people asking for consultation for symptoms, prevention and therapy, and psychological problems were 35.0%, 22.0%, and 23.0%, respectively. Two studies demonstrated that telemedicine can be used to screen the suspected patients and give advice. One study emphasized the limited possibilities to follow up people calling hotlines and difficulties in identifying all suspect cases.  Telemedicine services should focus on the issues that the public is most concerned about, such as then symptoms, prevention and treatment of the disease, and provide reasonable advice to patients with symptoms or people with epidemic history. Keywords：COVID-19; SARS; MERS; telemedicine; rapid review

title: COVID19-Tracker: una aplicación Shiny para analizar datos de la epidemia de SARS-CoV-2 en España./ [COVID19-Tracker: a shiny app to analise data on SARS-CoV-2 epidemic in Spain]
abstract: Data visualization is an important tool for exploring and communicating findings in medical research, and specially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases and mortality, from February 24th, 2020 onwards. Three applications have already been developed: 1) to analyze data trends and estimating short-term projections; 2) to estimate the case fatality rate; and 3) to assess the effect of the lockdowns on the data trends. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain.

title: Neurological Impact of Coronavirus Disease (COVID-19): Practical Considerations for the Neuroscience Community
abstract: The coronavirus disease of 2019 (COVID-19) that is caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has recently been designated a pandemic by the World Health Organization, affecting 2.7 million individuals globally as of April 25, 2020 with over 187,000 deaths. A growing body of evidence supports central nervous system (CNS) involvement.  We conducted a review of the literature for articles concerning COVID-19 pathophysiology, neurological manifestations, and neuroscience provider recommendations and guidelines.  CNS manifestations range from vague non-focal complaints to severe neurologic impairment associated with encephalitis. It is unclear whether neurological dysfunction is due to direct viral injury or systemic disease. The virus may affect brainstem pathways that lead to indirect respiratory dysfunction in addition to direct pulmonary injury. Necessary adaptations in patient management, triage, and diagnosis are evolving in light of ongoing scientific and clinical findings.  This review consolidates the current body of literature regarding the neurological impact of coronaviruses, discusses the reported neurologic manifestations of COVID-19, and highlights recommendations for patient management. Specific recommendations pertaining to clinical practice for neurologists and neurosurgeons are provided.

title: Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning
abstract: There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, genes, pathways, and expressions, from a large scientific corpus of 24 million PubMed publications. Using Amazon AWS computing resources, we identified 41 repurposable drugs (including indomethacin, toremifene and niclosamide) whose therapeutic association with COVID-19 were validated by transcriptomic and proteomic data in SARS-CoV-2 infected human cells and data from ongoing clinical trials. While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.

title: An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
abstract: The coronavirus disease (COVID-19) pandemic has led to a devastating effect on the global public health. Computed Tomography (CT) is an effective tool in the screening of COVID-19. It is of great importance to rapidly and accurately segment COVID-19 from CT to help diagnostic and patient monitoring. In this paper, we propose a U-Net based segmentation network using attention mechanism. As not all the features extracted from the encoders are useful for segmentation, we propose to incorporate an attention mechanism including a spatial and a channel attention, to a U-Net architecture to re-weight the feature representation spatially and channel-wise to capture rich contextual relationships for better feature representation. In addition, the focal tversky loss is introduced to deal with small lesion segmentation. The experiment results, evaluated on a COVID-19 CT segmentation dataset where 473 CT slices are available, demonstrate the proposed method can achieve an accurate and rapid segmentation on COVID-19 segmentation. The method takes only 0.29 second to segment a single CT slice. The obtained Dice Score, Sensitivity and Specificity are 83.1%, 86.7% and 99.3%, respectively.

title: An updated analysis of turning point, duration and attack rate of COVID-19 outbreaks in major Western countries with data of daily new cases
abstract: As coronavirus spreads around the world, the study of its effects is of great practical significance. We collated data on daily new cases of the COVID-19 outbreaks in the six Western countries of the Group of Seven and the dates of governments’ interventions. We studied the periods before and after the dates of major governments’ interventions integrally based on a segmented Poisson model. The relevant results are published in the paper of “Predicting turning point, duration and attack rate of COVID - 19 outbreaks in major Western countries” . Our method can be used to update prediction daily as COVID-19 outbreaks evolve. In this article, we illustrate an updated analysis with our method to facilitate reproducibility. Both datasets used and updated are provided.

title: UniProt: a worldwide hub of protein knowledge
abstract: The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

title: CORD-19: The Covid-19 Open Research Dataset.
abstract: The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 75K times and has served as the basis of many Covid-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and preview tools and upcoming shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for Covid-19.

title: Effects of age and sex on recovery from COVID-19: Analysis of 5,769 Israeli patients

title: Molecular mechanism of action of repurposed drugs and traditional Chinese medicine used for the treatment of patients infected with COVID-19: A systematic review
abstract: The emergence of COVID-19 as a pandemic has resulted in the need for urgent development of vaccines and drugs and the conduction of clinical trials to fight the outbreak. Because of the time constraints associated with the development of vaccines and effective drugs, drug repurposing and other alternative treatment methods have been used to treat patients that have been infected by the SARS-CoV-2 virus and have acquired COVID-19. In this systematic review, we provide an overview of the molecular mechanism of action of repurposed drugs or alternative treatment medicines used to attenuate COVID-19 disease. The research articles or grey literature, including theses, government reports, and official news online, were identified from 4 databases and 1 search engine. The full content of a total of 160 articles that fulfilled our inclusion criteria was analyzed and information about 6 drugs (ritonavir, lopinavir, oseltamivir, remdesivir, favipiravir, and chloroquine) and 4 traditional Chinese medicines (Shuang Huang Lian Kou Fu Ye, TCM combination of Bu Huan Jin Zheng Qi San and Da Yuan Yin, Xue Bi Jing Injection and Qing Fei Pai Du Tang) were extracted. All of the drug treatment options depend on the ability of the drug to inhibit the proliferation of the SARS-CoV-2 virus by binding to enzyme active sites, viral chain termination, or triggering of the molecular pathway, whereas traditional Chinese medicine has a pivotal role in triggering the inflammation pathway, such as the neuraminidase blocker, to fight the SARS-CoV-2 virus. This review provides an insight to experimental validation of drugs and alternative medicine used for the treatment and control of COVID-19.

title: In silico comparison of spike protein-ACE2 binding affinities across species; significance for the possible origin of the SARS-CoV-2 virus
abstract: The devastating impact of the COVID19 pandemic caused by SARS coronavirus 2 (SARSCoV2) has raised important questions on the origins of this virus, the mechanisms of any zoonotic transfer from exotic animals to humans, whether companion animals or those used for commercial purposes can act as reservoirs for infection, and the reasons for the large variations in susceptibilities across animal species. Traditional lab-based methods will ultimately answer many of these questions but take considerable time. In silico modeling methods provide the opportunity to rapidly generate information on newly emerged pathogens to aid countermeasure development and also to predict potential future behaviors. We used a structural homology modeling approach to characterize the SARSCoV2 spike protein and predict its binding strength to the human ACE2 receptor. We then explored the possible transmission path by which SARSCoV2 might have crossed to humans by constructing models of ACE2 receptors of relevant species, and calculating the binding energy of SARSCoV2 spike protein to each. Notably, SARSCoV2 spike protein had the highest overall binding energy for human ACE2, greater than all the other tested species including bat, the postulated source of the virus. This indicates that SARSCoV2 is a highly adapted human pathogen. Of the species studied, the next highest binding affinity after human was pangolin, which is most likely explained by a process of convergent evolution. Binding of SARSCoV2 for dog and cat ACE2 was similar to affinity for bat ACE2, all being lower than for human ACE2, and is consistent with only occasional observations of infections of these domestic animals. Overall, the data indicates that SARSCoV2 is uniquely adapted to infect humans, raising questions as to whether it arose in nature by a rare chance event or whether its origins lie elsewhere.

title: Eliciting Disease Data from Wikipedia Articles.
abstract: Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. Internet systems are particularly attractive for disease outbreaks because they can provide data in near real-time and can be verified by individuals around the globe. However, most existing systems have focused on disease monitoring and do not provide a data repository for policy makers or researchers. In order to fill this gap, we analyzed Wikipedia article content. We demonstrate how a named-entity recognizer can be trained to tag case counts, death counts, and hospitalization counts in the article narrative that achieves an F1 score of 0.753. We also show, using the 2014 West African Ebola virus disease epidemic article as a case study, that there are detailed time series data that are consistently updated that closely align with ground truth data. We argue that Wikipedia can be used to create the first community-driven open-source emerging disease detection, monitoring, and repository system.

title: A fast and simple one-step duplex PCR assay for canine distemper virus (CDV) and canine coronavirus (CCoV) detection
abstract: The one-step polymerase chain reaction (one-step PCR) detection assay is an innovative PCR detection method, eliminating nucleic acid extraction steps, in which samples can be directly added to PCR reagents for testing. For simultaneous detection of CDV and CCoV, a sensitive and specific one-step duplex PCR (one-step dPCR) assay was developed with two pairs of primers that were designed based on H and M gene sequences of CDV and CCoV, respectively. The one-step dPCR with optimized detection conditions has high specificity and sensitivity; independent sequencing assays further verified these results.

title: Oscillations in USA COVID-19 Incidence and Mortality Data reflect societal factors
abstract: The COVID-19 pandemic currently in process differs from other infectious disease calamities that have previously plagued humanity in the vast amount of information that is produces each day, which includes daily estimates of the disease incidence and mortality data. Apart from providing actionable information to public health authorities on the trend of the pandemic, the daily incidence reflects the process of disease in a susceptible population and thus reflects the pathogenesis of COVID-19, the public health response and diagnosis and reporting. Both daily new cases and daily mortality data in the US exhibit periodic oscillatory patterns. By analyzing NYC and LA testing data, we demonstrate that this oscillation in the number of cases can be strongly explained by the daily variation in testing. This seems to rule out alternative hypotheses such as increased infections on certain days of the week as driving this oscillation. Similarly, we show that the apparent oscillation in mortality in the US data is mostly an artifact of reporting, which disappears in datasets that record death by episode date, such as the NYC and LA datasets. Periodic oscillations in COVID-19 incidence and mortality data reflect testing and reporting practices and contingencies. Thus, these contingencies should be considered first prior to suggesting social or biological mechanisms.

title: Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries
abstract: The coronavirus disease 2019 (COVID-19) pandemic is the defining global health crisis of our time and the greatest challenge facing the world. Meteorological parameters are reportedly crucial factors affecting respiratory infectious disease epidemics; however, the effect of meteorological parameters on COVID-19 remains controversial. This study investigated the effects of temperature and relative humidity on daily new cases and daily new deaths of COVID-19, which has useful implications for policymakers and the public. Daily data on meteorological conditions, new cases and new deaths of COVID-19 were collected for 166 countries (excluding China) as of March 27, 2020. Log-linear generalized additive model was used to analyze the effects of temperature and relative humidity on daily new cases and daily new deaths of COVID-19, with potential confounders controlled for, including wind speed, median age of the national population, Global Health Security Index, Human Development Index and population density. Our findings revealed that temperature and relative humidity were both negatively related to daily new cases and deaths. A 1 °C increase in temperature was associated with a 3.08% (95% CI: 1.53%, 4.63%) reduction in daily new cases and a 1.19% (95% CI: 0.44%, 1.95%) reduction in daily new deaths, whereas a 1% increase in relative humidity was associated with a 0.85% (95% CI: 0.51%, 1.19%) reduction in daily new cases and a 0.51% (95% CI: 0.34%, 0.67%) reduction in daily new deaths. The results remained robust when different lag structures and the sensitivity analysis were used. These findings provide preliminary evidence that the COVID-19 pandemic may be partially suppressed with temperature and humidity increases. However, active measures must be taken to control the source of infection, block transmission and prevent further spread of COVID-19.

title: In Silico Discovery of Candidate Drugs against Covid-19
abstract: Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.

title: COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown
abstract: Italy has been severely affected by the COVID-19 pandemic, reporting the highest death toll in Europe as of April 2020. Following the identification of the first infections, on February 21, 2020, national authorities have put in place an increasing number of restrictions aimed at containing the outbreak and delaying the epidemic peak. On March 12, the government imposed a national lockdown. To aid the evaluation of the impact of interventions, we present daily time-series of three different aggregated mobility metrics: the origin-destination movements between Italian provinces, the radius of gyration, and the average degree of a spatial proximity network. All metrics were computed by processing a large-scale dataset of anonymously shared positions of about 170,000 de-identified smartphone users before and during the outbreak, at the sub-national scale. This dataset can help to monitor the impact of the lockdown on the epidemic trajectory and inform future public health decision making.

title: Experience of establishing severe acute respiratory surveillance in the Netherlands: evaluation and challenges
abstract: The 2009 influenza A(H1N1) pandemic prompted the World Health Organization (WHO) to recommend countries to establish a national severe acute respiratory infections (SARI) surveillance system for preparedness and emergency response. However, setting up or maintaining a robust SARI surveillance system has been challenging. Similar to other countries, surveillance data on hospitalisations for SARI in the Netherlands are still limited, in contrast to the robust surveillance data in primary care. The objective of this narrative review is to provide an overview, evaluation, and challenges of already available surveillance systems or datasets in the Netherlands, which might be used for near real-time surveillance of severe respiratory infections. Seven available surveillance systems or datasets in the Netherlands were reviewed. The evaluation criteria, including data quality, timeliness, representativeness, simplicity, flexibility, acceptability and stability were based on United States Centers for Disease Control and Prevention (CDC) and European Centre for Disease Prevention and Control (ECDC) guidelines for public health surveillance. We added sustainability as additional evaluation criterion. The best evaluated surveillance system or dataset currently available for SARI surveillance is crude mortality monitoring, although it lacks specificity. In contrast to influenza-like illness (ILI) in primary care, there is currently no gold standard for SARI surveillance in the Netherlands. Based on our experience with sentinel SARI surveillance, a fully or semi-automated, passive surveillance system seems most suited for a sustainable SARI surveillance system. An important future challenge remains integrating SARI surveillance into existing hospital programs in order to make surveillance data valuable for public health, as well as hospital quality of care management and individual patient care.

title: Cigarette smoke exposure and inflammatory signaling increase the expression of the SARS-CoV-2 receptor ACE2 in the respiratory tract
abstract: The factors mediating fatal SARS-CoV-2 infections are poorly understood. Here, we show that cigarette smoke causes a dose-dependent upregulation of Angiotensin Converting Enzyme 2 (ACE2), the SARS-CoV-2 receptor, in rodent and human lungs. Using single-cell sequencing data, we demonstrate that ACE2 is expressed in a subset of secretory cells in the respiratory tract. Chronic smoke exposure triggers the expansion of this cell population and a concomitant increase in ACE2 expression. In contrast, quitting smoking decreases the abundance of these secretory cells and reduces ACE2 levels. Finally, we demonstrate that ACE2 expression is responsive to inflammatory signaling and can be upregulated by viral infections or interferon treatment. Taken together, these results may partially explain why smokers are particularly susceptible to severe SARS-CoV-2 infections. Furthermore, our work identifies ACE2 as an interferon-stimulated gene in lung cells, suggesting that SARS-CoV-2 infections could create positive-feedback loops that increase ACE2 levels and facilitate viral dissemination.

title: Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data
abstract: Advances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses in cancer, but also in infectious diseases like COVID-19. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Here we propose Scirpy, a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data.  Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

title: Chinese Public Attention to COVID-19 Epidemic: Based on Social Media
abstract: Since the new coronavirus epidemic in China in December 2019, information and discussions about COVID-19 have spread rapidly on the Internet and have quickly become the focus of worldwide attention, especially on social media.  This study aims to investigate and analyze the public's attention to COVID-19-related events in China at the beginning of the COVID-19 epidemic in China (December 31, 2019, to February 20, 2020) through the Sina Microblog hot search list.  We collected topics related to the COVID-19 epidemic on the Sina Microblog hot search list from December 31, 2019, to February 20, 2020 and described the trend of public attention on COVID-19 epidemic-related topics. ROST CM6.0 (ROST Content Mining System Version 6.0) was used to analyze the collected text for word segmentation, word frequency, and sentiment analysis. We further described the hot topic keywords and sentiment trends of public attention. We used VOSviewer to implement a visual cluster analysis of hot keywords and build a social network of public opinion content.  The study has four main findings. First, we analyzed the changing trend of the public's attention to the COVID-19 epidemic, which can be divided into three stages. Second, the hot topic keywords of public attention at each stage are slightly different. In addition, the emotional tendency of the public toward the COVID-19 epidemic-related hot topics has changed from negative to neutral, with negative emotions weakening and positive emotions increasing as a whole. Finally, we divided the COVID-19 topics with the most public concern into five categories: (1) new COVID-19 epidemics and their impact; (2) frontline reporting of the epidemic and prevention and control measures; (3) expert interpretation and discussion on the source of infection; (4) medical services on the frontline of the epidemic; and (5) focus on the global epidemic and the search for suspected cases.  This is the first study of public attention on the COVID-19 epidemic using a Chinese social media platform (i.e., Sina Microblog). Our study found that social media (e.g., Sina Microblog) can be used to measure public attention to public health emergencies. During the epidemic of the novel coronavirus, a large amount of information about the COVID-19 epidemic was disseminated on Sina Microblog and received widespread public attention. We have learned about the hotspots of public concern regarding the COVID-19 epidemic. These findings can help the government and health departments better communicate with the public on health and translate public health needs into practice to create targeted measures to prevent and control the spread of COVID-19.  COVID-19; Sina Microblog; Public attention; Social media; China; Public health emergency

title: Surgical Practice in the Current COVID-19 Pandemic: A Rapid Systematic Review
abstract: The coronavirus disease (COVID-19) outbreak started in Wuhan, China, in December 2019, and evolved into a global problem in a short period. The pandemic has led to many social and health-care challenges. In this context, surgery is an area that is facing the need for many adaptations. In this systematic literature review, we analyzed different perspectives concerning this situation, aiming to provide recommendations that could guide surgeons and entities toward screening, elective and emergency surgeries, decision making, and operating room management. A computerized search in PubMed, Scopus, and Scientific Electronic Library Online (SciELO) for relevant literature up to April 4, 2020, was performed. Articles were included if they were related to surgery dynamics in the context of the COVID-19 pandemic. Of the 281 articles found in our initial search and 15 articles from alternative sources, 39 were included in our review after a systematic evaluation. Concerning preoperative testing for severe acute respiratory syndrome coronavirus 2 infection, 29 (74.4%) articles recommended some kind of screening. Another major suggestion was postponing all (or at least selected) elective operations (29 articles, 74.4%). Several additional recommendations with respect to surgical practice or surgical staff were also assessed and discussed, such as performing laparoscopic surgeries and avoiding the use of electrocauterization. On the basis of the current literature, we concluded that any surgery that can be delayed should be postponed. COVID-19 screening is strongly recommended for all surgical cases. Moreover, surgical staff should be reduced to the essential members and provided with institutional psychological support.

title: COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images
abstract: The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

title: Dataset of Ex-pat Teachers in Southeast Asia's Intention to Leave due to the COVID-19 pandemic
abstract: The COVID-19 pandemic exerted an adverse influence on the global education system, especially since starting school lockdown. The growth of teacher unemployment figures climbed double-digit and spawned these unexpected sequels. For instance, while native teachers seemed indisposed to leave the profession with the aim of seeking another more profited and seasonal jobs, many ex-pat teachers presented themselves with moving or stayed dilemma in the way the government salvaged their situation. In preference with the ex-pat teacher's case, we elucidated further throughout an e-survey in the International Baccalaureate community on Facebook from 4 to 11 April 2020 for 18,000 ex-pat teachers, who are teaching at Southeast Asia. This dataset includes 307 responses of ex-pat teachers who are staying in Singapore, Thailand, Vietnam, the Philippines, and Indonesia during the pandemic. The dataset comprises (i) Survey partakers' Demographics; (ii) Ex-pat teachers' perceptions in the relation of national, regional and school plans were afoot to the pandemic; (iii) The degree of attachment of ex-pat teacher to their current society, the ex-pat community, friends, and families during the pandemic time; (iv) Ex-pat teachers' embryo intention to reconsider their current teaching location.

title: Novel indicator of change in COVID-19 spread status
abstract: As the spread of COVID-19 worldwide progresses, it is important not only to protect human lives, but also to minimize social losses due to economic paralysis by detecting signs of spread at an early stage and predicting future trends accurately. This report introduces a new indicator (K) of the magnitude of the spread of COVID-19 based on daily number of infected people from publicly available data. Transitions of the K can predict effects of measures such as the blockage of cities and social distancing, signs of new spread, and possible regional dependence in the formation of population immunity.

title: Three months of informational trends in COVID-19 across New York City
abstract: In the midst of widespread community transmission of coronavirus disease 2019 (COVID-19) in New York, residents have sought information about COVID-19. We analyzed trends in New York State (NYS) and New York City (NYC) data to quantify the extent of COVID-19-related queries. Data on the number of 311 calls in NYC, Google Trend data on the search term ‘Coronavirus’ and information about trends in COVID-19 cases in NYS and the USA were compiled from multiple sources. There were 1228 994 total calls to 311 between 22 January 2020 and 22 April 2020, with 50 845 calls specific to COVID-19 in the study period. The proportion of 311 calls related to COVID-19 increased over time, while the ‘interest over time’ of the search term ‘Coronavirus’ has exponentially increased since the end of February 2020. It is vital that public health officials provide clear and up-to-date information about protective measures and crucial communications to respond to information-seeking behavior across NYC.

title: Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea
abstract: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations.  Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak.  Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources.  The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word "Coronavirus" communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers' attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852).  Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

title: A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19
abstract: The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates against a specific coronavirus. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19.

title: A new coronavirus associated with human respiratory disease in China
abstract: Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health(1–3). Despite intense research efforts, how, when and where new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing(4) of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae, which is designated here ‘WH-Human 1’ coronavirus (and has also been referred to as ‘2019-nCoV’). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China(5). This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans.

title: CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
abstract: The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. As of May 2020, 128,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset Challenge. Here we present CO-Search, a retriever-ranker semantic search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers during a time of crisis. The retriever is built from a Siamese-BERT encoder that is linearly composed with a TF-IDF vectorizer, and reciprocal-rank fused with a BM25 vectorizer. The ranker is composed of a multi-hop question-answering module, that together with a multi-paragraph abstractive summarizer adjust retriever scores. To account for the domain-specific and relatively limited dataset, we generate a bipartite graph of document paragraphs and citations, creating 1.3 million (citation title, paragraph) tuples for training the encoder. We evaluate our system on the data of the TREC-COVID information retrieval challenge. CO-Search obtains top performance on the datasets of the first and second rounds, across several key metrics: normalized discounted cumulative gain, precision, mean average precision, and binary preference.

title: Monitoring Depression Trend on Twitter during the COVID-19 Pandemic
abstract: The COVID-19 pandemic has severely affected people's daily lives and caused tremendous economic loss worldwide. However, its influence on people's mental health conditions has not received as much attention. To study this subject, we choose social media as our main data resource and create by far the largest English Twitter depression dataset containing 2,575 distinct identified depression users with their past tweets. To examine the effect of depression on people's Twitter language, we train three transformer-based depression classification models on the dataset, evaluate their performance with progressively increased training sizes, and compare the model's"tweet chunk"-level and user-level performances. Furthermore, inspired by psychological studies, we create a fusion classifier that combines deep learning model scores with psychological text features and users' demographic information and investigate these features' relations to depression signals. Finally, we demonstrate our model's capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. We hope this study can raise awareness among researchers and the general public of COVID-19's impact on people's mental health.

title: Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning.
abstract: Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma.

title: Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data
abstract: The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between “black box” deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data.

title: Artificial Intelligence-based Clinical Decision Support for COVID-19 -- Where Art Thou?
abstract: The COVID-19 crisis has brought about new clinical questions, new workflows, and accelerated distributed healthcare needs. While artificial intelligence (AI)-based clinical decision support seemed to have matured, the application of AI-based tools for COVID-19 has been limited to date. In this perspective piece, we identify opportunities and requirements for AI-based clinical decision support systems and highlight challenges that impact"AI readiness"for rapidly emergent healthcare challenges.

title: Research methodology and characteristics of journal articles with original data, preprint articles and registered clinical trial protocols about COVID-19
abstract: The research community reacted rapidly to the emergence of COVID-19. We aimed to assess characteristics of journal articles,  articles, and registered trial protocols about COVID-19 and its causal agent SARS-CoV-2.  We analyzed characteristics of journal articles with original data indexed by March 19, 2020, in World Health Organization (WHO) COVID-19 collection, articles published on preprint servers medRxiv and bioRxiv by April 3, 2010. Additionally, we assessed characteristics of clinical trials indexed in the WHO International Clinical Trials Registry Platform (WHO ICTRP) by April 7, 2020.  Among the first 2118 articles on COVID-19 published in scholarly journals, 533 (25%) contained original data. The majority was published by authors from China (75%) and funded by Chinese sponsors (75%); a quarter was published in the Chinese language. Among 312 articles that self-reported study design, the most frequent were retrospective studies (N = 88; 28%) and case reports (N = 86; 28%), analyzing patients’ characteristics (38%).  Among 1088 analyzed preprint articles, the majority came from authors affiliated in China (51%) and were funded by sources in China (46%). Less than half reported study design; the majority were modeling studies (62%), and analyzed transmission/risk/prevalence (43%). Of the 927 analyzed registered trials, the majority were interventional (58%). Half were already recruiting participants. The location for the conduct of the trial in the majority was China (N = 522; 63%). The median number of planned participants was 140 (range: 1 to 15,000,000). Registered intervention trials used highly heterogeneous primary outcomes and tested highly heterogeneous interventions; the most frequently studied interventions were hydroxychloroquine (N = 39; 7.2%) and chloroquine (N = 16; 3%).  Early articles on COVID-19 were predominantly retrospective case reports and modeling studies. The diversity of outcomes used in intervention trial protocols indicates the urgent need for defining a core outcome set for COVID-19 research. Chinese scholars had a head start in reporting about the new disease, but publishing articles in Chinese may limit their global reach. Mapping publications with original data can help finding gaps that will help us respond better to the new public health emergency.

title: Connecting data, tools and people across Europe: ELIXIR’s response to the COVID-19 pandemic
abstract: ELIXIR, the European research infrastructure for life science data, provides open access to data, tools and workflows in the response to the COVID-19 pandemic. ELIXIR’s 23 nodes have reacted swiftly to support researchers in their combined efforts against the pandemic setting out three joint priorities: 1. Connecting national COVID-19 data platforms to create federated European COVID-19 Data Spaces; 2. Fostering good data management to make COVID-19 data open, FAIR and reusable over the long term; 3. Providing open tools, workflows and computational resources to drive reproducible and collaborative science. ELIXIR’s strategy is based on the support given by our national nodes - collectively spanning over 200 institutes - to research projects and on partnering with community initiatives to drive development and adoption of good data practice and community driven standards. ELIXIR Nodes provide support activities locally and internationally, from provisioning compute capabilities to helping collect viral sequence data from hospitals. Some Nodes have prioritised access to their national cloud and compute facilities for all COVID-19 research projects, while others have developed tools to search, access and share all data related to the pandemic in a national healthcare setting.

title: Acute kidney injury in critically ill patients with COVID-19
abstract: Acute kidney injury (AKI) has been reported in up to 25% of critically-ill patients with SARS-CoV-2 infection, especially in those with underlying comorbidities. AKI is associated with high mortality rates in this setting, especially when renal replacement therapy is required. Several studies have highlighted changes in urinary sediment, including proteinuria and hematuria, and evidence of urinary SARS-CoV-2 excretion, suggesting the presence of a renal reservoir for the virus. The pathophysiology of COVID-19 associated AKI could be related to unspecific mechanisms but also to COVID-specific mechanisms such as direct cellular injury resulting from viral entry through the receptor (ACE2) which is highly expressed in the kidney, an imbalanced renin–angotensin–aldosteron system, pro-inflammatory cytokines elicited by the viral infection and thrombotic events. Non-specific mechanisms include haemodynamic alterations, right heart failure, high levels of PEEP in patients requiring mechanical ventilation, hypovolemia, administration of nephrotoxic drugs and nosocomial sepsis. To date, there is no specific treatment for COVID-19 induced AKI. A number of investigational agents are being explored for antiviral/immunomodulatory treatment of COVID-19 and their impact on AKI is still unknown. Indications, timing and modalities of renal replacement therapy currently rely on non-specific data focusing on patients with sepsis. Further studies focusing on AKI in COVID-19 patients are urgently warranted in order to predict the risk of AKI, to identify the exact mechanisms of renal injury and to suggest targeted interventions.  contains supplementary material, which is available to authorized users.

title: Coronavirus Disease: A Review of a New Threat to Public Health
abstract: In December 2019, several patients from Wuhan, China were admitted to hospitals with symptoms of pneumonia. As the number of patients presenting with similar symptoms started to rise, the causative agent was eventually isolated from samples. It was initially called the 2019 novel coronavirus (2019-nCoV) and has been recently relabelled as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); the disease it causes has been named coronavirus disease 2019 (COVID-19). Over the next few weeks, the virus spread from Wuhan to affect different provinces in China and, after a few months, it is now present in 109 countries. As of March 10, 2020, there have been 113,702 confirmed cases globally, and 4,012 deaths have been registered. The World Health Organization (WHO) called COVID-19 a pandemic on March 11, 2020. There are multiple drug trials going on with some positive results. However, since no vaccine is available, the best way to combat the virus is by preventive methods.

title: How related is SARS-CoV-2 to other coronaviruses?

title: E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine
abstract: Coronavirus becomes officially a global pandemic due to the speed spreading off in various countries. An increasing number of infected with this disease causes the Inability problem to fully care in hospitals and afflict many doctors and nurses inside the hospitals. This paper proposes a smart health system that monitors the patients holding the Coronavirus remotely. Due to protect the lives of the health services members (like physicians and nurses) from infection. This smart system observes the people with this disease based on putting many sensors to record many features of their patients in every second. These parameters include measuring the patient's temperature, respiratory rate, pulse rate, blood pressure, and time. The proposed system saves lives and improves making decisions in dangerous cases. It proposes using artificial intelligence and Internet-of-things to make remotely quarantine and develop decisions in various situations. It provides monitoring patients remotely and guarantees giving patients medicines and getting complete health care without anyone getting sick with this disease. It targets two people's slides the most serious medical conditions and infection and the lowest serious medical conditions in their houses. Observing in hospitals for the most serious medical cases that cause infection in thousands of healthcare members so there is a big need to uses it. Other less serious patients slide, this system enables physicians to monitor patients and get the healthcare from patient's houses to save places for the critical cases in hospitals.

title: AI-Empowered Data Analytics for Coronavirus Epidemic Monitoring and Control
abstract: Governments and authorities knew little about the virus since the emergency of COVID-19 outbreak. The Chinese government upon the discovery of the early patients in Wuhan, informed WHO on 31 December 2019, as pneumonia of unknown causes. Epidemiologists, data scientists and biostatisticians have been working hand-in-hand for a common mission of trying to characterize and understand the characteristics of the infection as well as the virus itself, which is SARS alike.

title: Hypergraph Learning for Identification of COVID-19 with CT Imaging
abstract: The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2,148 COVID-19 cases and 1,182 CAP cases from five hospitals, are conducted to evaluate the performance of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.

title: BioTorrents: A File Sharing Service for Scientific Data
abstract: The transfer of scientific data has emerged as a significant challenge, as datasets continue to grow in size and demand for open access sharing increases. Current methods for file transfer do not scale well for large files and can cause long transfer times. In this study we present BioTorrents, a website that allows open access sharing of scientific data and uses the popular BitTorrent peer-to-peer file sharing technology. BioTorrents allows files to be transferred rapidly due to the sharing of bandwidth across multiple institutions and provides more reliable file transfers due to the built-in error checking of the file sharing technology. BioTorrents contains multiple features, including keyword searching, category browsing, RSS feeds, torrent comments, and a discussion forum. BioTorrents is available at http://www.biotorrents.net.

title: Mutated COVID-19 may foretell a great risk for mankind in the future
abstract: Coronavirus disease 2019 SARS-CoV-2 (COVID-19) is a zoonotic virus causing a variety of severe respiratory diseases. SARS-CoV-2 is closest to SARS-CoV and MERS-CoV in structure. The high prevalence of COVID-19 is a result of a lack of symptoms at onset. Our study aimed to present an overview of the virus in terms of structure, epidemiology, symptoms, treatment and prevention. Whole genome sequences and some viral proteins were investigated to determine gaps and changes in alternation of nucleotides and amino acid sequences. We evaluate 11 complete genome sequences of different coronaviruses using BAST and MAFFT software. We also selected seven types of structural proteins. We conclude that COVID-19 might produce new mutations, specifically in glycoproteins, so caution and complete preparation by health authorities is required.

title: Gene expression and in situ protein profiling of candidate SARS-CoV-2 receptors in human airway epithelial cells and lung tissue
abstract: In December 2019, SARS-CoV-2 emerged causing the COVID-19 pandemic. SARS-CoV, the agent responsible for the 2003 SARS outbreak, utilizes ACE2 and TMPRSS2 host molecules for viral entry. ACE2 and TMPRSS2 have recently been implicated in SARS-CoV-2 viral infection. Additional host molecules including ADAM17, cathepsin L, CD147, and GRP78 may also function as receptors for SARS-CoV-2. To determine the expression and in situ localization of candidate SARS-CoV-2 receptors in the respiratory mucosa, we analyzed gene expression datasets from airway epithelial cells of 515 healthy subjects, gene promoter activity analysis using the FANTOM5 dataset containing 120 distinct sample types, single cell RNA sequencing (scRNAseq) of 10 healthy subjects, immunoblots on multiple airway epithelial cell types, and immunohistochemistry on 98 human lung samples. We demonstrate absent to low ACE2 promoter activity in a variety of lung epithelial cell samples and low ACE2 gene expression in both microarray and scRNAseq datasets of epithelial cell populations. Consistent with gene expression, rare ACE2 protein expression was observed in the airway epithelium and alveoli of human lung. We present confirmatory evidence for the presence of TMPRSS2, CD147, and GRP78 protein in vitro in airway epithelial cells and confirm broad in situ protein expression of CD147 in the respiratory mucosa. Collectively, our data suggest the presence of a mechanism dynamically regulating ACE2 expression in human lung, perhaps in periods of SARS-CoV-2 infection, and also suggest that alternate receptors for SARS-CoV-2 exist to facilitate initial host cell infection.

title: Identifying highly influential travellers for spreading disease on a public transport system
abstract: The recent outbreak of a novel coronavirus and its rapid spread underlines the importance of understanding human mobility. Enclosed spaces, such as public transport vehicles (e.g. buses and trains), offer a suitable environment for infections to spread widely and quickly. Investigating the movement patterns and the physical encounters of individuals on public transit systems is thus critical to understand the drivers of infectious disease outbreaks. For instance previous work has explored the impact of recurring patterns inherent in human mobility on disease spread, but has not considered other dimensions such as the distance travelled or the number of encounters. Here, we consider multiple mobility dimensions simultaneously to uncover critical information for the design of effective intervention strategies. We use one month of citywide smart card travel data collected in Sydney, Australia to classify bus passengers along three dimensions, namely the degree of exploration, the distance travelled and the number of encounters. Additionally, we simulate disease spread on the transport network and trace the infection paths. We investigate in detail the transmissions between the classified groups while varying the infection probability and the suspension time of pathogens. Our results show that characterizing individuals along multiple dimensions simultaneously uncovers a complex infection interplay between the different groups of passengers, that would remain hidden when considering only a single dimension. We also identify groups that are more influential than others given specific disease characteristics, which can guide containment and vaccination efforts.

title: The Possible of Immunotherapy for COVID-19: a Systematic Review
abstract: The novel coronavirus (2019-nCoV) is an emerging pathogen that was first described in late December 2019 and causes a severe respiratory infection in humans. Since the outbreak of COVID-19, international attention has raised to develop treatment and control options such as types of immunotherapies. The immunotherapy is an effective method for fighting against similar viral infections such as SARS-CoV, and MERS-CoV. These methods include several types of vaccines, monoclonal antibody candidates, and etc. This systematic review article was designed to evaluate the existing evidence and experience related to immunotherapy for 2019-nCoV. Web of Science (ISI), PubMed, and Scopus databases were used to search for suitable keywords such as 2019-nCoV, novel coronavirus, Immunotherapy, interleukin, vaccine and the related words for relevant publications up to 24.3.2020. The present systematic review was performed based on PRISMA protocol. Data extraction and quality valuation of articles were performed by two reviewers. 51 articles were the results of the search and based on the inclusions and exclusions criteria, 7 articles were included in the final review. As a conclusion of these studies demonstrated that although no serious research has been done on this subject at the time of writing this article, similar studies on the related viruses showed notable results. So immunotherapy for this virus can also be a suitable option.

title: AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images
abstract: As the Coronavirus Disease 2019 (COVID-19) pandemic continues to grow globally, testing to detect COVID-19 and isolating individuals who test positive remains to be the primary strategy for preventing community spread of the disease. The current gold standard method of testing for COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) test. The RT-PCR test, however, has an imperfect sensitivity (around 70%), is time-consuming and labor-intensive, and is in short supply, particularly in resource-limited countries. Therefore, automatic and accurate detection of COVID-19 using medical imaging modalities such as chest X-ray and Computed Tomography, which are more widely available and accessible, can be beneficial. We develop a novel hierarchical attention neural network model to classify chest radiography images as belonging to a person with either COVID-19, other infections, or no pneumonia (i.e., normal). We refer to this model as Artificial Intelligence for Detection of COVID-19 (AIDCOV). The hierarchical structure in AIDCOV captures the dependency of features and improves model performance while the attention mechanism makes the model interpretable and transparent. Using a publicly available dataset of 5801 chest images, we demonstrate that our model achieves a mean cross-validation accuracy of 97.8%. AIDCOV has a sensitivity of 99.3%, a specificity of 99.98%, and a positive predictive value of 99.6% in detecting COVID-19 from chest radiography images. AIDCOV can be used in conjunction with or instead of RT-PCR testing (where RT-PCR testing is unavailable) to detect and isolate individuals with COVID-19 and prevent onward transmission to the general population and healthcare workers.

title: Social distance and SARS memory: impact on the public awareness of 2019 novel coronavirus (COVID-19) outbreak
abstract: This study examines publicly available online search data in China to investigate the spread of public awareness of the 2019 novel coronavirus (COVID-19) outbreak. We found that cities that suffered from SARS and have greater migration ties to the epicentre, Wuhan, had earlier, stronger and more durable public awareness of the outbreak. Our data indicate that forty-eight such cities developed awareness up to 19 days earlier than 255 comparable cities, giving them an opportunity to better prepare. This study suggests that it is important to consider memory of prior catastrophic events as they will influence the public response to emerging threats.

title: Open-source analytics tools for studying the COVID-19 coronavirus outbreak
abstract: To provide convenient access to epidemiological data on the coronavirus outbreak, we developed an R package, nCov2019 (https://github.com/GuangchuangYu/nCov2019). Besides detailed real-time statistics, it offers access to three data sources with detailed daily statistics from December 1, 2019, for 43 countries and more than 500 Chinese cities. We also developed a web app (http://www.bcloud.org/e/) with interactive plots and simple time-series forecasts. These analytics tools could be useful in informing the public and studying how this and similar viruses spread in populous countries.

title: Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: Case study of COVID-19 in Alberta, Canada and Philadelphia, USA
abstract: The aim of our work was to better understand misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves. We examined publically available time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, about the sensitivity and specificity of the PCR-based diagnostic test. Data originated from Alberta, Canada (available on 3/28/2020) and city of Philadelphia, USA (available on 3/31/2020). Our analysis revealed that the data were compatible with near-perfect specificity but it was challenging to gain information about sensitivity (prior and posterior largely overlapped). We applied these insights to uncertainty/bias analysis of epidemic curves into jurisdictions under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the observed and adjusted epidemic curves likely fall within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60–70% range. In the extreme scenario, hundreds of undiagnosed cases, even among tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate. The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves, a task for which the Bayesian method we presented is well-suited.

title: Impact of corticosteroid therapy on outcomes of persons with SARS-CoV-2, SARS-CoV, or MERS-CoV infection: a systematic review and meta-analysis
abstract: We performed a meta-analysis to determine safety and efficacy of corticosteroids in SARS-CoV-2, SARS-CoV, and MERS-CoV infections. We searched PubMed, Web of Science, Medline, WanFang Chinese database, and ZhiWang Chinese database using Boolean operators and search terms covering SARS-CoV-2, SARS-CoV, OR MERS-CoV AND corticosteroids to find appropriate studies. Review Manager 5.3 was used to analyze results of meta-analysis. Observational studies were analyzed for quality using the modified Newcastle–Ottawa scale and randomized clinical trials, using the Jadad scale. Subjects were divided into those with severe-only and other (severe and not severe) cohorts based on published criteria. Efficacy endpoints studied included mortality, hospitalization duration, rates of intensive care unit (ICU) admission, use of mechanical ventilation, and a composite endpoint (death, ICU admission, or mechanical ventilation). We included 11 reports including 10 cohort studies and 1 randomized clinical trial involving 5249 subjects (2003–2020). Two discussed the association of corticosteroids and virus clearing and 10 explored how corticosteroids impacted mortality, hospitalization duration, use of mechanical ventilation, and a composite endpoint. Corticosteroid use was associated with delayed virus clearing with a mean difference (MD) = 3.78 days (95% confidence Interval [CI] = 1.16, 6.41 days; I(2) = 0%). There was no significant reduction in deaths with relative Risk Ratio (RR) = 1.07 (90% CI = 0.81; 1.42; I(2) = 80%). Hospitalization duration was prolonged and use of mechanical ventilation increased. In conclusion, corticosteroid use in subjects with SARS-CoV-2, SARS-CoV, and MERS-CoV infections delayed virus clearing and did not convincingly improve survival, reduce hospitalization duration or ICU admission rate and/or use of mechanical ventilation. There were several adverse effects. Because of a preponderance of observational studies in the dataset and selection and publication biases our conclusions, especially regarding SARS-CoV-2, need confirmation in a randomized clinical trial. In the interim we suggest caution using corticosteroids in persons with COVID-19.

title: A Light CNN for detecting COVID-19 from CT scans of the chest
abstract: OVID-19 is a world-wide disease that has been declared as a pandemic by the World Health Organization. Computer Tomography (CT) imaging of the chest seems to be a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. Deep Learning has been extensively used in medical imaging and convolutional neural networks (CNNs) have been also used for classification of CT images. We propose a light CNN design based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with other CT images (community-acquired pneumonia and/or healthy images). On the tested datasets, the proposed modified SqueezeNet CNN achieved 83.00\% of accuracy, 85.00\% of sensitivity, 81.00\% of specificity, 81.73\% of precision and 0.8333 of F1Score in a very efficient way (7.81 seconds medium-end laptot without GPU acceleration). Besides performance, the average classification time is very competitive with respect to more complex CNN designs, thus allowing its usability also on medium power computers. In the next future we aim at improving the performances of the method along two directions: 1) by increasing the training dataset (as soon as other CT images will be available); 2) by introducing an efficient pre-processing strategy.

title: Predicting the number of reported and unreported cases for the COVID-19 epidemics in China, South Korea, Italy, France, Germany and United Kingdom
abstract: The novel coronavirus (SARS-CoV-2) is currently causing concern in the medical, epidemiological and mathematical communities as the virus is rapidly spreading around the world. Internationally, there are more than 1,200,000 cases detected and confirmed in the world on April 6. The asymptomatic and mild symptomatic cases are just going to be really crucial for us to understand what is driving this epidemic to transmit rapidly. Combining a mathematical model of severe (SARS-CoV-2) transmission with data from China, South Korea, Italy, France, Germany and United Kingdom, we provide the epidemic predictions of the number of reported and unreported cases for the SARS-CoV-2 epidemics and evaluate the effectiveness of control measures for each country.

title: Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients
abstract: Circulating in China and 158 other countries and areas, the ongoing COVID-19 outbreak has caused devastating mortality and posed a great threat to public health. However, efforts to identify effectively supportive therapeutic drugs and treatments has been hampered by our limited understanding of host immune response for this fatal disease. To characterize the transcriptional signatures of host inflammatory response to SARS-CoV-2 (HCoV-19) infection, we carried out transcriptome sequencing of the RNAs isolated from the bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) specimens of COVID-19 patients. Our results reveal distinct host inflammatory cytokine profiles to SARS-CoV-2 infection in patients, and highlight the association between COVID-19 pathogenesis and excessive cytokine release such as CCL2/MCP-1, CXCL10/IP-10, CCL3/MIP-1A, and CCL4/MIP1B. Furthermore, SARS-CoV-2 induced activation of apoptosis and P53 signalling pathway in lymphocytes may be the cause of patients' lymphopenia. The transcriptome dataset of COVID-19 patients would be a valuable resource for clinical guidance on anti-inflammatory medication and understanding the molecular mechansims of host response.

title: Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19
abstract: Despite the death rate of COVID-19 is less than 3%, the fatality rate of severe/critical cases is high, according to World Health Organization (WHO). Thus, screening the severe/critical cases before symptom occurs effectively saves medical resources.  In this study, all 336 cases of patients infected COVID-19 in Shanghai to March 12(th), were retrospectively enrolled, and divided in to training and test datasets. In addition, 220 clinical and laboratory observations/records were also collected. Clinical indicators were associated with severe/critical symptoms were identified and a model for severe/critical symptom prediction was developed.  Totally, 36 clinical indicators significantly associated with severe/critical symptom were identified. The clinical indicators are mainly thyroxine, immune related cells and products. Support Vector Machine (SVM) and optimized combination of age, GSH, CD3 ratio and total protein has a good performance in discriminating the mild and severe/critical cases. The area under receiving operating curve (AUROC) reached 0.9996 and 0.9757 in the training and testing dataset, respectively. When the using cut-off value as 0.0667, the recall rate was 93.33% and 100% in the training and testing datasets, separately. Cox multivariate regression and survival analyses revealed that the model significantly discriminated the severe/critical cases and used the information of the selected clinical indicators.  The model was robust and effective in predicting the severe/critical COVID cases.

title: COVID-19 and the coming epidemic in US immigration detention centres

title: CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images
abstract: In late 2019, a new Coronavirus disease, referred to as Corona virus disease 2019 (COVID-19), emerged in Wuhan city, Hubei, China, and resulted in a global pandemic---claiming a large number of lives and affecting billions all around the world. The current global standard used in diagnosis of COVID-19 in suspected cases is the real-time polymerase chain reaction (RT-PCR) test. Although the RT-PCR remains the standard reference for diagnosis purposes, it is a time-consuming and expensive test, and moreover, it usually suffers from high rates of false-negatives. Several early works have reported that the sensitivity of the chest Computed Tomography (CT) and the chest X-ray imaging are noticeably greater than that of the RT-PCR test at the initial representations of the disease, making them great candidates for developing new and sophisticated methodologies for analysis and classification of COVID-19 cases. In this paper, we establish the use of a rapid, non-invasive and cost-effective X-ray-based method as a key diagnosis and screening tool for COVID-19 at early and intermediate stages of the disease. To this end, we develop a novel and sophisticated deep learning-based signal and image processing technique as well as classification methodology for analyzing X-ray images specific to COVID-19 disease. Specifically, we consider a semi-supervised learning methodology based on AutoEncoders to first extract the infected legions in chest X-ray manifestation of COVID-19 and other Pneumonia-like diseases (as well as healthy cases). Then, we utilize this highly-tailored deep architecture to extract the relevant features specific to each class (i.e., healthy, non-COVID pneumonia, and COVID-19) and train a powerful yet efficient classifier to perform the task of automatic diagnosis. Furthermore, the semi-supervised nature of the proposed framework enables us to efficiently exploit the limited available dataset on COVID-19 while exploiting the vast amount of available X-ray dataset for healthy and non-COVID classes. Moreover, such a semi-supervised approach does not require an expert-annotated lesion area for each class. Our numerical investigations demonstrate that the proposed framework outperforms the state-of-the-art methods for COVID-19 identification while employing approximately ten times fewer training parameters as compared to other existing methodologies for classification of the COVID-19 from X-ray images (facilitating efficient training in a limited data regime). We further develop explainable artificial intelligence tools that can explain the diagnosis by using attribution maps while providing an indispensable tool for the radiologist in triage state. We have made the codes of our proposed framework publicly available to the research and healthcare community.

title: The Time for Universal Masking of the Public for Coronavirus Disease 2019 Is Now
abstract: In this perspective, we recommend universal masking of the US public during coronavirus disease 2019 due to the high contagiousness of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), viral shedding of viable SARS-CoV-2 from asymptomatic individuals, and the likely contribution of masking to core distancing public health strategies for curbing transmission.

title: COVID-19 Variants Database: A repository for Human SARS-CoV-2 Polymorphism Data
abstract: COVID-19 is a newly communicable disease with a catastrophe outbreak that affects all over the world. We retrieved about 8,781 nucleotide fragments and complete genomes of SARS-CoV-2 reported from sixty-four countries. The CoV-2 reference genome was obtained from the National Genomics Data Center (NGDC), GISAID, and NCBI Genbank. All the sequences were aligned against reference genomes using Clustal Omega and variants were called using in-house built Python script. We intend to establish a user-friendly online resource to visualize the variants in the viral genome along with the Primer Infopedia. After analyzing and filtering the data globally, it was made available to the public. The detail of data available to the public includes mutations from 5688 SARS-CoV-2 sequences curated from 91 regions. This database incorporated 39920 mutations over 3990 unique positions. According to the translational impact, these mutations include 11829 synonymous mutations including 681 synonymous frameshifts and 21701 nonsynonymous mutations including 10 nonsynonymous frameshifts. Development of SARS-CoV-2 mutation genome browsers is a fundamental step obliging towards the virus surveillance, viral detection, and development of vaccine and therapeutic drugs. The SARS-COV-2 mutation browser is available at http://covid-19.dnageography.com.

title: COVID-19 Mobility Data Collection of Seoul, South Korea
abstract: The relationship between pandemic and human mobility has received considerable attention from scholars, as investigating such relationship can provide an indication of how human mobility changes in response to a public health crisis or whether reduced mobility contributes to preventing the spread of an infectious disease. While several studies attempted to unveil this relationship, no studies have focused on changes in mobility pattern at a finer scale utilizing high-resolution datasets. To address the complex association between pandemic's spread and human mobility, this paper presents two categories of mobility datasets-trip mode and trip purpose-that concern nearly 10 million citizens' movements during the first 100 days of COVID-19 in Seoul, South Korea, where no major lockdown has been imposed. We curate hourly data of subway ridership, traffic volume and population present count at selected points of interests. The results to be derived from the presented datasets can be used as an important reference for public health decision making in the post COVID-19 era.

title: Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR
abstract: The ongoing outbreak of the recently emerged novel coronavirus (2019-nCoV) poses a challenge for public health laboratories as virus isolates are unavailable while there is growing evidence that the outbreak is more widespread than initially thought, and international spread through travellers does already occur.  We aimed to develop and deploy robust diagnostic methodology for use in public health laboratory settings without having virus material available.  Here we present a validated diagnostic workflow for 2019-nCoV, its design relying on close genetic relatedness of 2019-nCoV with SARS coronavirus, making use of synthetic nucleic acid technology.  The workflow reliably detects 2019-nCoV, and further discriminates 2019-nCoV from SARS-CoV. Through coordination between academic and public laboratories, we confirmed assay exclusivity based on 297 original clinical specimens containing a full spectrum of human respiratory viruses. Control material is made available through European Virus Archive – Global (EVAg), a European Union infrastructure project.  The present study demonstrates the enormous response capacity achieved through coordination of academic and public laboratories in national and European research networks.

title: Cross-data Automatic Feature Engineering via Meta-learning and Reinforcement Learning
abstract: Feature Engineering (FE) is one of the most beneficial, yet most difficult and time-consuming tasks of machine learning projects, and requires strong expert knowledge. It is thus significant to design generalized ways to perform FE. The primary difficulties arise from the multiform information to consider, the potentially infinite number of possible features and the high computational cost of feature generation and evaluation. We present a framework called Cross-data Automatic Feature Engineering Machine (CAFEM), which formalizes the FE problem as an optimization problem over a Feature Transformation Graph (FTG). CAFEM contains two components: a FE learner (FeL) that learns fine-grained FE strategies on one single dataset by Double Deep Q-learning (DDQN) and a Cross-data Component (CdC) that speeds up FE learning on an unseen dataset by the generalized FE policies learned by Meta-Learning on a collection of datasets. We compare the performance of FeL with several existing state-of-the-art automatic FE techniques on a large collection of datasets. It shows that FeL outperforms existing approaches and is robust on the selection of learning algorithms. Further experiments also show that CdC can not only speed up FE learning but also increase learning performance.

title: Revealing COVID-19 Transmission by SARS-CoV-2 Genome Sequencing and Agent Based Modelling
abstract: Community transmission of the new coronavirus SARS-CoV-2 is a major public health concern that remains difficult to assess. We present a genomic survey of SARS-CoV-2 from a during the first 10 weeks of COVID-19 activity in New South Wales, Australia. Transmission events were monitored prospectively during the critical period of implementation of national control measures. SARS-CoV-2 genomes were sequenced from 209 patients diagnosed with COVID-19 infection between January and March 2020. Only a quarter of cases appeared to be locally acquired and genomic-based estimates of local transmission rates were concordant with predictions from a computational agent-based model. This convergent assessment indicates that genome sequencing provides key information to inform public health action and has improved our understanding of the COVID-19 evolution from outbreak to epidemic.

title: EP News: Basic and Translational

title: Clinical Manifestations of Children with COVID-19: a Systematic Review
abstract: The coronavirus disease 2019 (COVID-19) outbreak is an unprecedented global public health challenge, leading to thousands of deaths every day worldwide. Despite the epidemiological importance, clinical patterns of children with COVID-19 remain unclear.  To describe the clinical, laboratorial and radiological characteristics of children with COVID-19.  The Medline database was searched between December 1st 2019 and March 30th 2020.  Inclusion criteria were: (1) studied patients younger than 18 years old; (2) presented original data from cases of COVID-19 confirmed by reverse-transcription polymerase chain reaction; and (3) contained descriptions of clinical manifestations, laboratory tests or radiological examinations.  Number of cases, gender, age, clinical manifestations, laboratory tests, radiological examinations and outcomes.  A total of 34 studies (1,118 cases) were included. From all the cases, 1,111 had their severity classified: 14.3% were asymptomatic, 36.4% were mild, 46.0% were moderate, 2.2% were severe and 1.2% were critical. The most prevalent symptom was fever (16.3%), followed by cough (14.4%), nasal symptoms (3.6%), diarrhea (2.7%) and nausea/vomiting (2.5%). One hundred forty-five (12.9%) children were diagnosed with pneumonia and 43 (3.8%) upper airway infections were reported. Reduced lymphocyte count were reported in 13.1% of cases. Abnormalities on computed tomography was reported in 62.7% of cases. The most prevalent abnormalities reported were ground glass opacities, patchy shadows and consolidations. Only one death was reported.  Clinical manifestations of children with COVID-19 differ widely from adults cases. Fever and respiratory symptoms should not be considered a hallmark of COVID-19 in children.

title: CO.ME.T.A. -- covid-19 media textual analysis. A dashboard for media monitoring
abstract: The focus of this paper is to trace how mass media, particularly newspapers, have addressed the issues about the containment of contagion or the explanation of epidemiological evolution. We propose an interactive dashboard: CO.ME.T.A.. During crises it is important to shape the best communication strategies in order to respond to critical situations. In this regard, it is important to monitor the information that mass media and social platforms convey. The dashboard allows to explore the mining of contents extracted and study the lexical structure that links the main discussion topics. The dashboard merges together four methods: text mining, sentiment analysis, textual network analysis and latent topic models. Results obtained on a subset of documents show not only a health-related semantic dimension, but it also extends to social-economic dimensions.

title: Dataset of Vietnamese student's learning habits during COVID-19
abstract: A dataset was constructed to examine Vietnamese student's learning habits during the time schools were suspended due to the novel coronavirus - SARS-CoV-2 (COVID-19), in response to a call for interdisciplinary research on the potential effects of the coronavirus pandemic (Elsevier, 2020). The questionnaires were spread over a network of educational communities on Facebook from February 7 to February 28, 2020. Using the snowball sampling method, researchers delivered the survey to teachers and parents to provide formal consent before they forwarded it to their students and children. In order to measure the influence of students' socioeconomic status and occupational aspirations on their learning habits during school closures, the survey included three major groups of questions: (1) Individual demographics, including family socioeconomic status, school type, and occupational aspirations; (2) Student's learning habits, including hours of learning before and during the period of school suspension, with and without other people's support; and (3) Students' perceptions of their self-learning during the school closures. There was a total of 920 clicks on the survey link, but only 460 responses accompanied by consent forms were received. Non-credible answers (e.g., year of birth after 2009, more than 20 hours of learning per day) were eliminated. The final dataset included 420 valid observations.

title: Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set
abstract: At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter.  In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository.  We started this ongoing data collection on January 28, 2020, leveraging Twitter's streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter's search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020.  Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events.  It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic-and undoubtedly more.

title: Initial viral load and the outcomes of SARS.
abstract: Severe acute respiratory syndrome (SARS) is caused by a novel coronavirus. It may progress to respiratory failure, and a significant proportion of patients die. Preliminary data suggest that a high viral load of the SARS coronavirus is associated with adverse outcomes in the intensive care unit, but the relation of viral load to survival is unclear.  We prospectively studied an inception cohort of 133 patients with virologically confirmed SARS who were admitted to 2 general acute care hospitals in Hong Kong from Mar. 24 to May 4, 2003. The patients were followed until death or for a minimum of 90 days. We used Cox proportional hazard modelling to analyze potential predictors of survival recorded at the time of presentation, including viral load from nasopharyngeal specimens (measured by quantitative reverse transcriptase polymerase chain reaction [PCR] of the SARS-associated coronavirus).  Thirty-two patients (24.1%) met the criteria for acute respiratory distress syndrome, and 24 patients (18.0%) died. The following baseline factors were independently associated with worse survival: older age (61-80 years) (adjusted hazard ratio [HR] 5.24, 95% confidence interval [CI] 2.03-13.53), presence of an active comorbid condition (adjusted HR 3.36, 95% CI 1.44-7.82) and higher initial viral load of SARS coronavirus, according to quantitative PCR of nasopharyngeal specimens (adjusted HR 1.21 per log10 increase in number of RNA copies per millilitre, 95% CI 1.06-1.39).  We found preliminary evidence that higher initial viral load is independently associated with worse prognosis in SARS. Mortality data for patients with SARS should be interpreted in light of age, comorbidity and viral load. These considerations will be important in future studies of SARS.

title: Temporal and spatial evolution of online public sentiment on emergencies
abstract: The transmission of online emergency information has become an active means of expressing public opinion and has vitally affected societal emergency response techniques. This paper analyzes interactions between three groups in time and space using a classic SIR (susceptible, infected, and recovered) epidemic model. Through social network theory and analog simulation analysis, we utilize data from China's Sina Weibo (a popular social media platform) to conduct empirical research on 101 major incidents in China that occurred between 2010 and 2017. We divide these emergencies into four types—natural disasters, accidents, public health events, and social security events—and conduct a simulation using three examples from each group. The results show that government control of public opinion is both cheaper and more effective when it occurs at the initial stages of an incident. By cooperating with the government, the media can facilitate emergency management. Finally, if netizens trust the government and the media, they are more likely to make cooperative decisions, maintain interest, and improve the management of online public sentiment.

title: Scoping Review on Search Queries and Social Media for Disease Surveillance: A Chronology of Innovation
abstract: The threat of a global pandemic posed by outbreaks of influenza H5N1 (1997) and Severe Acute Respiratory Syndrome (SARS, 2002), both diseases of zoonotic origin, provoked interest in improving early warning systems and reinforced the need for combining data from different sources. It led to the use of search query data from search engines such as Google and Yahoo! as an indicator of when and where influenza was occurring. This methodology has subsequently been extended to other diseases and has led to experimentation with new types of social media for disease surveillance.  The objective of this scoping review was to formally assess the current state of knowledge regarding the use of search queries and social media for disease surveillance in order to inform future work on early detection and more effective mitigation of the effects of foodborne illness.  Structured scoping review methods were used to identify, characterize, and evaluate all published primary research, expert review, and commentary articles regarding the use of social media in surveillance of infectious diseases from 2002-2011.  Thirty-two primary research articles and 19 reviews and case studies were identified as relevant. Most relevant citations were peer-reviewed journal articles (29/32, 91%) published in 2010-11 (28/32, 88%) and reported use of a Google program for surveillance of influenza. Only four primary research articles investigated social media in the context of foodborne disease or gastroenteritis. Most authors (21/32 articles, 66%) reported that social media-based surveillance had comparable performance when compared to an existing surveillance program. The most commonly reported strengths of social media surveillance programs included their effectiveness (21/32, 66%) and rapid detection of disease (21/32, 66%). The most commonly reported weaknesses were the potential for false positive (16/32, 50%) and false negative (11/32, 34%) results. Most authors (24/32, 75%) recommended that social media programs should primarily be used to support existing surveillance programs.  The use of search queries and social media for disease surveillance are relatively recent phenomena (first reported in 2006). Both the tools themselves and the methodologies for exploiting them are evolving over time. While their accuracy, speed, and cost compare favorably with existing surveillance systems, the primary challenge is to refine the data signal by reducing surrounding noise. Further developments in digital disease surveillance have the potential to improve sensitivity and specificity, passively through advances in machine learning and actively through engagement of users. Adoption, even as supporting systems for existing surveillance, will entail a high level of familiarity with the tools and collaboration across jurisdictions.

title: Expression of ACE2, the SARS-CoV-2 receptor, and TMPRSS2 in prostate epithelial cells
abstract: The COVID-19 pandemic has spread across more than 200 countries and resulted in over 170,000 deaths. For unclear reasons, higher mortality rates from COVID-19 have been reported in men compared to women. While the SARS-CoV-2 receptor ACE2 and serine protease TMPRSS2 have been detected in lung and other tissues, it is not clear what sex differences may exist. We analyzed a publicly-available normal human prostate single-cell RNA sequencing dataset and found TMPRSS2 and ACE2 co-expressing cells in epithelial cells, with a higher proportion in club and hillock cells. Then we investigated datasets of lung epithelial cells and also found club cells co-expressing TMPRSS2 and ACE2. A comparison of ACE2 expression in lung tissue between males and females showed higher expression in males and a larger proportion of ACE2+ cells in male type II pneumocytes, with preliminary evidence that type II pneumocytes of all lung epithelial cell types showed the highest expression of ACE2. These results raise the possibility that sex differences in ACE2 expression and the presence of double-positive cells in the prostate may contribute to the observed disparities of COVID-19.

title: Rampant C->U hypermutation in the genomes of SARS-CoV-2 and other coronaviruses – causes and consequences for their short and long evolutionary trajectories
abstract: The pandemic of SARS coronavirus 2 (SARS-CoV-2) has motivated an intensive analysis of its molecular epidemiology following its worldwide spread. To understand the early evolutionary events following its emergence, a dataset of 985 complete SARS-CoV-2 sequences was assembled. Variants showed a mean 5.5-9.5 nucleotide differences from each other, commensurate with a mid-range coronavirus substitution rate of 3×10−4 substitutions/site/year. Almost half of sequence changes were C->U transitions with an 8-fold base frequency normalised directional asymmetry between C->U and U->C substitutions. Elevated ratios were observed in other recently emerged coronaviruses (SARS-CoV and MERS-CoV) and to a decreasing degree in other human coronaviruses (HCoV-NL63, -OC43, -229E and -HKU1) proportionate to their increasing divergence. C->U transitions underpinned almost half of the amino acid differences between SARS-CoV-2 variants, and occurred preferentially in both 5’U/A and 3’U/A flanking sequence contexts comparable to favoured motifs of human APOBEC3 proteins. Marked base asymmetries observed in non-pandemic human coronaviruses (U>>A>G>>C) and low G+C contents may represent long term effects of prolonged C->U hypermutation in their hosts.  The evidence that much of sequence change in SARS-CoV-2 and other coronaviruses may be driven by a host APOBEC-like editing process has profound implications for understanding their short and long term evolution. Repeated cycles of mutation and reversion in favoured mutational hotspots and the widespread occurrence of amino acid changes with no adaptive value for the virus represents a quite different paradigm of virus sequence change from neutral and Darwinian evolutionary frameworks that are typically used in molecular epidemiology investigations.

title: Covid-19: Pandemonium in our time
abstract: While pandemonium has come to mean wild and noisy disorder, the reference here is to John Milton's epic poem Paradise Lost and the upheaval following Lucifer's banishment from Heaven and his construction of Pandæmonium as his hub. Today's avalanche of conflicting news on how to deal with the coronavirus disease 2019 (Covid-19) brings to mind the Trinity nuclear bomb test with Enrico Fermi estimating its strength by releasing small pieces of paper into the air and measuring their displacement by the shock wave. Fermi's result, in fact not far from the true value, emphasised his ability to make good approximations with few or no actual data. The current wave of Covid-19 presents just this kind of situation as it engulfs the world from ground zero in Wuhan, China. Much information is indeed missing, but datasets that might lead to useful ideas on how to handle this pandemic are steadily accumulating.

title: Conserved antigenic sites between MERS-CoV and Bat-coronavirus are revealed through sequence analysis
abstract: MERS-CoV is a newly emerged human coronavirus reported closely related with HKU4 and HKU5 Bat coronaviruses. Bat and MERS corona-viruses are structurally related. Therefore, it is of interest to estimate the degree of conserved antigenic sites among them. It is of importance to elucidate the shared antigenic-sites and extent of conservation between them to understand the evolutionary dynamics of MERS-CoV.  Multiple sequence alignment of the spike (S), membrane (M), enveloped (E) and nucleocapsid (N) proteins was employed to identify the sequence conservation among MERS and Bat (HKU4, HKU5) coronaviruses. We used various in silico tools to predict the conserved antigenic sites. We found that MERS-CoV shared 30 % of its S protein antigenic sites with HKU4 and 70 % with HKU5 bat-CoV. Whereas 100 % of its E, M and N protein’s antigenic sites are found to be conserved with those in HKU4 and HKU5.  This sharing suggests that in case of pathogenicity MERS-CoV is more closely related to HKU5 bat-CoV than HKU4 bat-CoV. The conserved epitopes indicates their evolutionary relationship and ancestry of pathogenicity.

title: COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19
abstract: The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer from lung infection, so one important diagnostic method is to screen chest radiography images, e.g., X-Ray or CT images. However, such examinations are time-consuming and labor-intensive, leading to limited diagnostic efficiency. To solve this issue, AI-based technologies, such as deep learning, have been used recently as effective computer-aided means to improve diagnostic efficiency. However, one practical and critical difficulty is the limited availability of annotated COVID-19 data, due to the prohibitive annotation costs and urgent work of doctors to fight against the pandemic. This makes the learning of deep diagnosis models very challenging. To address this, motivated by that typical pneumonia has similar characteristics with COVID-19 and many pneumonia datasets are publicly available, we propose to conduct domain knowledge adaptation from typical pneumonia to COVID-19. There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19. To address them, we propose a new deep domain adaptation method for COVID-19 diagnosis, namely COVID-DA. Specifically, we alleviate the domain discrepancy via feature adversarial adaptation and handle the task difference issue via a novel classifier separation scheme. In this way, COVID-DA is able to diagnose COVID-19 effectively with only a small number of COVID-19 annotations. Extensive experiments verify the effectiveness of COVID-DA and its great potential for real-world applications.

title: Coronavirus disease 2019: International public health considerations
abstract: On December 31, 2019, the Chinese government announced an outbreak of a novel coronavirus, recently named COVID‐19. During the following weeks the international medical community has witnessed with unprecedented coverage the public health response both domestically by the Chinese government, and on an international scale as cases have spread to dozens of countries. While much regarding the virus and the Chinese public health response is still unknown, national and public health institutions globally are preparing for a pandemic. As cases and spread of the virus grow, emergency and other front‐line providers may become more anxious about the possibility of encountering a potential case. This review describes the tenets of a public health response to an infectious outbreak by using recent historical examples and also by characterizing what is known about the ongoing response to the COVID‐19 outbreak. The intent of the review is to empower the practitioner to monitor and evaluate the local, national and global public health response to an emerging infectious disease.

title: Indian Publications on SARS-CoV-2: A Bibliometric Study of WHO COVID-19 Database
abstract: Nowadays, the whole World is under threat of Coronavirus disease (COVID-19). The ongoing COVID-19 pandemic has resulted in many fatalities and forced scientific communities to foster their Research and Development (R & D) activities. As a result, there is an enormous growth of scholarly literature on the subject. In order to combat this novel coronavirus, the open access to scientific literature is essential. On this line, many reputed academic institutions and publication firms have made their literature on COVID-19 accessible to all. By maintaining the database of updated information on global literature on Coronavirus disease, the World Health Organization (WHO) is playing a pivotal role. The present study analyzed 89 Indian publications on SARS-CoV-2 accessible through WHO COVID-19 database. The research data was restricted for the period of 2/3/2020 to 12/5/2020. The analysis was carried out in light of the objectives of the study. The study found the considerable and constant growth of Indian publications on COVID-19 from mid-April. It is interesting to note that the prolific authors belong to either AIIMS or ICMR institutes. Majority of the COVID-19 articles were found to be collaborative publications. The study noticed that no research publications on COVID-19 have appeared from North Eastern region. Regarding the research output on COVID-19, the performance of largest states like Uttar Pradesh, Madhya Pradesh and Bihar was found to be poor. Delhi state contributed highest publications on COVID-19. The All India Institute of Medical Sciences (AIIMS), New Delhi was the most productive institution in terms of publications. It is also important to note that the central government undertakings like AIIMS and ICMR, New Delhi and its affiliated institutions shared largest proportion of publications on COVID-19. The Indian Journal of Medical Research has emerged as the productive journal contributing highest number of the publications. The highest contribution in COVID-19 research takes the form of journal articles. In terms of research area, the majority of the publications were related to Epidemiology. The study reported covid, coronavirus, India, pandemic, sars etc. as the frequently occurred keywords in the COVID-19 publications. The highly cited publications were of evidenced based studies. It is observed that the studies pertaining to virology, diagnosis and treatment, clinical features etc. have received highest citations than general studies on epidemiology or pandemic

title: Consideration of the Aerosol Transmission for COVID‐19 and Public Health
abstract: This article analyzes the available evidence to address airborne, aerosol transmission of the SARS‐CoV‐2. We review and present three lines of evidence: case reports of transmission for asymptomatic individuals in association with studies that show that normal breathing and talking produce predominantly small droplets of the size that are subject to aerosol transport; limited empirical data that have recorded aerosolized SARS‐CoV‐2 particles that remain suspended in the air for hours and are subject to transport over distances including outside of rooms and intrabuilding, and the broader literature that further supports the importance of aerosol transmission of infectious diseases. The weight of the available evidence warrants immediate attention to address the significance of aerosols and implications for public health protection.

title: World Federation for Ultrasound in Medicine and Biology Position Statement: How to Perform a Safe Ultrasound Examination and Clean Equipment in the Context of COVID-19

title: Clinical and pathological characteristics of 2019 novel coronavirus disease (COVID-19): a systematic review
abstract: In 2002-2003, a severe pulmonary infectious disease occurred in Guangdong, China. The disease was caused by SARS-CoV, 17 years apart, also happen in China, and SARS-CoV-2, this epidemic has posed a significant hazard to people health both China and the whole world.  Summarized the latest epidemiological changes, clinical manifestations, auxiliary examination and pathological characteristics of COVID-19.  PubMed database were searched from 2019 to 2020 using the index terms novel coronavirus or COVID-19 or 2019-nCoV or SARS-CoV-2 and synonyms. Articles that reported clinical characteristics, laboratory results, imageological diagnosis and pathologic condition were included and were retrospectively reviewed for these cases. This paper adopts the method of descriptive statistics.  34 COVID-19-related articles were eligible for this systematic review;Four of the articles were related to pathology. We found that Fever (86.0%), cough (63.9%) and Malaise/Fatigue (34.7%) were the most common symptoms in COVID-19. But in general, the clinical symptoms and signs of COVID-19 were not obvious. Compared with SARS, COVID-19 was transmitted in a more diverse way. The mortality rates of COVID-19 were 2.5%, and the overall infection rate of healthcare worker of COVID-19 was 3.9%. We also found that the pathological features of COVID-19 have greatly similar with SARS, which manifested as ARDS. But the latest pathological examination of COVID-19 revealed the obvious mucinous secretions in the lungs.  The clinical and pathological characteristics of SARS and COVID-19 in China are very similar, but also difference. The latest finds of pathological examination on COVID-19 may upend existing treatment schemes, so the early recognition of disease by healthcare worker is very important.

title: A County-level Dataset for Informing the United States' Response to COVID-19
abstract: As the coronavirus disease 2019 (COVID-19) becomes a global pandemic, policy makers must enact interventions to stop its spread. Data driven approaches might supply information to support the implementation of mitigation and suppression strategies. To facilitate research in this direction, we present a machine-readable dataset that aggregates relevant data from governmental, journalistic, and academic sources on the county level. In addition to county-level time-series data from the JHU CSSE COVID-19 Dashboard, our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and in come, climate, transit scores, and healthcare system-related metrics. Furthermore, we present aggregated out-of-home activity information for various points of interest for each county, including grocery stores and hospitals, summarizing data from SafeGraph. By collecting these data, as well as providing tools to read them, we hope to aid researchers investigating how the disease spreads and which communities are best able to accommodate stay-at-home mitigation efforts. Our dataset and associated code are available at https://github.com/JieYingWu/COVID-19_US_County-level_Summaries.

title: Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19
abstract: Fractional calculus provides a promising tool for modeling fractional dynamics in computational biology, and this study tests the applicability of fractional-derivative equations (FDEs) for modeling the dynamics and mitigation scenarios of the novel coronavirus for the first time. The coronavirus disease 2019 (COVID-19) pandemic radically impacts our lives, while the evolution dynamics of COVID-19 remain obscure. A time-dependent Susceptible, Exposed, Infectious, and Recovered (SEIR) model was proposed and applied to fit and then predict the time series of COVID-19 evolution observed over the last three months (up to 3/22/2020) in China. The model results revealed that 1) the transmission, infection and recovery dynamics follow the integral-order SEIR model with significant spatiotemporal variations in the recovery rate, likely due to the continuous improvement of screening techniques and public hospital systems, as well as full city lockdowns in China, and 2) the evolution of number of deaths follows the time FDE, likely due to the time memory in the death toll. The validated SEIR model was then applied to predict COVID-19 evolution in the United States, Italy, Japan, and South Korea. In addition, a time FDE model based on the random walk particle tracking scheme, analogous to a mixing-limited bimolecular reaction model, was developed to evaluate non-pharmaceutical strategies to mitigate COVID-19 spread. Preliminary tests using the FDE model showed that self-quarantine may not be as efficient as strict social distancing in slowing COVID-19 spread. Therefore, caution is needed when applying FDEs to model the coronavirus outbreak, since specific COVID-19 kinetics may not exhibit nonlocal behavior. Particularly, the spread of COVID-19 may be affected by the rapid improvement of health care systems which may remove the memory impact in COVID-19 dynamics (resulting in a short-tailed recovery curve), while the death toll and mitigation of COVID-19 can be captured by the time FDEs due to the nonlocal, memory impact in fatality and human activities.

title: First genome sequences of buffalo coronavirus from water buffaloes in Bangladesh
abstract: We report the complete genome sequences of a buffalo coronavirus (BufCoV HKU26) detected from the faecal samples of two domestic water buffaloes (Bubalus bubalis) in Bangladesh. They possessed 98–99% nucleotide identities to bovine coronavirus (BCoV) genomes, supporting BufCoV HKU26 as a member of Betacoronavirus 1. Nevertheless, BufCoV HKU26 possessed distinct accessory proteins between spike and envelope compared to BCoV. Sugar-binding residues in the N-terminal domain of S protein in BCoV are conserved in BufCoV HKU26.

title: Core Minimal Datasets to Advance Clinical Research for Priority Epidemic Diseases
abstract: The Ebola virus disease outbreak in west Africa has prompted significant progress in responding to the clinical needs of patients affected by emerging infectious disease outbreaks. Among the noteworthy successes of vaccine trials, and the commendable efforts to implement clinical treatment trials during Ebola outbreaks, we should also focus on strengthening the collection and curation of epidemiological and observational data that can improve the conception and design of clinical research.

title: Tree-Miner: Mining Sequential Patterns from SP-Tree
abstract: Data mining is used to extract actionable knowledge from huge amount of raw data. In numerous real life applications, data are stored in sequential form, hence mining sequential patterns has been one of the most popular fields in data mining. Due to its various applications, across the past decades, a significant number of literature have addressed this problem and provided elegant solutions. In this paper we propose a novel tree data structure, SP-Tree, to store the sequence database in a new and efficient manner. Additionally, we propose a new mining algorithm Tree-miner to mine sequential patterns from SP-Tree. To further enhance the performance of our algorithm, we incorporate multiple pruning techniques and optimizations. As our SP-Tree stores the complete database, it can also be used for incremental and dynamic databases, tree-structure is particularly advantageous for interactive mining. We demonstrate how our SP-Tree based Tree-miner algorithm significantly outperforms all of the existing state-of-the-art algorithms, across 6 real life datasets. We conclude by discussing the possible extensions of our approach to other related fields of sequential data.

title: A call for governments to pause Twitter censorship: a cross-sectional study using Twitter data as social-spatial sensors of COVID-19/SARS-CoV-2 research diffusion
abstract: To determine whether Twitter data can be used as social-spatial sensors to show how research on COVID-19/SARS-CoV-2 diffuses through the population to reach the people that are especially affected by the disease.   Three sources of data were used in the analysis: (1) deaths per number of population for COVID-19/SARS-CoV-2 retrieved from Coronavirus Resource Center at John Hopkins University and Worldometer, (2) publications related to COVID-19/SARS-CoV-2 retrieved from WHO COVID-19 database of global publications, and (3) tweets of these publications retrieved from Altmetric.com and Twitter.  To map Twitter activity against number of publications and deaths per number of population worldwide and in the USA states. To determine the relationship between number of tweets as dependent variable and deaths per number of population and number of publications as independent variables.  Deaths per one hundred thousand population for countries ranged from 0 to 104, and deaths per one million population for USA states ranged from 2 to 513. Total number of publications used in the analysis was 1761, and total number of tweets used in the analysis was 751,068. Mapping of worldwide data illustrated that high Twitter activity was related to high numbers of COVID-19/SARS-CoV-2 deaths, with tweets inversely weighted with number of publications. Poisson regression models of worldwide data showed a positive correlation between the national deaths per number of population and tweets when holding the country's number of publications constant (coefficient 0.0285, S.E. 0.0003, p<0.001). Conversely, this relationship was negatively correlated in USA states (coefficient -0.0013, S.E. 0.0001, p<0.001).  This study shows that Twitter can play a crucial role in the rapid research response during the COVID-19/SARS-CoV-2 global pandemic, especially to spread research with prompt public scrutiny. Governments are urged to pause censorship of social media platforms during these unprecedented times to support the scientific community's fight against COVID-19/SARS-CoV-2.

title: Analyzing the epidemiological outbreak of COVID‐19: A visual exploratory data analysis approach
abstract: There is an obvious concern globally regarding the fact about the emerging coronavirus 2019 novel coronavirus (2019‐nCoV) as a worldwide public health threat. As the outbreak of COVID‐19 causes by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) progresses within China and beyond, rapidly available epidemiological data are needed to guide strategies for situational awareness and intervention. The recent outbreak of pneumonia in Wuhan, China, caused by the SARS‐CoV‐2 emphasizes the importance of analyzing the epidemiological data of this novel virus and predicting their risks of infecting people all around the globe. In this study, we present an effort to compile and analyze epidemiological outbreak information on COVID‐19 based on the several open datasets on 2019‐nCoV provided by the Johns Hopkins University, World Health Organization, Chinese Center for Disease Control and Prevention, National Health Commission, and DXY. An exploratory data analysis with visualizations has been made to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China. Overall, at the outset of an outbreak like this, it is highly important to readily provide information to begin the evaluation necessary to understand the risks and begin containment activities.

title: Real time estimation of the risk of death from novel coronavirus (2019-nCoV) infection: Inference using exported cases
abstract: The exported cases of 2019 novel coronavirus (2019-nCoV) infection who were confirmed in other countries provide a chance to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in China. Knowledge of the cCFR is critical to characterize the severity and understand pandemic potential of 2019-nCoV in the early stage of epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number, i.e., the average number of secondary cases generated by a single primary case in a naive population. As of 24 January 2020, with 23 exported cases, and estimating the growth rate from 8 December 2019 (scenario 1) and using the data since growth of exported cases (scenario 2), the cumulative incidence in China was estimated at 5433 cases (95% confidence interval (CI): 3883, 7160) and 17780 cases (95% CI: 9646, 28724), respectively. The latest estimates of the cCFR were 4.6% (95% CI: 3.1-6.6) for scenario 1 and 7.7% (95% CI: 4.9-11.3%) for scenario 2, respectively. The basic reproduction number was estimated to be 2.2 (95% CI: 2.1, 2.3) and 3.7 (95% CI: 3.1, 4.3) for scenarios 1 and 2, respectively. Based on the results, we note that current 2019-nCoV epidemic has a substation potential to cause a pandemic. The proposed approach can provide insights into early risk assessment using only publicly available data.

title: Letter to the Editor Re: Coronavirus disease 2019: The harms of exaggerated information and nonevidence-based measures

title: DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images
abstract: Amid the coronavirus disease(COVID-19) pandemic, humanity experiences a rapid increase in infection numbers across the world. Challenge hospitals are faced with, in the fight against the virus, is the effective screening of incoming patients. One methodology is the assessment of chest radiography(CXR) images, which usually requires expert radiologist's knowledge. In this paper, we propose an explainable deep neural networks(DNN)-based method for automatic detection of COVID-19 symptoms from CXR images, which we call DeepCOVIDExplainer. We used 15,959 CXR images of 15,854 patients, covering normal, pneumonia, and COVID-19 cases. CXR images are first comprehensively preprocessed, before being augmented and classified with a neural ensemble method, followed by highlighting class-discriminating regions using gradient-guided class activation maps(Grad-CAM++) and layer-wise relevance propagation(LRP). Further, we provide human-interpretable explanations of the predictions. Evaluation results based on hold-out data show that our approach can identify COVID-19 confidently with a positive predictive value(PPV) of 91.6%, 92.45%, and 96.12%; precision, recall, and F1 score of 94.6%, 94.3%, and 94.6%, respectively for normal, pneumonia, and COVID-19 cases, respectively, making it comparable or improved results over recent approaches. We hope that our findings will be a useful contribution to the fight against COVID-19 and, in more general, towards an increasing acceptance and adoption of AI-assisted applications in the clinical practice.

title: Sars‐CoV‐2 in the context of past coronaviruses epidemics: Consideration for prenatal care
abstract: Since December 2019, the novel SARS‐CoV‐2 outbreak has resulted in millions of cases and more than 200,000 deaths worldwide. The clinical course among non‐pregnant women has been described but data about potential risks for women and their fetus remain scarce. The SARS and MERS epidemics were responsible for miscarriages, adverse fetal and neonatal outcomes and maternal deaths. For COVID‐19 infection, only 9 cases of maternal death have been reported as of April 22, 2020 and pregnant women seem to develop the same clinical presentation as the general population. However, severe maternal cases, as well as prematurity, fetal distress and stillbirth among newborns have been reported. The SARS‐CoV‐2 pandemic greatly impacts prenatal management and surveillance and raise the need for clear unanimous guidelines. In this narrative review, we describe the current knowledge about coronaviruses (SARS, MERS and SARS‐CoV‐2) risks and consequences on pregnancies and we summarize available current candidate therapeutic options for pregnant women. Finally, we compare current guidance proposed by RCOG, ACOG and the WHO to give an overview of prenatal management which should be utilized until future data appear. This article is protected by copyright. All rights reserved.

title: Potential Fecal Transmission of SARS-CoV-2: Current Evidence and Implications for Public Health
abstract: Coronavirus disease 2019 (COVID-19) emerged in Hubei Province, China in December 2019 and has since become a global pandemic, with hundreds of thousands of cases and over 165 affected countries. Primary routes of transmission of the causative virus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), are through respiratory droplets and close person-to-person contact. While information about other potential modes of transmission are relatively sparse, evidence supporting the possibility of a fecally-mediated mode of transmission has been accumulating. Here, current knowledge on the potential for fecal transmission is briefly reviewed and the possible implications are discussed from a public health perspective.

title: A dataset for emotional reactions and family resilience during COVID-19 isolation period among Indonesian families
abstract: This study presents a survey dataset describing families’ conditions during the COVID-19 isolation period obtained from individuals who serve as parents. A survey was conducted to measure the family's positive or negative emotional reactions and the degree of their resilience. The data were categorized into age, sex, type of family, family size, length of marriage, family's environment, and family COVID-19 status. The samples were gathered from 365 parents of Indonesian students who were willing to fill an online questionnaire. Further, the data analysis involved demographic sample, descriptive statistics, median test, Kruskal-Wallis, and an inter-variable correlation.

title: Personal protective equipment needs in the USA during the COVID-19 pandemic

title: Domain Adaptation in Highly Imbalanced and Overlapping Datasets
abstract: In many machine learning domains, datasets are characterized by highly imbalanced and overlapping classes. Particularly in the medical domain, a specific list of symptoms can be labeled as one of various different conditions. Some of these conditions may be more prevalent than others by several orders of magnitude. Here we present a novel unsupervised domain adaptation scheme for such datasets. The scheme, based on a specific type of Quantification, is designed to work under both label and conditional shifts. It is demonstrated on datasets generated from electronic health records and provides high quality results for both Quantification and Domain Adaptation in very challenging scenarios. Potential benefits of using this scheme in the current COVID-19 outbreak, for estimation of prevalence and probability of infection are discussed.

title: Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences
abstract: Predicting the hosts of newly discovered viruses is important for pandemic surveillance of infectious diseases. We investigated the use of alignment-based and alignment-free methods and support vector machine using mononucleotide frequency and dinucleotide bias to predict the hosts of viruses, and applied these approaches to three datasets: rabies virus, coronavirus, and influenza A virus. For coronavirus, we used the spike gene sequences, while for rabies and influenza A viruses, we used the more conserved nucleoprotein gene sequences. We compared the three methods under different scenarios and showed that their performances are highly correlated with the variability of sequences and sample size. For conserved genes like the nucleoprotein gene, longer k-mers than mono- and dinucleotides are needed to better distinguish the sequences. We also showed that both alignment-based and alignment-free methods can accurately predict the hosts of viruses. When alignment is difficult to achieve or highly time-consuming, alignment-free methods can be a promising substitute to predict the hosts of new viruses.

title: Sharing health research data – the role of funders in improving the impact
abstract: Recent public health emergencies with outbreaks of influenza, Ebola and Zika revealed that the mechanisms for sharing research data are neither being used, or adequate for the purpose, particularly where data needs to be shared rapidly. A review of research papers, including completed clinical trials related to priority pathogens, found only 31% (98 out of 319 published papers, excluding case studies) provided access to all the data underlying the paper - 65% of these papers give no information on how to find or access the data. Only two clinical trials out of 58 on interventions for WHO priority pathogens provided any link in their registry entry to the background data. Interviews with researchers revealed a reluctance to share data included a lack of confidence in the utility of the data; an absence of academic-incentives for rapid dissemination that prevents subsequent publication and a disconnect between those who are collecting the data and those who wish to use it quickly. The role of the funders of research needs to change to address this. Funders need to engage early with the researchers and related stakeholders to understand their concerns and work harder to define the more explicitly the benefits to all stakeholders. Secondly, there needs to be a direct benefit to sharing data that is directly relevant to those people that collect and curate the data. Thirdly more work needs to be done to realise the intent of making data sharing resources more equitable, ethical and efficient. Finally, a checklist of the issues that need to be addressed when designing new or revising existing data sharing resources should be created. This checklist would highlight the technical, cultural and ethical issues that need to be considered and point to examples of emerging good practice that can be used to address them.

title: Spatiotemporal fluctuation scaling law and metapopulation modeling of the novel coronavirus (COVID-19) and SARS outbreaks
abstract: We comparatively analyzed the spatiotemporal fluctuations of the 2019-novel coronavirus (COVID-19) and SARS outbreaks to understand their epidemiological characteristics. Methodologically, we introduced TPL (Taylor power law) to characterize their spatiotemporal heterogeneity/stability and Hubbell (2001) unified neutral theory of biodiversity (UNTB) [specifically Harris et al. (2015) HDP-MSN model (hierarchical Dirichlet process multi-site neutral model)] to approximate the metapopulation of coronavirus infections. First, TPL analysis suggested that the coronaviruses appear to have a specific heterogeneity/stability scaling parameter (TPL-b) slightly exceeding 2 for cumulative infections or exceeding 1 for daily incremental infections, suggesting their potentially chaotic, unstable outbreaks. Another TPL parameter (M0) (i.e., infection critical threshold) depends on virus kinds (COVID-19/SARS), time (disease-stages), space (regions) and public-health interventions (e.g., quarantines and mobility control). M0 measures the infection level, at which infections are random (Poisson distribution) and below which infections follow uniform distribution and may die off if M0 coincides or below the level of Allee effects. It was found that COVID-19 outbreak seems nearly twice more risky than SARS, and the lower infection threshold may be due to its lower lethality than SARS since lower fatality rates can facilitate the survival and spread of pathogen. Second, metacommunity UNTB neutrality testing seems appropriate for approximating metapopulation of coronavirus infections. Specifically, two parameters {\theta} and M, borrowed from neutral theory, may be used to assess the relative significance of infection through local contagion vs. infection through migration, both of which may depend on time, space, virus kinds, and particularly public-health interventions.

title: Cryo-EM structure of infectious bronchitis coronavirus spike protein reveals structural and functional evolution of coronavirus spike proteins
abstract: As cell-invading molecular machinery, coronavirus spike proteins pose an evolutionary conundrum due to their high divergence. In this study, we determined the cryo-EM structure of avian infectious bronchitis coronavirus (IBV) spike protein from the γ-genus. The trimeric IBV spike ectodomain contains three receptor-binding S1 heads and a trimeric membrane-fusion S2 stalk. While IBV S2 is structurally similar to those from the other genera, IBV S1 possesses structural features that are unique to different other genera, thereby bridging these diverse spikes into an evolutionary spectrum. Specifically, among different genera, the two domains of S1, the N-terminal domain (S1-NTD) and C-terminal domain (S1-CTD), diverge from simpler tertiary structures and quaternary packing to more complex ones, leading to different functions of the spikes in receptor usage and membrane fusion. Based on the above structural and functional comparisons, we propose that the evolutionary spectrum of coronavirus spikes follows the order of α-, δ-, γ-, and β-genus. This study has provided insight into the evolutionary relationships among coronavirus spikes and deepened our understanding of their structural and functional diversity.

title: Extracting and categorising the reactions to COVID-19 by the South African public -- A social media study
abstract: Social Media can be used to extract discussion topics during a disaster. With the COVID-19 pandemic impact on South Africa, we need to understand how the law and regulation promulgated by the government in response to the pandemic contrasts with discussion topics social media users have been engaging in. In this work, we expand on traditional media analysis by using Social Media discussions driven by or directed to South African government officials. We find topics that are similar as well as different in some cases. The findings can inform further study into social media during disaster settings in South Africa and beyond.

title: Plasma Metabolomic and Lipidomic Alterations Associated with COVID-19
abstract: The pandemic of the coronavirus disease 2019 (COVID-19) has become a global public health crisis. COVID-19 is marked by its rapid progression from mild to severe conditions, particularly in the absence of adequate medical care. However, the physiological changes associated with COVID-19 are barely understood. In this study, we performed untargeted metabolomic and lipidomic analyses of plasma from a cohort of COVID-19 patients who had experienced different symptoms. We found the metabolite and lipid alterations exhibit apparent correlation with the course of disease in these COVID-19 patients, indicating that the development of COVID-19 affected patient metabolism. Moreover, many of the metabolite and lipid alterations, particularly ones associated with hepatic functions, have been found to align with the progress and severity of COVID-19. This work provides valuable knowledge about blood biomarkers associated with COVID-19 and potential therapeutic targets, and presents important resource for further studies of COVID-19 pathogenesis.

title: Global research trend in the treatment of the new Coronavirus diseases (COVID-19) : bibliometric analysis.
abstract: The Coronavirus 2019 (COVID-19) pandemic has caused worldwide concern and has become a major medical problem. Vaccines and therapeutics are important interventions for the management of this outbreak. This study aims to used bibliometric methods to identify research trends in the domain of therapeutics and vaccines to cure patients with COVID-19 since the beginning of the pandemic. The Web of Science Core Collection database was retrieved for articles on therapeutic approaches to coronavirus disease management published between January 1, 2020 and May 20, 2020. Identified and analyzed the data included title, corresponding author, language, publication time, publication type, research focus. A total of 1569 articles on coronavirus therapeutic means from 84 countries were published in 620 journals. We note the remarkable progressive increase in the number of publications related to research on therapies and vaccines for COVID-19. The United States provided the largest number of articles (405), followed by China (364). Journal of Medical Virology published most of them (n=40). 1005 (64.05%) were articles, 286 (18.23%) were letters, 230 (14.66%) were reviews. The terms "COVID- 19" or "SARS-CoV-2" or "Coronavirus" or "hydroxychloroquine" or "chloroquine" or "2019-nCOV" or "ACE2" or "treatment" or "remdesivir" or "pneumonia" were most frequently used, as shown in the density visualization map. A network analysis based on keyword co-occurrence revealed five distinct types of studies: clinical, biological, epidemiological, pandemic management, and therapeutics (vaccines and treatments). COVID-19 is a major disease that has had an impact on international public health at the global level. Several avenues for treatment and vaccines have been explored. Most of them focus on older drugs used to treat other diseases that have been effective for other types of coronaviruses. There is a discrepancy in the results obtained from the studies of the drugs included in this study. Randomized clinical trials are needed to evaluate older drugs and develop new treatment options.

title: ElaD, a Deubiquitinating Protease Expressed by E. coli
abstract: Ubiquitin and ubiquitin-like proteins (Ubl) are designed to modify polypeptides in eukaryotes. Covalent binding of ubiquitin or Ubls to substrate proteins can be reversed by specific hydrolases. One particular set of cysteine proteases, the CE clan, which targets ubiquitin and Ubls, has homologs in eukaryotes, prokaryotes, and viruses.  We have cloned and analyzed the E. coli protein elaD, which is distantly related to eukaryotic CE clan members of the ULP/SENP protease family that are specific for SUMO and Nedd8. Previously misannotated as a putative sulfatase/phosphatase, elaD is an efficient and specific deubiquitinating enzyme in vitro. Interestingly, elaD is present in all intestinal pathogenic E. coli strains, but conspicuously absent from extraintestinal pathogenic strains (ExPECs). Further homologs of this protease can be found in Acanthamoeba Polyphaga Mimivirus, and in Alpha-, Beta-and Gammaproteobacteria.  The expression of ULP/SENP-related hydrolases in bacteria therefore extends to plant pathogens and medically relevant strains of Escherichia coli, Legionella pneumophila, Rickettsiae, Chlamydiae, and Salmonellae, in which the elaD ortholog sseL has recently been identified as a virulence factor with deubiquitinating activity. As a counterpoint, our phylogenetic and functional examination reveals that ancient eukaryotic ULP/SENP proteases also have the potential of ubiquitin-specific hydrolysis, suggesting an early common origin of this peptidase clan.

title: What Caused the Outbreak of COVID-19 in China: From the Perspective of Crisis Management
abstract: Since the first known case of a COVID-19 infected patient in Wuhan, China on 8 December 2019, COVID-19 has spread to more than 200 countries, causing a worldwide public health crisis. The existing literature fails to examine what caused this sudden outbreak from a crisis management perspective. This article attempts to fill this research gap through analysis of big data, officially released information and other social media sources to understand the root cause of the crisis as it relates to China’s current management system and public health policy. The article draws the following conclusions: firstly, strict government control over information was the main reason for the early silencing of media announcements, which directly caused most people to be unprepared and unaware of COVID-19. Secondly, a choice between addressing a virus with an unknown magnitude and nature, and mitigating known public panic during a politically and culturally sensitive time, lead to falsehood and concealment. Thirdly, the weak autonomous management power of local public health management departments is not conducive for providing a timely response to the crisis. Finally, the privatization of many state-owned hospitals led to the unavailability of public health medical resources to serve affected patients in the Wuhan and Hubei Province. This article suggests that China should adopt a Singaporean-style public health crisis information management system to ensure information disclosure and information symmetry and should use it to monitor public health crises in real time. In addition, the central government should adopt the territorial administration model of a public health crisis and increase investment in public health in China.

title: A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales
abstract: As the COVID-19 pandemic continues to threaten various regions around the world, obtaining accurate and reliable COVID-19 data is crucial for governments and local communities aiming at rigorously assessing the extent and magnitude of the virus spread and deploying efficient interventions. Using data reported between January and February 2020 in China, we compared counts of COVID-19 from near-real time spatially disaggregated data (city-level) with fine-spatial scale predictions from a Bayesian downscaling regression model applied to a reference province-level dataset. The results highlight discrepancies in the counts of coronavirus-infected cases at district level and identify districts that may require further investigation.

title: ACE2 Expression in Kidney and Testis May Cause Kidney and Testis Damage After 2019-nCoV Infection
abstract: In December 2019 and January 2020, novel coronavirus (2019-nCoV) - infected pneumonia (NCIP) occurred in Wuhan, and has already posed a serious threat to public health. ACE2 (Angiotensin Converting Enzyme 2) has been shown to be one of the major receptors that mediate the entry of 2019-nCoV into human cells, which also happens in severe acute respiratory syndrome coronavirus (SARS). Several researches have indicated that some patients have abnormal renal function or even kidney damage in addition to injury in respiratory system, and the related mechanism is unknown. This arouses our interest in whether coronavirus infection will affect the urinary and male reproductive systems. Here in this study, we used the online datasets to analyze ACE2 expression in different human organs. The results indicate that ACE2 highly expresses in renal tubular cells, Leydig cells and cells in seminiferous ducts in testis. Therefore, virus might directly bind to such ACE2 positive cells and damage the kidney and testicular tissue of patients. Our results indicate that renal function evaluation and special care should be performed in 2019-nCoV patients during clinical work, because of the kidney damage caused by virus and antiviral drugs with certain renal toxicity. In addition, due to the potential pathogenicity of the virus to testicular tissues, clinicians should pay attention to the risk of testicular lesions in patients during hospitalization and later clinical follow-up, especially the assessment and appropriate intervention in young patients' fertility.

title: Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images
abstract: Confronting the pandemic of COVID-19, is nowadays one of the most prominent challenges of the human species. A key factor in slowing down the virus propagation is the rapid diagnosis and isolation of infected patients. The standard method for COVID-19 identification, the Reverse transcription polymerase chain reaction method, is time-consuming and in short supply due to the pandemic. Thus, researchers have been looking for alternative screening methods and deep learning applied to chest X-rays of patients has been showing promising results. Despite their success, the computational cost of these methods remains high, which imposes difficulties to their accessibility and availability. Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays.  To achieve the defined objective we exploit and extend the EfficientNet family of deep artificial neural networks which are known for their high accuracy and low footprints in other applications. We also exploit the underlying taxonomy of the problem with a hierarchical classifier. A dataset of 13,569 X-ray images divided into healthy, non-COVID-19 pneumonia, and COVID-19 patients is used to train the proposed approaches and other 5 competing architectures. Finally, 231 images of the three classes were used to assess the quality of the methods.  The results show that the proposed approach was able to produce a high-quality model, with an overall accuracy of 93.9%, COVID-19, sensitivity of 96.8% and positive prediction of 100%, while having from 5 to 30 times fewer parameters than other than the other tested architectures. Larger and more heterogeneous databases are still needed for validation before claiming that deep learning can assist physicians in the task of detecting COVID-19 in X-ray images.

title: Identifying Research Trends and Gaps in the Context of COVID-19
abstract: The COVID-19 pandemic has affected the world in different ways. Not only are people’s lives and livelihoods affected, but the virus has also affected people’s lifestyles. In the research sector, there have been significant changes, and new research is coming very strongly in the related fields of virology and epidemiology. Similar trends were observed after the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) episodes of 2003 and 2012, respectively. Analyzing 20 years of published scientific papers, this article points out the highlights of coronavirus-related research. Significant progress is observed in the past research related to virology, epidemiology, infectious diseases among others. However, in research linked to public health, its governance, technology, and risk communication there seem to be gap areas. Although the World Health Organization (WHO) global research road map has identified social science-related research as a priority area, more focus needs to be given in the upcoming days for multi, cross and trans-disciplinary research related to public health and disaster risk reduction.

title: Diagnostic assays for respiratory syncytial virus disease.
abstract: Respiratory syncytial virus (RSV) infection in the United States and worldwide is a major cause of morbidity and mortality and of outpatient visits, hospitalizations, and increased healthcare costs. Effective diagnosis of viral respiratory infections, as well as recognition and understanding of the benefits and limitations of diagnostic laboratory testing, is essential. Serology is not useful for diagnosing acute respiratory illness. Antigen-based assays are widely available, easy to use, provide rapid results, and are inexpensive; however, they are less sensitive than cell culture utilizing good specimen collection and processing techniques. Cell culture, which was previously considered the gold standard for identification of respiratory viruses, in many settings is being replaced by nucleic acid amplification assays that have even greater sensitivity and provide more rapid results. Although available chiefly at large hospitals and reference laboratories, molecular assays may fulfill the need for more sensitive and rapid diagnosis of illnesses caused by respiratory viruses. The seasonality of RSV as measured by nucleic acid amplification-based assays appears to be broader with better identification of patient populations that harbor RSV between yearly epidemic peaks compared with the seasonality of RSV as measured by the older techniques. As these new diagnostic methodologies emerge, guidelines will be needed to direct their appropriate use in the diagnostic laboratory.

title: Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining
abstract: This paper proposes a simple method to extract from a set of multiple related time series a compressed representation for each time series based on statistics for the entire set of all time series. This is achieved by a hierarchical algorithm that first generates an alphabet of shapelets based on the segmentation of centroids for clustered data, before labels of these shapelets are assigned to the segmentation of each single time series via nearest neighbor search using unconstrained dynamic time warping as distance measure to deal with non-uniform time series lenghts. Thereby, a sequence of labels is assigned for each time series. Completion of the last label sequence permits prediction of individual time series. Proposed method is evaluated on two global COVID-19 datasets, first, for the number of daily net cases (daily new infections minus daily recoveries), and, second, for the number of daily deaths attributed to COVID-19 as of April 27, 2020. The first dataset involves 249 time series for different countries, each of length 96. The second dataset involves 264 time series, each of length 96. Based on detected anomalies in available data a decentralized exit strategy from lockdowns is advocated.

title: A Deep Learning Pipeline for Patient Diagnosis Prediction Using Electronic Health Records
abstract: Augmentation of disease diagnosis and decision-making in healthcare with machine learning algorithms is gaining much impetus in recent years. In particular, in the current epidemiological situation caused by COVID-19 pandemic, swift and accurate prediction of disease diagnosis with machine learning algorithms could facilitate identification and care of vulnerable clusters of population, such as those having multi-morbidity conditions. In order to build a useful disease diagnosis prediction system, advancement in both data representation and development of machine learning architectures are imperative. First, with respect to data collection and representation, we face severe problems due to multitude of formats and lack of coherency prevalent in Electronic Health Records (EHRs). This causes hindrance in extraction of valuable information contained in EHRs. Currently, no universal global data standard has been established. As a useful solution, we develop and publish a Python package to transform public health dataset into an easy to access universal format. This data transformation to an international health data format facilitates researchers to easily combine EHR datasets with clinical datasets of diverse formats. Second, machine learning algorithms that predict multiple disease diagnosis categories simultaneously remain underdeveloped. We propose two novel model architectures in this regard. First, DeepObserver, which uses structured numerical data to predict the diagnosis categories and second, ClinicalBERT_Multi, that incorporates rich information available in clinical notes via natural language processing methods and also provides interpretable visualizations to medical practitioners. We show that both models can predict multiple diagnoses simultaneously with high accuracy.

title: CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
abstract: The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays.  In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases.  CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available.  CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases.

title: The ongoing COVID-19 epidemic in Minas Gerais, Brazil: insights from epidemiological data and SARS-CoV-2 whole genome sequencing.
abstract: The recent emergence of a previously unknown coronavirus (SARS-CoV-2), first confirmed in the city of Wuhan in China in December 2019, has caused serious public health and economic issues due to its rapid dissemination worldwide. Although 61,888 confirmed cases had been reported in Brazil by 28 April 2020, little was known about the SARS-CoV-2 epidemic in the country. To better understand the recent epidemic in the second most populous state in southeast Brazil (Minas Gerais, MG), we looked at existing epidemiological data from 3 states and sequenced 40 complete genomes from MG cases using Nanopore. We found evidence of multiple independent introductions from outside MG, both from genome analyses and the overly dispersed distribution of reported cases and deaths. Epidemiological estimates of the reproductive number using different data sources and theoretical assumptions all suggest a reduction in transmission potential since the first reported case, but potential for sustained transmission in the near future. The estimated date of introduction in Brazil was consistent with epidemiological data from the first case of a returning-traveler from Lombardia, Italy. These findings highlight the unique reality of MGs epidemic and reinforce the need for real-time and continued genomic surveillance strategies as a way of understanding and therefore preparing against the epidemic spread of emerging viral pathogens.

title: Coronavirus disease (COVID-19) and fertility: viral host entry protein expression in male and female reproductive tissues
abstract: To identify cell types in the male and female reproductive systems at risk of SARS-CoV-2 infection due to expression of host genes and proteins used by the virus for cell entry.  Descriptive analysis of transcriptomic and proteomic data.  Academic research department and clinical diagnostic laboratory.   Identification of cell types co-expressing the key ACE2 and TMPRSS2 genes and proteins, as well as other candidates potentially involved in SARS-CoV-2 cell entry.  Based on single cell RNA sequencing data, co-expression of ACE2 and TMPRSS2 was not detected in testicular cells, including sperm. A subpopulation of oocytes in non-human primate ovarian tissue were found to express ACE2 and TMPRSS2, but co-expression was not observed in ovarian somatic cells. RNA expression of TMPRSS2 in 18 samples of human cumulus cells was shown to be low or absent. There was general agreement between publicly available bulk RNA and protein datasets in terms of ACE2 and TMPRSS2 expression patterns in testis, ovary, endometrial and placental cells.  These analyses suggest that SARS-CoV-2 infection is unlikely to have long-term effects on male and female reproductive function. While the results cannot be considered definitive they imply that procedures in which oocytes are collected and fertilized in vitro are associated with very little risk of viral transmission from gametes to embryos and may indeed have the potential to minimize exposure of susceptible reproductive cell types to infection when compared to natural conception.

title: Coronaviruses and people with intellectual disability: an exploratory data analysis
abstract: Corona virus disease 2019 (COVID-19) has been announced as a new coronavirus disease by the World Health Organization. At the time of writing this article (April 2020), the world is drastically influenced by the COVID-19. Recently, the COVID-19 Open Research Dataset (CORD-19) was published. For researchers on ID such as ourselves, it is of key interest to learn whether this open research dataset may be used to investigate the virus and its consequences for people with an ID.  From CORD-19, we identified full-text articles containing terms related to the ID care and applied a text mining technique, specifically the term frequency-inverse document frequency analysis in combination with K-means clustering.  Two hundred fifty-nine articles contained one or more of our specified terms related to ID. We were able to cluster these articles related to ID into five clusters on different topics, namely: mental health, viral diseases, diagnoses and treatments, maternal care and paediatrics, and genetics.  The CORD-19 open research dataset consists of valuable information about not only COVID-19 disease but also ID and the relationship between them. We suggest researchers investigate literature-based discovery approaches on the CORD-19 and develop a new dataset that addresses the intersection of these two fields for further research.

title: Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning
abstract: Coronavirus disease (COVID-19) is an infectious disease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world.  The aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia and healthy cases using deep learning techniques.  In this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance different CNN architectures.  Evaluation results using K-fold (10) showed that we have achieved state of the art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole.  Quantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models develop in this study could be used early screening of coronavirus, however it calls for extensive need to CT or X-rays dataset to develop a reliable application.

title: Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic
abstract: The emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in China at December 2019 had led to a global outbreak of coronavirus disease 2019 (COVID-19) and the disease started to spread all over the world and became an international public health issue. The entire humanity has to fight in this war against the unexpected and each and every individual role is important. Healthcare system is doing exceptional work and the government is taking various measures that help the society to control the spread. Public, on the other hand, coordinates with the policies and act accordingly in most state of affairs. But the role of technologies in assisting different social bodies to fight against the pandemic remains hidden. The intention of our study is to uncover the hidden roles of technologies that ultimately help for controlling the pandemic. On investigating, it is found that the strategies utilizing potential technologies would yield better benefits and these technological strategies can be framed either to control the pandemic or to support the confinement of the society during pandemic which in turn aids in controlling the spreading of infection. This study enlightens the various implemented technologies that assists the healthcare systems, government and public in diverse aspects for fighting against COVID-19. Furthermore, the technological swift that happened during the pandemic and their influence in the environment and society is discussed. Besides the implemented technologies, this work also deals with untapped potential technologies that have prospective applications in controlling the pandemic circumstances. Alongside the various discussion, our suggested solution for certain situational issues is also presented.

title: Breaking down of the healthcare system: Mathematical modelling for controlling the novel coronavirus (2019-nCoV) outbreak in Wuhan, China
abstract: A novel coronavirus pneumonia initially identified in Wuhan, China and provisionally named 2019-nCoV has surged in the public. In anticipation of substantial burdens on healthcare system following this human-to-human spread, we aim to scrutinise the currently available information and evaluate the burden of healthcare systems during this outbreak in Wuhan. We applied a modified SIR model to project the actual number of infected cases and the specific burdens on isolation wards and intensive care units, given the scenarios of different diagnosis rates as well as different public health intervention efficacy. Our estimates suggest the actual number of infected cases could be much higher than the reported, with estimated 26,701 cases (as of 28th January 2020) assuming 50% diagnosis rate if no public health interventions were implemented. The estimated burdens on healthcare system could be largely reduced if at least 70% efficacy of public health intervention is achieved.

title: Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
abstract: The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such major viral outbreaks demand early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 virus genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 virus genomes. The proposed method combines supervised machine learning with digital signal processing (MLDSP) for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman's rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp, including the 29 COVID-19 virus sequences available on January 27, 2020. Our results support a hypothesis of a bat origin and classify the COVID-19 virus as Sarbecovirus, within Betacoronavirus. Our method achieves 100% accurate classification of the COVID-19 virus sequences, and discovers the most relevant relationships among over 5000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

title: Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics
abstract: In December 2019, a new virus (initially called ‘Novel Coronavirus 2019-nCoV’ and later renamed to SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China, and rapidly spread to other parts of China and other countries around the world, despite China’s massive efforts to contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, geographic information systems and methods, including, among other application possibilities, online real-or near-real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time, are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight against infectious disease outbreaks and epidemics.

title: COVID-19: A promising cure for the global panic
abstract: The novel Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, which is the causative agent of a potentially fatal disease that is of great global public health concern. The outbreak of COVID-19 is wreaking havoc worldwide due to inadequate risk assessment regarding the urgency of the situation. The COVID-19 pandemic has entered a dangerous new phase. When compared with SARS and MERS, COVID-19 has spread more rapidly, due to increased globalization and adaptation of the virus in every environment. Slowing the spread of the COVID-19 cases will significantly reduce the strain on the healthcare system of the country by limiting the number of people who are severely sick by COVID-19 and need hospital care. Hence, the recent outburst of COVID-19 highlights an urgent need for therapeutics targeting SARS-CoV-2. Here, we have discussed the structure of virus; varying symptoms among COVID-19, SARS, MERS and common flu; the probable mechanism behind the infection and its immune response. Further, the current treatment options, drugs available, ongoing trials and recent diagnostics for COVID-19 have been discussed. We suggest traditional Indian medicinal plants as possible novel therapeutic approaches, exclusively targeting SARS-CoV-2 and its pathways.

title: SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems
abstract: Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge.

title: A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study
abstract: Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to install them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Nowadays Smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.

title: The global spread of Middle East respiratory syndrome: an analysis fusing traditional epidemiological tracing and molecular phylodynamics
abstract: Since its discovery in 2012, over 1700 confirmed cases of Middle East Respiratory Syndrome (MERS) have been documented worldwide and more than a third of those cases have died. While the greatest number of cases has occurred in Saudi Arabia, the recent export of MERS-coronavirus (MERS-CoV) to Republic of Korea showed that a pandemic is a possibility that cannot be ignored. Due to the deficit of knowledge in transmission methodology, targeted treatment and possible vaccines, understanding this virus should be a priority. Our aim was to combine epidemiological data from literature with genetic information from viruses sequenced around the world to present a phylodynamic picture of MERS spread molecular level to global scale.  We performed a qualitative meta-analysis of all laboratory confirmed cases worldwide to date based on literature, with emphasis on international transmission and healthcare associated infections. In parallel, we used publicly available MERS-CoV genomes from GenBank to create a phylogeographic tree, detailing geospatial timeline of viral evolution.  Several healthcare associated outbreaks starting with the retrospectively identified hospital outbreak in Jordan to the most recent outbreak in Riyadh, Saudi Arabia have occurred. MERS has also crossed many oceans, entering multiple nations in eight waves between 2012 and 2015. In this paper, the spatiotemporal history of MERS cases, as documented epidemiologically, was examined by Bayesian phylogenetic analysis. Distribution of sequences into geographic clusters and interleaving of MERS-CoV sequences from camels among those isolated from humans indicated that multiple zoonotic introductions occurred in endemic nations. We also report a summary of basic reproduction numbers for MERS-CoV in humans and camels.  Together, these analyses can help us identify factors associated with viral evolution and spread as well as establish efficacy of infection control measures. The results are especially pertinent to countries without current MERS-CoV endemic, since their unfamiliarity makes them particularly susceptible to uncontrollable spread of a virus that may be imported by travelers.  contains supplementary material, which is available to authorized users.

title: Clinical characteristics of severe acute respiratory syndrome coronavirus 2 reactivation
abstract: Previous studies on the pneumonia outbreak caused by the 2019 novel coronavirus disease (COVID-19) were based on information from the general population. However, limited data was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reactivation. This study aimed to evaluate the clinical characteristics of the SARS-CoV-2 reactivation.  Clinical records, laboratory results, and chest CT scans were retrospectively reviewed for 55 patients with laboratory-confirmed COVID-19 pneumonia (i.e., with throat swab samples that were positive for SARS-CoV-2) who were admitted to Zhongnan Hospital of Wuhan University, Wuhan, China, from Jan. 8 to Feb. 10, 2020.  All 55 patients had a history of epidemiological exposure to COVID-19, and 5 (9%) patients who discharged from hospital presented with SARS-CoV-2 reactivation. Among the 5 reactivated patients, other symptoms were also observed, including fever, cough, sore throat, and fatigue. One of the 5 patients had progressive lymphopenia (from 1.3 to 0.56 × 109 cells per L) and progressive neutrophilia (from 4.5 to 18.28 × 109 cells per L). All 5 reactivated patients presented normal aminotransferase levels. Throat swab samples from the 5 reactivated patients were tested for SARS-CoV-2, indicating all positive for the virus.  Findings from this small group of cases suggested that there was currently evidence for reactivation of SARS-CoV-2 and there might be no specific clinical characteristics to distinguish them.

title: CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification
abstract: The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight studies of severe patients and direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods provide reasonable quality only for one of these setups. To consolidate both triage approaches, we employ a multitask learning and propose a convolutional neural network to combine all available labels within a single model. We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 33 COVID patients, 32 healthy patients, and 36 patients with other lung pathologies to emulate a typical patient flow in an out-patient hospital. The developed model achieved 0.951 ROC AUC for Identification of COVID-19 and 0.98 Spearman Correlation for Severity quantification. We release all the code and create a public leaderboard, where other community members can test their models on our dataset.

title: What we know so far: COVID-19 current clinical knowledge and research.
abstract: In December 2019, health authorities in Wuhan, China, identified a cluster of pneumonia cases of unknown aetiology linked to the city's South China Seafood Market. Subsequent investigations revealed a novel coronavirus, SARS-CoV-2, as the causative agent now at the heart of a major outbreak. The rising case numbers have been accompanied by unprecedented public health action, including the wholesale isolation of Wuhan. Alongside this has been a robust scientific response, including early publication of the pathogen genome, and rapid development of highly specific diagnostics. This article will review the new knowledge of SARS-CoV-2 COVID-19 acute respiratory disease, and summarise its clinical features.

title: Epidemiology of seasonal coronaviruses: Establishing the context for COVID-19 emergence
abstract: Public health preparedness for coronavirus disease 2019 (COVID-19) is challenging in the absence of setting-specific epidemiological data. Here we describe the epidemiology of seasonal human coronaviruses (sCoVs) and other cocirculating viruses in the West of Scotland, UK. We analyzed routine diagnostic data for >70,000 episodes of respiratory illness tested molecularly for multiple respiratory viruses between 2005 and 2017. Statistical associations with patient age and sex differed between CoV-229E, CoV-OC43 and CoV-NL63. Furthermore, the timing and magnitude of sCoV outbreaks did not occur concurrently and coinfections were not reported. With respect to other cocirculating respiratory viruses, we found evidence of positive, rather than negative, interactions with sCoVs. These findings highlight the importance of considering cocirculating viruses in the differential diagnosis of COVID-19. Further work is needed to establish the occurrence/degree of cross-protective immunity conferred across sCoVs and with COVID-19, as well as the role of viral coinfection in COVID-19 disease severity.

title: Does infection of 2019 novel coronavirus cause acute and/or chronic sialadenitis?
abstract: 2019 novel coronavirus (2019-nCoV) is widespread in China and other countries. The target of 2019-nCoV and severe acute respiratory syndrome coronavirus (SARS-CoV) is angiotensin-converting enzyme 2 (ACE2) positive cells. ACE2 is present in the salivary gland duct epithelium, and thus it could be the target of 2019-nCoV and SARS-CoV. SARS-CoV-related animal model experiments show that it can infect the epithelial cells on the salivary gland duct in Chinese rhesus macaques by targeting ACE2. Clinical studies confirmed that 2019-nCoV and SARS-CoV could be detected in saliva of human patients. We hypothesize that the infection of 2019-nCoV and SARS-CoV will lead to inflammatory pathological lesions in patients’ target organs, and possibly inflammatory lesions in salivary glands. 2019-nCoV may cause acute sialoadenitis in the acute phase of infection. After the acute phase, chronic sialoadenitis may be caused by fibrosis repairment. Although there was no direct evidence to prove this, the available indirect evidence indicates a high probability of our hypothesis.

title: Continual BERT: Continual Learning for Adaptive Extractive Summarization of COVID-19 Literature
abstract: The scientific community continues to publish an overwhelming amount of new research related to COVID-19 on a daily basis, leading to much literature without little to no attention. To aid the community in understanding the rapidly flowing array of COVID-19 literature, we propose a novel BERT architecture that provides a brief yet original summarization of lengthy papers. The model continually learns on new data in online fashion while minimizing catastrophic forgetting, thus fitting to the need of the community. Benchmark and manual examination of its performance show that the model provide a sound summary of new scientific literature.

title: Information Fusion-2-Text: Explainable Aggregation via Linguistic Protoforms
abstract: Recent advancements and applications in artificial intelligence (AI) and machine learning (ML) have highlighted the need for explainable, interpretable, and actionable AI-ML. Most work is focused on explaining deep artificial neural networks, e.g., visual and image captioning. In recent work, we established a set of indices and processes for explainable AI (XAI) relative to information fusion. While informative, the result is information overload and domain expertise is required to understand the results. Herein, we explore the extraction of a reduced set of higher-level linguistic summaries to inform and improve communication with non-fusion experts. Our contribution is a proposed structure of a fusion summary and method to extract this information from a given set of indices. In order to demonstrate the usefulness of the proposed methodology, we provide a case study for using the fuzzy integral to combine a heterogeneous set of deep learners in remote sensing for object detection and land cover classification. This case study shows the potential of our approach to inform users about important trends and anomalies in the models, data and fusion results. This information is critical with respect to transparency, trustworthiness, and identifying limitations of fusion techniques, which may motivate future research and innovation.

title: Novel Betacoronavirus in Dromedaries of the Middle East, 2013
abstract: In 2013, a novel betacoronavirus was identified in fecal samples from dromedaries in Dubai, United Arab Emirates. Antibodies against the recombinant nucleocapsid protein of the virus, which we named dromedary camel coronavirus (DcCoV) UAE-HKU23, were detected in 52% of 59 dromedary serum samples tested. In an analysis of 3 complete DcCoV UAE-HKU23 genomes, we identified the virus as a betacoronavirus in lineage A1. The DcCoV UAE-HKU23 genome has G+C contents; a general preference for G/C in the third position of codons; a cleavage site for spike protein; and a membrane protein of similar length to that of other betacoronavirus A1 members, to which DcCoV UAE-HKU23 is phylogenetically closely related. Along with this coronavirus, viruses of at least 8 other families have been found to infect camels. Because camels have a close association with humans, continuous surveillance should be conducted to understand the potential for virus emergence in camels and for virus transmission to humans.

title: Poster Presentations

title: The epidemiological and clinical features of COVID-19 and lessons from this global infectious public health event
abstract: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and represents a potentially fatal disease of great global public health importance. As of March 26, 2020, the outbreak of COVID-19 has resulted in 462,801 confirmed cases and 20,839 deaths globally, which is more than those caused by SARS and Middle East respiratory syndrome (MERS) in 2003 and 2013, respectively. The epidemic has posed considerable challenges worldwide. Under a strict mechanism of massive prevention and control, China has seen a rapid decrease in new cases of coronavirus; however, the global situation remains serious. Additionally, the origin of COVID-19 has not been determined and no specific antiviral treatment or vaccine is currently available. Based on the published data, this review systematically discusses the etiology, epidemiology, clinical characteristics, and current intervention measures related to COVID-19 in the hope that it may provide a reference for future studies and aid in the prevention and control of the COVID-19 epidemic.

title: Catalytic site studies on tuna (Thunnus albacares) pyloric caeca aminopeptidase
abstract: Tuna pyloric caeca aminopeptidase (tAP) is a glycosylated zinc-metalloenzyme containing apparently two identical subunits. The enzyme is reversibly inhibited in a time-dependent manner by amastatin. Slow development of tAP inhibition by this inhibitor could be demonstrated. Dissociation of the complex of tAP with amastatin is also slow. Two molar equivalents of the inhibitor are bound by the enzyme suggesting the presence of one catalytic site in each subunit. Chemical modification of tAP with 1-cyclohexyl-3-(2-morpholinoethyl)carbonyl-metho-p-toluene sulfonate and N-ethoxycarbonyl-2-ethoxy-1,2-dihydroquinone revealed the presence of essential acidic amino acid residues probably located at the active site. Compatible with the presence of arginine and tyrosine residues at the catalytic site of most metalloproteinases, tAP is reversibly inhibited by phenylglyoxal and inactivated by tetranitromethane in a time-dependent fashion. The rate of inhibition by these modifiers could be significantly decreased if the enzyme was previously treated with amastatin suggesting that the modified amino acid residues are located at the catalytic site. Diethylpyrocarbonate did not affect the activity of both native and zinc-depleted tAP suggesting that histidine is not involved in the zinc-ligand formation.

title: Molecular diversity of coronaviruses in bats
abstract: The existence of coronaviruses in bats is unknown until the recent discovery of bat-SARS-CoV in Chinese horseshoe bats and a novel group 1 coronavirus in other bat species. Among 309 bats of 13 species captured from 20 different locations in rural areas of Hong Kong over a 16-month period, coronaviruses were amplified from anal swabs of 37 (12%) bats by RT-PCR. Phylogenetic analysis of RNA-dependent-RNA-polymerase (pol) and helicase genes revealed six novel coronaviruses from six different bat species, in addition to the two previously described coronaviruses. Among the six novel coronaviruses, four were group 1 coronaviruses (bat-CoV HKU2 from Chinese horseshoe bat, bat-CoV HKU6 from rickett's big-footed bat, bat-CoV HKU7 from greater bent-winged bat and bat-CoV HKU8 from lesser bent-winged bat) and two were group 2 coronaviruses (bat-CoV HKU4 from lesser bamboo bats and bat-CoV HKU5 from Japanese pipistrelles). An astonishing diversity of coronaviruses was observed in bats.

title: Global Preparedness Against COVID-19: We Must Leverage the Power of Digital Health
abstract: The coronavirus disease (COVID-19) pandemic has revealed many areas of public health preparedness that are lacking, especially in lower- and middle-income countries. Digital interventions provide many opportunities for strengthening health systems and could be vital resources in the current public health emergency. We provide several use cases for infection control, home-based diagnosis and screening, empowerment through information, public health surveillance and epidemiology, and leveraging crowd-sourced data. A thoughtful, concerted effort—leveraging existing experience and robust enterprise-grade technologies—can have a substantive impact on the immediate and distal consequences of COVID-19.

title: Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes
abstract: Genome Detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present The Genome Detective Coronavirus Typing Tool that can accurately identify the novel severe acute respiratory syndrome (SARS) related coronavirus (SARS-CoV-2) sequences isolated in China and around the world. The tool can accept up to 2,000 sequences per submission and the analysis of a new whole genome sequence will take approximately one minute. The tool has been tested and validated with hundreds of whole genomes from ten coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for SARS-CoV-2. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines to stop the COVID-19 disease.

title: COVID19-Tracker: una aplicación web para visualización de datos de la epidemia de SARS-CoV-2 en España
abstract: Data visualization is an important tool for exploring and communicating findings in medical research, and specially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases and mortality, from February 24th, 2020 onwards. Three applications have already been developed: 1) to analyze data trends and estimating short-term projections; 2) to estimate the case fatality rate; and 3) to assess the effect of the lockdowns on the data trends. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain.

title: Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCov literature
abstract: During the current century, each major coronavirus outbreak has triggered a quick and immediate surge of academic publications on this topic. The spike in research publications following the 2019 Novel Coronavirus (Covid-19) outbreak, however, has been like no other. The global crisis caused by the Covid-19 pandemic has mobilised scientific efforts in an unprecedented way. In less than five months, more than 12,000 research items have been indexed while the number increasing every day. With the crisis affecting all aspects of life, research on Covid-19 seems to have become a focal point of interest across many academic disciplines. Here, scientometric aspects of the Covid-19 literature are analysed and contrasted with those of the two previous major Coronavirus diseases, i.e. Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). The focus is on the co-occurrence of key-terms, bibliographic coupling and citation relations of journals and collaborations between countries. Certain recurring patterns across all three literatures were discovered. All three outbreaks have commonly generated three distinct and major cohort of studies: (i) studies linked to the public health response and epidemic control, (ii) studies associated with the chemical constitution of the virus and (iii) studies related to treatment, vaccine and clinical care. While studies affiliated with the category (i) seem to have been the first to emerge, they overall received least numbers of citations compared to those of the two other categories. Covid-19 studies seem to have been distributed across a broader variety of journals and subject areas. Clear links are observed between the geographical origins of each outbreak or the local geographical severity of each outbreak and the magnitude of research originated from regions. Covid-19 studies also display the involvement of authors from a broader variety of countries compared to SARS and MRS.

title: Digit analysis for Covid-19 reported data
abstract: The coronavirus which appeared in December 2019 in Wuhan has spread out worldwide and caused the death of more than 280,000 people (as of May, 11 2020). Since February 2020, doubts were raised about the numbers of confirmed cases and deaths reported by the Chinese government. In this paper, we examine data available from China at the city and provincial levels and we compare them with Canadian provincial data, US state data and French regional data. We consider cumulative and daily numbers of confirmed cases and deaths and examine these numbers through the lens of their first two digits and in particular we measure departures of these first two digits to the Newcomb-Benford distribution, often used to detect frauds. Our finding is that there is no evidence that cumulative and daily numbers of confirmed cases and deaths for all these countries have different first or second digit distributions. We also show that the Newcomb-Benford distribution cannot be rejected for these data.

title: COVID-19 TestNorm - A tool to normalize COVID-19 testing names to LOINC codes
abstract: Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on COVID-19. Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from eight healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online web application for end-users (https://clamp.uth.edu/covid/loinc.php). We believe it will be a useful tool to support secondary use of EHRs for research on COVID-19.

title: COVID-19: Social Media Sentiment Analysis on Reopening
abstract: The novel coronavirus (COVID-19) pandemic is the most talked topic in social media platforms in 2020. People are using social media such as Twitter to express their opinion and share information on a number of issues related to the COVID-19 in this stay at home order. In this paper, we investigate the sentiment and emotion of peoples in the United States on the subject of reopening. We choose the social media platform Twitter for our analysis and study the Tweets to discover the sentimental perspective, emotional perspective, and triggering words towards the reopening. During this COVID-19 pandemic, researchers have made some analysis on various social media dataset regarding lockdown and stay at home. However, in our analysis, we are particularly interested to analyse public sentiment on reopening. Our major finding is that when all states resorted to lockdown in March, people showed dominant emotion of fear, but as reopening starts people have less fear. While this may be true, due to this reopening phase daily positive cases are rising compared to the lockdown situation. Overall, people have a less negative sentiment towards the situation of reopening.

title: SARS-CoV-2 spike protein predicted to form complexes with host receptor protein orthologues from a broad range of mammals
abstract: SARS-CoV-2 has a zoonotic origin and was transmitted to humans via an undetermined intermediate host, leading to infections in humans and other mammals. To enter host cells, the viral spike protein (S-protein) binds to its receptor, ACE2, and is then processed by TMPRSS2. Whilst receptor binding contributes to the viral host range, S-protein:ACE2 complexes from other animals have not been investigated widely. To predict infection risks, we modelled S-protein:ACE2 complexes from 215 vertebrate species, calculated changes in the energy of the complex caused by mutations in each species, relative to human ACE2, and correlated these changes with COVID-19 infection data. We also analysed structural interactions to better understand the key residues contributing to affinity. We predict that mutations are more detrimental in ACE2 than TMPRSS2. Finally, we demonstrate phylogenetically that human SARS-CoV-2 strains have been isolated in animals. Our results suggest that SARS-CoV-2 can infect a broad range of mammals, but few fish, birds or reptiles. Susceptible animals could serve as reservoirs of the virus, necessitating careful ongoing animal management and surveillance.

title: An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT
abstract: Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is the emergent international health terminology standard for encoding clinical information in electronic health records. The CORE Problem List Subset was created to facilitate the terminology’s implementation. This study evaluates the CORE Subset’s coverage and examines its growth pattern as source datasets are being incorporated.  Coverage of frequently used terms and the corresponding usage of the covered terms were assessed by “leave-one-out” analysis of the eight datasets constituting the current CORE Subset. The growth pattern was studied using a retrospective experiment, growing the Subset one dataset at a time and examining the relationship between the size of the starting subset and the coverage of frequently used terms in the incoming dataset. Linear regression was used to model that relationship.  On average, the CORE Subset covered 80.3% of the frequently used terms of the left-out dataset, and the covered terms accounted for 83.7% of term usage. There was a significant positive correlation between the CORE Subset’s size and the coverage of the frequently used terms in an incoming dataset. This implies that the CORE Subset will grow at a progressively slower pace as it gets bigger.  The CORE Problem List Subset is a useful resource for the implementation of Systematized Nomenclature of Medicine Clinical Terms in electronic health records. It offers good coverage of frequently used terms, which account for a high proportion of term usage. If future datasets are incorporated into the CORE Subset, it is likely that its size will remain small and manageable.

title: The deadly coronaviruses: The 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China
abstract: The 2019-nCoV is officially called SARS-CoV-2 and the disease is named COVID-19. This viral epidemic in China has led to the deaths of over 1800 people, mostly elderly or those with an underlying chronic disease or immunosuppressed state. This is the third serious Coronavirus outbreak in less than 20 years, following SARS in 2002–2003 and MERS in 2012. While human strains of Coronavirus are associated with about 15% of cases of the common cold, the SARS-CoV-2 may present with varying degrees of severity, from flu-like symptoms to death. It is currently believed that this deadly Coronavirus strain originated from wild animals at the Huanan market in Wuhan, a city in Hubei province. Bats, snakes and pangolins have been cited as potential carriers based on the sequence homology of CoV isolated from these animals and the viral nucleic acids of the virus isolated from SARS-CoV-2 infected patients. Extreme quarantine measures, including sealing off large cities, closing borders and confining people to their homes, were instituted in January 2020 to prevent spread of the virus, but by that time much of the damage had been done, as human-human transmission became evident. While these quarantine measures are necessary and have prevented a historical disaster along the lines of the Spanish flu, earlier recognition and earlier implementation of quarantine measures may have been even more effective. Lessons learned from SARS resulted in faster determination of the nucleic acid sequence and a more robust quarantine strategy. However, it is clear that finding an effective antiviral and developing a vaccine are still significant challenges. The costs of the epidemic are not limited to medical aspects, as the virus has led to significant sociological, psychological and economic effects globally. Unfortunately, emergence of SARS-CoV-2 has led to numerous reports of Asians being subjected to racist behavior and hate crimes across the world.

title: A database of geopositioned Middle East Respiratory Syndrome Coronavirus occurrences
abstract: As a World Health Organization Research and Development Blueprint priority pathogen, there is a need to better understand the geographic distribution of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and its potential to infect mammals and humans. This database documents cases of MERS-CoV globally, with specific attention paid to zoonotic transmission. An initial literature search was conducted in PubMed, Web of Science, and Scopus; after screening articles according to the inclusion/exclusion criteria, a total of 208 sources were selected for extraction and geo-positioning. Each MERS-CoV occurrence was assigned one of the following classifications based upon published contextual information: index, unspecified, secondary, mammal, environmental, or imported. In total, this database is comprised of 861 unique geo-positioned MERS-CoV occurrences. The purpose of this article is to share a collated MERS-CoV database and extraction protocol that can be utilized in future mapping efforts for both MERS-CoV and other infectious diseases. More broadly, it may also provide useful data for the development of targeted MERS-CoV surveillance, which would prove invaluable in preventing future zoonotic spillover.

title: The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic
abstract: Recently, the pandemic of the novel Coronavirus Disease-2019 (COVID-19) has presented governments with ultimate challenges. In the United States, the country with the highest confirmed COVID-19 infection cases, a nationwide social distancing protocol has been implemented by the President. For the first time in a hundred years since the 1918 flu pandemic, the US population is mandated to stay in their households and avoid public contact. As a result, the majority of public venues and services have ceased their operations. Following the closure of the University of Washington on March 7th, more than a thousand colleges and universities in the United States have cancelled in-person classes and campus activities, impacting millions of students. This paper aims to discover the social implications of this unprecedented disruption in our interactive society regarding both the general public and higher education populations by mining people's opinions on social media. We discover several topics embedded in a large number of COVID-19 tweets that represent the most central issues related to the pandemic, which are of great concerns for both college students and the general public. Moreover, we find significant differences between these two groups of Twitter users with respect to the sentiments they expressed towards the COVID-19 issues. To our best knowledge, this is the first social media-based study which focuses on the college student community's demographics and responses to prevalent social issues during a major crisis.

title: Transmission dynamics: Data sharing in the COVID‐19 era
abstract: The current coronavirus disease 2019 (COVID‐19) pandemic underscores the need for building and sustaining public health data infrastructure to support a rapid local, regional, national, and international response. Despite a historical context of public health crises, data sharing agreements and transactional standards do not uniformly exist between institutions which hamper a foundational infrastructure to meet data sharing and integration needs for the advancement of public health.  There is a growing need to apply population health knowledge with technological solutions to data transfer, integration, and reasoning, to improve health in a broader learning health system ecosystem. To achieve this, data must be combined from healthcare provider organizations, public health departments, and other settings. Public health entities are in a unique position to consume these data, however, most do not yet have the infrastructure required to integrate data sources and apply computable knowledge to combat this pandemic.  Herein, we describe lessons learned and a framework to address these needs, which focus on: (a) identifying and filling technology “gaps”; (b) pursuing collaborative design of data sharing requirements and transmission mechanisms; (c) facilitating cross‐domain discussions involving legal and research compliance; and (d) establishing or participating in multi‐institutional convening or coordinating activities. NEXT STEPS: While by no means a comprehensive evaluation of such issues, we envision that many of our experiences are universal. We hope those elucidated can serve as the catalyst for a robust community‐wide dialogue on what steps can and should be taken to ensure that our regional and national health care systems can truly learn, in a rapid manner, so as to respond to this and future emergent public health crises.

title: A brief review of antiviral drugs evaluated in registered clinical trials for COVID-19
abstract: Although a number of antiviral agents have been evaluated for coronaviruses there are no approved drugs available. To provide an overview of the landscape of therapeutic research for COVID-19, we conducted a review of registered clinical trials.  A review of currently registered clinical trials was performed on registries, including the Chinese (chictr.org.cn) and US (clinicaltrials.gov) databases to identify relevant studies up to March, 7th 2020. The search was conducted using the search terms “2019-nCoV”, “COVID-19”, “SARS-CoV-2”, “Hcov-19”, “new coronavirus”, “novel coronavirus”. We included interventional clinical trials focusing on patients with COVID-19 and assessing antiviral drugs or agents.  Out of the 353 studies identified, 115 clinical trials were selected for data extraction. Phase IV trials were the most commonly reported study type (n=27, 23%). However, 62 trials (54%) did not describe the phase of the study. Eighty percent (n=92) of the trials were randomized with parallel assignment and the median number of planned inclusions was 63 (IQR, 36-120). Open-label studies were the most frequent (46%) followed by double-blind (13%) and single blind studies (10%). The most frequently assessed therapies were: stem cells therapy (n=23 trials), lopinavir/ritonavir (n=15), chloroquine (n=11), umifenovir (n=9), hydroxychloroquine (n=7), plasma treatment (n=7), favipiravir (n=7), methylprednisolone (n=5), and remdesivir (n=5). Remdesivir was tested in 5 trials with a median of 400 (IQR, 394-453) planned inclusions per trial, while stem cells therapy was tested in 23 trials, but had a median of 40 (IQR, 23-60) planned inclusions per trial. Lopinavir/ritonavir was associated with the highest total number of planned inclusions (2606) followed by remdesivir (2155). Only 52% of the clinical trials reported the treatment dose (n=60) and only 34% (n=39) the duration. The primary outcome was clinical in 76 studies (66%), virological in 27 (23%); radiological in 9 (8%) or immunological in three studies (3%).  Numerous clinical trials have been registered since the beginning of the COVID-19 outbreak, however, a number of information regarding drugs or trial design were lacking.

title: Intra-model Variability in COVID-19 Classification Using Chest X-ray Images
abstract: X-ray and computed tomography (CT) scanning technologies for COVID-19 screening have gained significant traction in AI research since the start of the coronavirus pandemic. Despite these continuous advancements for COVID-19 screening, many concerns remain about model reliability when used in a clinical setting. Much has been published, but with limited transparency in expected model performance. We set out to address this limitation through a set of experiments to quantify baseline performance metrics and variability for COVID-19 detection in chest x-ray for 12 common deep learning architectures. Specifically, we adopted an experimental paradigm controlling for train-validation-test split and model architecture where the source of prediction variability originates from model weight initialization, random data augmentation transformations, and batch shuffling. Each model architecture was trained 5 separate times on identical train-validation-test splits of a publicly available x-ray image dataset provided by Cohen et al. (2020). Results indicate that even within model architectures, model behavior varies in a meaningful way between trained models. Best performing models achieve a false negative rate of 3 out of 20 for detecting COVID-19 in a hold-out set. While these results show promise in using AI for COVID-19 screening, they further support the urgent need for diverse medical imaging datasets for model training in a way that yields consistent prediction outcomes. It is our hope that these modeling results accelerate work in building a more robust dataset and a viable screening tool for COVID-19.

title: COVID-19 Public Sentiment Insights and MachineLearning for Tweets Classification
abstract: Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Na"ive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

title: BOAssembler: A Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance
abstract: High throughput sequencing of RNA (RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample. How to better recover the original RNA transcripts from those fragments (RNA-Seq assembly) is still a difficult task. For example, RNA-Seq assembly tools typically require hyper-parameter tuning to achieve good performance for particular datasets. This kind of tuning is usually unintuitive and time-consuming. Consequently, users often resort to default parameters, which do not guarantee consistent good performance for various datasets.  Here we propose BOAssembler, a framework that enables end-to-end automatic tuning of RNA-Seq assemblers, based on Bayesian Optimization principles. Experiments show this data-driven approach is effective to improve the overall assembly performance. The approach would be helpful for downstream (e.g. gene, protein, cell) analysis, and more broadly, for future bioinformatics benchmark studies. Availability: https://github.com/shunfumao/boassembler.

title: Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS
abstract: The purpose of this paper is to reveal the spread rules of the three pneumonia: COVID-19, SARS and MERS. We compare the new spread characteristics of COVID-19 with those of SARS and MERS. By considering the growth rate and inhibition constant of infectious diseases, their propagation growth model is established. The parameters of the three coronavirus transmission growth models are obtained by nonlinear fitting. Parametric analysis shows that the growth rate of COVID-19 is about twice that of the SARS and MERS, and the COVID-19 doubling cycle is two to three days, suggesting that the number of COVID-19 patients would double in two to three days without human intervention. The infection inhibition constant in Hubei is two orders of magnitude lower than in other regions, which reasonably explains the situation of the COVID-19 outbreak in Hubei.

title: Safe bronchodilator treatment in mechanically ventilated COVID-19 patients: A single center experience

title: Incarceration And Its Disseminations: COVID-19 Pandemic Lessons From Chicago's Cook County Jail
abstract: Jails and prisons are major sites of novel coronavirus (SARS-CoV-2) infection. Many jurisdictions in the United States have therefore accelerated release of low-risk offenders. Early release, however, does not address how arrest and pre-trial detention practices may be contributing to disease spread. Using data from Cook County Jail, in Chicago, Illinois, one of the largest known nodes of SARS-CoV-2 spread, we analyze the relationship between jailing practices and community infections at the zip-code level. We find that jail cycling is a significant predictor of SARS-CoV-2 infection, accounting for 55 percent of the variance in case rates across zip codes in Chicago and 37 percent in Illinois. By comparison, jail cycling far exceeds race, poverty, public transit utilization, and population density as a predictor of variance. The data suggest that cycling through Cook County Jail alone is associated with 15.7 percent of all documented novel coronavirus disease (COVID-19) cases in Illinois and 15.9 percent in Chicago as of April 19, 2020. Our findings support arguments for reduced reliance on incarceration and for related justice reforms both as emergency measures during the present pandemic and as sustained structural changes vital for future pandemic preparedness and public health.

title: Exploring genome architecture through GOV: a WWW-based gene order visualizer.
abstract: The past decade has seen extension in the methods of sequence analysis from single gene based to analyzing multiple genes and proteins simultaneously. Consequently, there is a need for software tools that will allow mining of these enormous datasets at genome level effectively. A key challenge is to make them user-friendly, available to a larger community and integrate with public domain software without much hassle.  A web-based interactive computational tool is described for visualization and comparison of gene order from prokaryotic and selected viral genome data. Many intriguing similarities and differences in gene order of multiple genomes can be compared and revealed. The interface facilitates easy extraction of the nucleotide sequence of the gene of interest and BLAST analysis against GenBank at NCBI to provide insights into gene functions and orthologs of the gene in other species.

title: Social and administrative issues related to the COVID-19 pandemic in Pakistan: better late than never
abstract: The study critically reviewed Pakistan’s provincial updates of coronavirus disease 2019 (COVID-19) and discussed the current challenges faced by the government in a given context. The coronavirus-associated death tolls have been increasing rapidly in a country. The provincial status of confirmed cases of coronavirus is higher in Punjab, followed by the Sindh, Khyber Pakhtunkhwa (KPK), and Balochistan. The case fatality ratio shows that KPK has a higher ratio, i.e., 5.11%, followed by the Punjab, i.e., 1.82%; Sindh, i.e., 1.80%; Balochistan, i.e., 1.28%; Gilgit-Baltistan, i.e., 0.71%; and Federal territory, i.e., 0.66%. The country has a less testing capacity to identify more suspected coronavirus patients. The study calculated that if we increase five times our testing capacity from the current date, the total registered cases will be reached to 137,370 and death tolls will increase up to 3090. It is highly needed to increase testing capacity across Pakistan in order to minimize the outbreak of coronavirus. The provincial government should follow the Federal Government instructions to contain coronavirus by increasing testing capacities, tracing suspected patients, smart lockdowns, emergency relief to the poor, and vigilant monitoring system.

title: Variational Recurrent Sequence-to-Sequence Retrieval for Stepwise Illustration
abstract: We address and formalise the task of sequence-to-sequence (seq2seq) cross-modal retrieval. Given a sequence of text passages as query, the goal is to retrieve a sequence of images that best describes and aligns with the query. This new task extends the traditional cross-modal retrieval, where each image-text pair is treated independently ignoring broader context. We propose a novel variational recurrent seq2seq (VRSS) retrieval model for this seq2seq task. Unlike most cross-modal methods, we generate an image vector corresponding to the latent topic obtained from combining the text semantics and context. This synthetic image embedding point associated with every text embedding point can then be employed for either image generation or image retrieval as desired. We evaluate the model for the application of stepwise illustration of recipes, where a sequence of relevant images are retrieved to best match the steps described in the text. To this end, we build and release a new Stepwise Recipe dataset for research purposes, containing 10K recipes (sequences of image-text pairs) having a total of 67K image-text pairs. To our knowledge, it is the first publicly available dataset to offer rich semantic descriptions in a focused category such as food or recipes. Our model is shown to outperform several competitive and relevant baselines in the experiments. We also provide qualitative analysis of how semantically meaningful the results produced by our model are through human evaluation and comparison with relevant existing methods.

title: Epidemiology of Seasonal Coronaviruses: Establishing the Context for the Emergence of Coronavirus Disease 2019
abstract: Public health preparedness for coronavirus (CoV) disease 2019 (COVID-19) is challenging in the absence of setting-specific epidemiological data. Here we describe the epidemiology of seasonal CoVs (sCoVs) and other cocirculating viruses in the West of Scotland, United Kingdom. We analyzed routine diagnostic data for >70 000 episodes of respiratory illness tested molecularly for multiple respiratory viruses between 2005 and 2017. Statistical associations with patient age and sex differed between CoV-229E, CoV-OC43, and CoV-NL63. Furthermore, the timing and magnitude of sCoV outbreaks did not occur concurrently, and coinfections were not reported. With respect to other cocirculating respiratory viruses, we found evidence of positive, rather than negative, interactions with sCoVs. These findings highlight the importance of considering cocirculating viruses in the differential diagnosis of COVID-19. Further work is needed to establish the occurrence/degree of cross-protective immunity conferred across sCoVs and with COVID-19, as well as the role of viral coinfection in COVID-19 disease severity.

title: In silico approach toward the identification of unique peptides from viral protein infection: Application to COVID-19
abstract: We describe a method for rapid in silico selection of diagnostic peptides from newly described viral pathogens and applied this approach to SARS-CoV-2/COVID-19. This approach is multi-tiered, beginning with compiling the theoretical protein sequences from genomic derived data. In the case of SARS-CoV-2 we begin with 496 peptides that would be produced by proteolytic digestion of the viral proteins. To eliminate peptides that would cause cross-reactivity and false positives we remove peptides from consideration that have sequence homology or similar chemical characteristics using a progressively larger database of background peptides. Using this pipeline, we can remove 47 peptides from consideration as diagnostic due to the presence of peptides derived from the human proteome. To address the complexity of the human microbiome, we describe a method to create a database of all proteins of relevant abundance in the saliva microbiome. By utilizing a protein-based approach to the microbiome we can more accurately identify peptides that will be problematic in COVID-19 studies which removes 12 peptides from consideration. To identify diagnostic peptides, another 7 peptides are flagged for removal following comparison to the proteome backgrounds of viral and bacterial pathogens of similar clinical presentation. By aligning the protein sequences of SARS-CoV-2 field isolates deposited to date we can identify peptides for removal due to their presence in highly variable regions that may lead to false negatives as the pathogen evolves. We provide maps of these regions and highlight 3 peptides that should be avoided as potential diagnostic or vaccine targets. Finally, we leverage publicly deposited proteomics data from human cells infected with SARS-CoV-2, as well as a second study with the closely related MERS-CoV to identify the two proteins of highest abundance in human infections. The resulting final list contains the 24 peptides most unique and diagnostic of SARS-CoV-2 infections. These peptides represent the best targets for the development of antibodies are clinical diagnostics. To demonstrate one application of this we model peptide fragmentation using a deep learning tool to rapidly generate targeted LCMS assays and data processing method for detecting CoVID-19 infected patient samples.

title: Current epidemiological and clinical features of COVID-19; a global perspective from China
abstract: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and represents a potentially fatal disease of great global public health importance. As of March 26, 2020, the outbreak of COVID-19 has resulted in 462,801 confirmed cases and 20,839 deaths globally, which is more than those caused by SARS and Middle East respiratory syndrome (MERS) in 2003 and 2013, respectively. The epidemic has posed considerable challenges worldwide. Under a strict mechanism of massive prevention and control, China has seen a rapid decrease in new cases of coronavirus; however, the global situation remains serious. Additionally, the origin of COVID-19 has not been determined and no specific antiviral treatment or vaccine is currently available. Based on the published data, this review systematically discusses the etiology, epidemiology, clinical characteristics, and current intervention measures related to COVID-19 in the hope that it may provide a reference for future studies and aid in the prevention and control of the COVID-19 epidemic.

title: A metaanalysis of bat phylogenetics and positive selection based on genomes and transcriptomes from 18 species
abstract: Historically, the evolution of bats has been analyzed using a small number of genetic loci for many species or many genetic loci for a few species. Here we present a phylogeny of 18 bat species, each of which is represented in 1,107 orthologous gene alignments used to build the tree. We generated a transcriptome sequence of Hypsignathus monstrosus, the African hammer-headed bat, and additional transcriptome sequence for Rousettus aegyptiacus, the Egyptian fruit bat. We then combined these data with existing genomic and transcriptomic data from 16 other bat species. In the analysis of such datasets, there is no clear consensus on the most reliable computational methods for the curation of quality multiple sequence alignments since these public datasets represent multiple investigators and methods, including different source materials (chromosomal DNA or expressed RNA). Here we lay out a systematic analysis of parameters and produce an advanced pipeline for curating orthologous gene alignments from combined transcriptomic and genomic data, including a software package: the Mismatching Isoform eXon Remover (MIXR). Using this method, we created alignments of 11,677 bat genes, 1,107 of which contain orthologs from all 18 species. Using the orthologous gene alignments created, we assessed bat phylogeny and also performed a holistic analysis of positive selection acting in bat genomes. We found that 181 genes have been subject to positive natural selection. This list is dominated by genes involved in immune responses and genes involved in the production of collagens.

title: An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)
abstract: The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a naïve, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th(,) 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented.

title: Positive result of Sars‐Cov‐2 in faeces and sputum from discharged patient with COVID‐19 in Yiwu, China
abstract: With the effective prevention and control of COVID ‐ 19 in China, the number of cured cases increased significantly. Further monitoring of the disease prognosis and effective control of the "relapse" of the epidemic become the next focus of work. To analyse the clinical prognosis of discharged COVID‐19 patients by monitoring their SAR‐CoV‐2 nucleic acid status, which may provide evidence to establish discharge standards and follow‐up management for COVID‐19 patients.  We included 13 discharged COVID‐19 patients who were quarantined for 4‐week at home. The patient's daily clinical signs were recorded and sputum and faecal specimens were regularly sent for the detection of SARS‐CoV‐2 nucleic acid.  The time between initial symptoms and meeting discharge criteria was 18 – 44 days with an average of 25 ± 6 days. The faecal samples of two patients still tested positive after meeting discharge criteria and the sputum samples of four patients returned positive 5 – 14 days after discharge. The rate of a recurring positive test result in samples from the respiratory system was 31%(4/13).  Under the present discharge criteria, the high presence of SARS‐CoV‐2 nucleic acid in faecal and respiratory samples of discharged COVID‐19 patients indicate potential infectivity. Therefore, we suggest that faecal virus nucleic acid should be tested as a routine monitoring index for COVID‐19 and a negative result be added to the criteria. Simultaneously, we should strengthen the regular follow‐up of discharged patients with continuous monitoring of the recurrence of viral nucleic acid. This article is protected by copyright. All rights reserved.

title: Prior and Novel Coronaviruses, COVID-19, and Human Reproduction: What Is Known?
abstract: To summarize current understanding of the effects of novel and prior coronaviruses on human reproduction, specifically male and female gametes, and in pregnancy.   Manuscripts were screened for reports including coronavirus, reproduction, including pathophysiology and pregnancy.  Reproductive outcomes; effects on gametes; pregnancy outcomes; neonatal complications.  Seventy-nine reports formed the basis of the review. Coronavirus binding to cells involves the S1 domain of the spike protein to receptors present in reproductive tissues, including angiotensin converting enzyme-2 (ACE2), CD26, Ezrin, and cyclophilins. SARS-CoV-1 may cause severe orchitis leading to germ cell destruction in males. Reports indicate decreased sperm concentration and motility for 72-90 days following COVID-19 infection. Gonadotropin-dependent expression of ACE2 was found in human ovaries, but it is unclear whether SARS-CoV-2 adversely affects female gametogenesis. Evidence suggests that COVID-19 infection has a lower maternal case fatality rate than SARS or MERS, but anecdotal reports suggest that infected, asymptomatic women may develop respiratory symptoms postpartum. COVID-19 infections in pregnancy are associated with preterm delivery. Postpartum neonatal transmission from mother to child has been reported. Conclusion COVID-19 infection may adversely affect some pregnant women and their offspring. Additional studies are needed to assess effects of SARS-CoV-2 infection on male and female fertility.

title: On the Generation of Medical Dialogues for COVID-19
abstract: Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size, which bear high risk of overfitting, we leverage transfer learning to mitigate data deficiency. Specifically, we take the pretrained models of Transformer, GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune them on our CovidDialog datasets. Experiments demonstrate that these approaches are promising in generating meaningful medical dialogue about COVID-19. But more advanced approaches are needed to build a fully useful dialogue system that can offer accurate COVID-related consultations. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue

title: COVID-19 Trend Estimation in the Elderly Italian Region of Sardinia
abstract: December 2019 saw a novel coronavirus (COVID-19) from China quickly spread globally. Currently, COVID-19, defined as the new pandemic by the World Health Organization (WHO), has reached over 750,000 confirmed cases worldwide. The virus began to spread in Italy from the 22nd February, and the number of related cases is still increasing. Furthermore, given that a relevant proportion of infected people need hospitalization in Intensive Care Units, this may be a crucial issue for National Healthcare System's capacity. WHO underlines the importance of specific disease regional estimates. Because of this, Italy aimed to put in place proportioned and controlled measures, and to guarantee adequate funding to both increase the number of ICU beds and increase production of personal protective equipment. Our aim is to investigate the current COVID-19 epidemiological context in Sardinia region (Italy) and to estimate the transmission parameters using a stochastic model to establish the number of infected, recovered, and deceased people expected. Based on available data from official Italian and regional sources, we describe the distribution of infected cases during the period between 2nd and 15th March 2020. To better reflect the actual spread of COVID-19 in Sardinia based on data from 15th March (first Sardinian declared outbreak), two Susceptible-Infectious-Recovered-Dead (SIRD) models have been developed, describing the best and worst scenarios. We believe that our findings represent a valid contribution to better understand the epidemiological context of COVID-19 in Sardinia. Our analysis can help health authorities and policymakers to address the right interventions to deal with the rapidly expanding health emergency.

title: Racism and discrimination in COVID-19 responses

title: A mathematical model for the coronavirus COVID-19 outbreak
abstract: A mathematical model is proposed for quantitative description of the outbreak of novel coronavirus COVID-19 in China. Although the model is relatively simple, the comparison with the public data shows that an exact solution solution of the model (with the correctly-specified parameters) leads to the results, which are in good agreement with the measured data. Prediction of the total number of the COVID-19 cases is discussed and an example is presented using the measured data in Austria.

title: Open Access and Altmetrics in the pandemic age: Forescast analysis on COVID-19 literature
abstract: We present an analysis on the uptake of open access on COVID-19 related literature as well as the social media attention they gather when compared with non OA papers. We use a dataset of publications curated by Dimensions and analyze articles and preprints. Our sample includes 11,686 publications of which 67.5% are openly accessible. OA publications tend to receive the largest share of social media attention as measured by the Altmetric Attention Score. 37.6% of OA publications are bronze, which means toll journals are providing free access. MedRxiv contributes to 36.3% of documents in repositories but papers in BiorXiv exhibit on average higher AAS. We predict the growth of COVID-19 literature in the following 30 days estimating ARIMA models for the overall publications set, OA vs. non OA and by location of the document (repository vs. journal). We estimate that COVID-19 publications will double in the next 20 days, but non OA publications will grow at a higher rate than OA publications. We conclude by discussing the implications of such findings on the dissemination and communication of research findings to mitigate the coronavirus outbreak.

title: Functional Immune Deficiency Syndrome via Intestinal Infection in COVID-19
abstract: Using a Systems Biology approach, we integrated genomic, transcriptomic, proteomic, and molecular structure information to provide a holistic understanding of the COVID-19 pandemic. The expression data analysis of the Renin Angiotensin System indicates mild nasal, oral or throat infections are likely and that the gastrointestinal tissues are a common primary target of SARS-CoV-2. Extreme symptoms in the lower respiratory system likely result from a secondary-infection possibly by a comorbidity-driven upregulation of ACE2 in the lung. The remarkable differences in expression of other RAS elements, the elimination of macrophages and the activation of cytokines in COVID-19 bronchoalveolar samples suggest that a functional immune deficiency is a critical outcome of COVID-19. We posit that using a non-respiratory system as a major pathway of infection is likely determining the unprecedented global spread of this coronavirus. One Sentence  A Systems Approach Indicates Non-respiratory Pathways of Infection as Key for the COVID-19 Pandemic

title: Point-of-Care Diagnostic Tests for Detecting SARS-CoV-2 Antibodies: A Systematic Review and Meta-Analysis of Real-World Data
abstract: SARS-CoV-2 is responsible for a highly contagious infection, known as COVID-19. SARS-CoV-2 was discovered in late December 2019 and, since then, has become a global pandemic. Timely and accurate COVID-19 laboratory testing is an essential step in the management of the COVID-19 outbreak. To date, assays based on the reverse-transcription polymerase chain reaction (RT-PCR) in respiratory samples are the gold standard for COVID-19 diagnosis. Unfortunately, RT-PCR has several practical limitations. Consequently, alternative diagnostic methods are urgently required, both for alleviating the pressure on laboratories and healthcare facilities and for expanding testing capacity to enable large-scale screening and ensure a timely therapeutic intervention. To date, few studies have been conducted concerning the potential utilization of rapid testing for COVID-19, with some conflicting results. Therefore, the present systematic review and meta-analysis was undertaken to explore the feasibility of rapid diagnostic tests in the management of the COVID-19 outbreak. Based on ten studies, we computed a pooled sensitivity of 64.8% (95%CI 54.5–74.0), and specificity of 98.0% (95%CI 95.8–99.0), with high heterogeneity and risk of reporting bias. We can conclude that: (1) rapid diagnostic tests for COVID-19 are necessary, but should be adequately sensitive and specific; (2) few studies have been carried out to date; (3) the studies included are characterized by low numbers and low sample power, and (4) in light of these results, the use of available tests is currently questionable for clinical purposes and cannot substitute other more reliable molecular tests, such as assays based on RT-PCR.

title: A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening
abstract: We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. To better address such inefficiency, we design a cascaded learning strategy to improve both the sensitivity and the specificity of the resulting DNN classification model. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. The resulting screening system is shown to achieve good classification performance on the expanded dataset, including those newly added COVID-19 CXR images.

title: Bioinformatics Applications in Advancing Animal Virus Research
abstract: Viruses serve as infectious agents for all living entities. There have been various research groups that focus on understanding the viruses in terms of their host-viral relationships, pathogenesis and immune evasion. However, with the current advances in the field of science, now the research field has widened up at the ‘omics’ level. Apparently, generation of viral sequence data has been increasing. There are numerous bioinformatics tools available that not only aid in analysing such sequence data but also aid in deducing useful information that can be exploited in developing preventive and therapeutic measures. This chapter elaborates on bioinformatics tools that are specifically designed for animal viruses as well as other generic tools that can be exploited to study animal viruses. The chapter further provides information on the tools that can be used to study viral epidemiology, phylogenetic analysis, structural modelling of proteins, epitope recognition and open reading frame (ORF) recognition and tools that enable to analyse host-viral interactions, gene prediction in the viral genome, etc. Various databases that organize information on animal and human viruses have also been described. The chapter will converse on overview of the current advances, online and downloadable tools and databases in the field of bioinformatics that will enable the researchers to study animal viruses at gene level.

title: The 2019‐new coronavirus epidemic: Evidence for virus evolution
abstract: There is a worldwide concern about the new coronavirus 2019‐nCoV as a global public health threat. In this article, we provide a preliminary evolutionary and molecular epidemiological analysis of this new virus. A phylogenetic tree has been built using the 15 available whole genome sequences of 2019‐nCoV, 12 whole genome sequences of 2019‐nCoV, and 12 highly similar whole genome sequences available in gene bank (five from the severe acute respiratory syndrome, two from Middle East respiratory syndrome, and five from bat SARS‐like coronavirus). Fast unconstrained Bayesian approximation analysis shows that the nucleocapsid and the spike glycoprotein have some sites under positive pressure, whereas homology modeling revealed some molecular and structural differences between the viruses. The phylogenetic tree showed that 2019‐nCoV significantly clustered with bat SARS‐like coronavirus sequence isolated in 2015, whereas structural analysis revealed mutation in Spike Glycoprotein and nucleocapsid protein. From these results, the new 2019‐nCoV is distinct from SARS virus, probably trasmitted from bats after mutation conferring ability to infect humans.

title: Towards Efficient COVID-19 CT Annotation: A Benchmark for Lung and Infection Segmentation
abstract: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. In this paper, we created a COVID-19 3D CT dataset with 20 cases that contains 1800+ annotated slices and made it publicly available. To promote the development of annotation-efficient deep learning methods, we built three benchmarks for lung and infection segmentation that contain current main research interests, e.g., few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide unified training, validation and testing dataset splits, and evaluation metrics and corresponding code. In addition, we provided more than 40 pre-trained baseline models for the benchmarks, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. To the best of our knowledge, this work presents the largest public annotated COVID-19 CT volume dataset, the first segmentation benchmark, and the most pre-trained models up to now. We hope these resources (\url{https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark}) could advance the development of deep learning methods for COVID-19 CT segmentation with limited data.

title: Mass Spectrometric Identification of SARS-CoV-2 Proteins from Gargle Solution Samples of COVID-19 Patients
abstract: Mass spectrometry (MS) can deliver valuable diagnostic data that complements genomic information and allows us to increase our current knowledge of the COVID-19 disease caused by the SARS-CoV-2 virus. We developed a simple, MS-based method to specifically detect SARS-CoV-2 proteins from gargle solution samples of COVID-19 patients. Our protocol consists of an acetone precipitation and tryptic digestion of proteins contained within the gargle solution, followed by a targeted MS analysis. Our methodology identifies unique peptides originating from SARS-CoV-2 nucleoprotein. Building on these promising initial results, faster MS protocols can now be developed as routine diagnostic tools for COVID-19 patients.

title: Facemask shortage and the novel coronavirus disease (COVID-19) outbreak: Reflections on public health measures
abstract: A novel coronavirus disease (COVID-19) outbreak due to the severe respiratory syndrome coronavirus (SARS-CoV-2) infection occurred in China in late December 2019. Facemask wearing with proper hand hygiene is considered an effective measure to prevent SARS-CoV-2 transmission, but facemask wearing has become a social concern due to the global facemask shortage. China is the major facemask producer in the world, contributing to 50% of global production. However, a universal facemask wearing policy would put an enormous burden on the facemask supply.  We performed a policy review concerning facemasks using government websites and mathematical modelling shortage analyses based on data obtained from the National Health Commission (NHC), the Ministry of Industry and Information Technology (MIIT), the Centre for Disease Control and Prevention (CDC), and General Administration of Customs (GAC) of the People's Republic of China. Three scenarios with respect to wearing facemasks were considered: (1) a universal facemask wearing policy implementation in all regions of mainland China; (2) a universal facemask wearing policy implementation only in the epicentre (Hubei province, China); and (3) no implementation of a universal facemask wearing policy.  Regardless of different universal facemask wearing policy scenarios, facemask shortage would occur but eventually end during our prediction period (from 20 Jan 2020 to 30 Jun 2020). The duration of the facemask shortage described in the scenarios of a country-wide universal facemask wearing policy, a universal facemask wearing policy in the epicentre, and no universal facemask wearing policy were 132, seven, and four days, respectively. During the prediction period, the largest daily facemask shortages were predicted to be 589·5, 49·3, and 37·5 million in each of the three scenarios, respectively. In any scenario, an N95 mask shortage was predicted to occur on 24 January 2020 with a daily facemask shortage of 2·2 million.  Implementing a universal facemask wearing policy in the whole of China could lead to severe facemask shortage. Without effective public communication, a universal facemask wearing policy could result in societal panic and subsequently, increase the nationwide and worldwide demand for facemasks. These increased demands could cause a facemask shortage for healthcare workers and reduce the effectiveness of outbreak control in the affected regions, eventually leading to a pandemic. To fight novel infectious disease outbreaks, such as COVID-19, governments should monitor domestic facemask supplies and give priority to healthcare workers. The risk of asymptomatic transmission and facemask shortages should be carefully evaluated before introducing a universal facemask wearing policy in high-risk regions. Public health measures aimed at improving hand hygiene and effective public communication should be considered along with the facemask policy.

title: Letter to the Editor Re: Coronavirus disease 2019: The harms of exaggerated information and nonevidence‐based measures
abstract: Dr. Ioannidis may cause irreparable harm from recent publications. Novel coronavirus, by definition, arrives without precedence and therefore the reliable data Ioannidis craves. The unshakable facts regarding the Covid-19 consists of the number of deaths, country populations, and the dataset from the Diamond Princess cruise.

title: The ocular surface, coronaviruses and COVID‐19
abstract: The ocular surface has been suggested as a site of infection with Coronavirus‐2 (SARS‐CoV‐2) responsible for the coronavirus disease‐19 (COVID‐19). This review examines the evidence for this hypothesis, and its implications for clinical practice. Severe Acute Respiratory Syndrome Coronavirus‐2 (SARS‐CoV‐2), responsible for the COVID‐19 pandemic, is transmitted by person‐to‐person contact, via airborne droplets, or through contact with contaminated surfaces. SARS‐CoV‐2 binds to angiotensin converting enzyme‐2 (ACE2) to facilitate infection in humans. This review sets out to evaluate evidence for the ocular surface as a route of infection. A literature search in this area was conducted on 15 April 2020 using the Scopus database. In total, 287 results were returned and reviewed. There is preliminary evidence for ACE2 expression on corneal and conjunctival cells, but most of the other receptors to which coronaviruses bind appear to be found under epithelia of the ocular surface. Evidence from animal studies is limited, with a single study suggesting viral particles on the eye can travel to the lung, resulting in very mild infection. Coronavirus infection is rarely associated with conjunctivitis, with occasional cases reported in patients with confirmed COVID‐19, along with isolated cases of conjunctivitis as a presenting sign. Coronaviruses have been rarely isolated from tears or conjunctival swabs. The evidence suggests coronaviruses are unlikely to bind to ocular surface cells to initiate infection. Additionally, hypotheses that the virus could travel from the nasopharynx or through the conjunctival capillaries to the ocular surface during infection are probably incorrect. Conjunctivitis and isolation of the virus from the ocular surface occur only rarely, and overwhelmingly in patients with confirmed COVID‐19. Necessary precautions to prevent person‐to‐person transmission should be employed in clinical practice throughout the pandemic, and patients should be reminded to maintain good hygiene practices.

title: Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries
abstract: Public health measures to prevent, detect, and respond to events are essential to control public health risks, including infectious disease outbreaks, as highlighted in the International Health Regulations (IHR). In light of the outbreak of 2019 novel coronavirus disease (COVID-19), we aimed to review existing health security capacities against public health risks and events.  We used 18 indicators from the IHR State Party Annual Reporting (SPAR) tool and associated data from national SPAR reports to develop five indices: (1) prevent, (2) detect, (3) respond, (4) enabling function, and (5) operational readiness. We used SPAR 2018 data for all of the indicators and categorised countries into five levels across the indices, in which level 1 indicated the lowest level of national capacity and level 5 the highest. We also analysed data at the regional level (using the six geographical WHO regions).  Of 182 countries, 52 (28%) had prevent capacities at levels 1 or 2, and 60 (33%) had response capacities at levels 1 or 2. 81 (45%) countries had prevent capacities and 78 (43%) had response capacities at levels 4 or 5, indicating that these countries were operationally ready. 138 (76%) countries scored more highly in the detect index than in the other indices. 44 (24%) countries did not have an effective enabling function for public health risks and events, including infectious disease outbreaks (7 [4%] at level 1 and 37 [20%] at level 2). 102 (56%) countries had level 4 or level 5 enabling function capacities in place. 32 (18%) countries had low readiness (2 [1%] at level 1 and 30 [17%] at level 2), and 104 (57%) countries were operationally ready to prevent, detect, and control an outbreak of a novel infectious disease (66 [36%] at level 4 and 38 [21%] at level 5).  Countries vary widely in terms of their capacity to prevent, detect, and respond to outbreaks. Half of all countries analysed have strong operational readiness capacities in place, which suggests that an effective response to potential health emergencies could be enabled, including to COVID-19. Findings from local risk assessments are needed to fully understand national readiness capacities in relation to COVID-19. Capacity building and collaboration between countries are needed to strengthen global readiness for outbreak control.

title: Target specific mining of COVID-19 scholarly articles using one-class approach
abstract: In recent years, several research articles have been published in the field of corona-virus caused diseases like severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS) and COVID-19. In the presence of numerous research articles, extracting best-suited articles is time-consuming and manually impractical. The objective of this paper is to extract the activity and trends of corona-virus related research articles using machine learning approaches. The COVID-19 open research dataset (CORD-19) is used for experiments, whereas several target-tasks along with explanations are defined for classification, based on domain knowledge. Clustering techniques are used to create the different clusters of available articles, and later the task assignment is performed using parallel one-class support vector machines (OCSVMs). Experiments with original and reduced features validate the performance of the approach. It is evident that the k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.

title: Association between ambient temperature and COVID-19 infection in 122 cities from China
abstract: Coronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus.  Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail.  The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0–14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209–6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses.  Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.

title: Coronavirus Diseases (COVID-19) Current Status and Future Perspectives: A Narrative Review
abstract: At the end of 2019 a novel virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing severe acute respiratory syndrome expanded globally from Wuhan, China. In March 2020 the World Health Organization declared the SARS-Cov-2 virus a global pandemic. We performed a narrative review to describe existing literature with regard to Corona Virus Disease 2019 (COVID-19) epidemiology, pathophysiology, diagnosis, management and future perspective. MEDLINE, EMBASE and Scopus databases were searched for relevant articles. Although only when the pandemic ends it will be possible to assess the full health, social and economic impact of this global disaster, this review represents a picture of the current state of the art. In particular, we focus on public health impact, pathophysiology and clinical manifestations, diagnosis, case management, emergency response and preparedness.

title: Social Media and Internet-Based Data in Global Systems for Public Health Surveillance: A Systematic Review
abstract: The exchange of health information on the Internet has been heralded as an opportunity to improve public health surveillance. In a field that has traditionally relied on an established system of mandatory and voluntary reporting of known infectious diseases by doctors and laboratories to governmental agencies, innovations in social media and so-called user-generated information could lead to faster recognition of cases of infectious disease. More direct access to such data could enable surveillance epidemiologists to detect potential public health threats such as rare, new diseases or early-level warnings for epidemics. But how useful are data from social media and the Internet, and what is the potential to enhance surveillance? The challenges of using these emerging surveillance systems for infectious disease epidemiology, including the specific resources needed, technical requirements, and acceptability to public health practitioners and policymakers, have wide-reaching implications for public health surveillance in the 21st century.  This article divides public health surveillance into indicator-based surveillance and event-based surveillance and provides an overview of each. We did an exhaustive review of published articles indexed in the databases PubMed, Scopus, and Scirus between 1990 and 2011 covering contemporary event-based systems for infectious disease surveillance.  Our literature review uncovered no event-based surveillance systems currently used in national surveillance programs. While much has been done to develop event-based surveillance, the existing systems have limitations. Accordingly, there is a need for further development of automated technologies that monitor health-related information on the Internet, especially to handle large amounts of data and to prevent information overload. The dissemination to health authorities of new information about health events is not always efficient and could be improved. No comprehensive evaluations show whether event-based surveillance systems have been integrated into actual epidemiological work during real-time health events.  The acceptability of data from the Internet and social media as a regular part of public health surveillance programs varies and is related to a circular challenge: the willingness to integrate is rooted in a lack of effectiveness studies, yet such effectiveness can be proved only through a structured evaluation of integrated systems. Issues related to changing technical and social paradigms in both individual perceptions of and interactions with personal health data, as well as social media and other data from the Internet, must be further addressed before such information can be integrated into official surveillance systems.

title: Gender Differences in Patients With COVID-19: Focus on Severity and Mortality
abstract: The recent outbreak of Novel Coronavirus Disease (COVID-19) is reminiscent of the SARS outbreak in 2003. We aim to compare the severity and mortality between male and female patients with COVID-19 or SARS.  We extracted the data from: (1) a case series of 43 hospitalized patients we treated, (2) a public data set of the first 37 cases of patients who died of COVID-19 and 1,019 patients who survived in China, and (3) data of 524 patients with SARS, including 139 deaths, from Beijing in early 2003.  Older age and a high number of comorbidities were associated with higher severity and mortality in patients with both COVID-19 and SARS. Age was comparable between men and women in all data sets. In the case series, however, men's cases tended to be more serious than women's (P = 0.035). In the public data set, the number of men who died from COVID-19 is 2.4 times that of women (70.3 vs. 29.7%, P = 0.016). In SARS patients, the gender role in mortality was also observed. The percentage of males were higher in the deceased group than in the survived group (P = 0.015).  While men and women have the same prevalence, men with COVID-19 are more at risk for worse outcomes and death, independent of age.

title: Potential magnitude of COVID-19-induced healthcare resource depletion in Ontario, Canada
abstract: The global spread of coronavirus disease 2019 (COVID-19) continues in several jurisdictions, causing significant strain to healthcare systems. The purpose of our study is to predict the impact of the COVID-19 pandemic on patient outcomes and the healthcare system in Ontario, Canada.  We developed an individual-level simulation to model the flow of COVID-19 patients through the Ontario healthcare system. We simulated different combined scenarios of epidemic trajectory and healthcare capacity. Outcomes include numbers of patients needing admission to the ward, Intensive Care Unit (ICU), and requiring ventilation; days to resource depletion; and numbers of patients awaiting resources and deaths associated with limited access to resources.  We demonstrate that with effective early public health measures system resources need not be depleted. For scenarios considering late or ineffective implementation of physical distancing, health system resources would be depleted within 14-26 days. Resource depletion was also avoided or delayed with aggressive measures to rapidly increase ICU, ventilator, and acute care hospital capacity.  We found that without aggressive physical distancing measures the Ontario healthcare system would have been inadequately equipped to manage the expected number of patients with COVID-19, despite the rapid capacity increase. This overall lack of resources would have led to an increase in mortality. By slowing the spread of the disease via ongoing public health measures and having increased healthcare capacity, Ontario may have avoided catastrophic stresses to its health care system.

title: Sharing patient-level real-time COVID-19 data

title: Comparative computational analysis of SARS-CoV-2 nucleocapsid protein epitopes in taxonomically related coronaviruses
abstract: Several research lines are currently ongoing to address the multitude of facets of the pandemic COVID-19. In line with the One-Health concept, extending the target of the studies to the animals which humans are continuously interacting with may favor a better understanding of the SARS-CoV-2 biology and pathogenetic mechanisms; thus, helping to adopt the most suitable containment measures. The last two decades have already faced severe manifestations of the coronavirus infection in both humans and animals, thus, circulating epitopes from previous outbreaks might confer partial protection from SARS-CoV-2 infections. In the present study, we provide an in-silico survey of the major nucleocapsid protein epitopes and compare them with the homologues of taxonomically-related coronaviruses with tropism for animal species that are closely inter-related with the human beings population all over the world. Protein sequence alignment provides evidence of high sequence homology for some of the investigated proteins. Moreover, structural epitope mapping by homology modelling revealed a potential immunogenic value also for specific sequences scoring a lower identity with SARS-CoV-2 nucleocapsid proteins. These evidence provide a molecular structural rationale for a potential role in conferring protection from SARS-CoV-2 infection and identifying potential candidates for the development of diagnostic tools and prophylactic-oriented strategies.

title: Decoding SARS-CoV-2 transmission, evolution and ramification on COVID-19 diagnosis, vaccine, and medicine
abstract: Tremendous effort has been given to the development of diagnostic tests, preventive vaccines, and therapeutic medicines for coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Much of this development has been based on the reference genome collected on January 5, 2020. Based on the genotyping of 6156 genome samples collected up to April 24, 2020, we report that SARS-CoV-2 has had 4459 alarmingly mutations which can be clustered into five subtypes. We introduce mutation ratio and mutation $h$-index to characterize the protein conservativeness and unveil that SARS-CoV-2 envelope protein, main protease, and endoribonuclease protein are relatively conservative, while SARS-CoV-2 nucleocapsid protein, spike protein, and papain-like protease are relatively non-conservative. In particular, the nucleocapsid protein has more than half its genes changed in the past few months, signaling devastating impacts on the ongoing development of COVID-19 diagnosis, vaccines, and drugs.

title: Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning
abstract: The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to theWorld Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

title: COVID-19 Outbreak: An Overview
abstract: In late December 2019, Chinese health authorities reported an outbreak of pneumonia of unknown origin in Wuhan, Hubei Province.  A few days later, the genome of a novel coronavirus was released (http://virological.org/t/novel-2019-coronavirus-genome/319; Wuhan-Hu-1, GenBank accession No. MN908947) and made publicly available to the scientific community. This novel coronavirus was provisionally named 2019-nCoV, now SARS-CoV-2 according to the Coronavirus Study Group of the International Committee on Taxonomy of Viruses. SARS-CoV-2 belongs to the Coronaviridae family, Betacoronavirus genus, subgenus Sarbecovirus. Since its discovery, the virus has spread globally, causing thousands of deaths and having an enormous impact on our health systems and economies. In this review, we summarize the current knowledge about the epidemiology, phylogenesis, homology modeling, and molecular diagnostics of SARS-CoV-2.  Phylogenetic analysis is essential to understand viral evolution, whereas homology modeling is important for vaccine strategies and therapies. Highly sensitive and specific diagnostic assays are key to case identification, contact tracing, identification of the animal source, and implementation of control measures.

title: A scientometric overview of CORD-19
abstract: As the COVID-19 pandemic unfolds, researchers from all disciplines are coming together and contributing their expertise. CORD-19, a dataset of COVID-19 and coronavirus publications, has recently been published alongside calls to help mine the information it contains, and to create tools to search it more effectively. Here, we focus on the delineation of the publications included in CORD-19, and analyse this delineation from a scientometric perspective. We find that CORD-19 contains research not only on COVID-19 and coronaviruses, but on viruses in general. Publications from CORD-19 mostly focus on a few, well-defined areas, including: coronaviruses (primarily SARS, MERS, COVID-19); public health and viral epidemics; the molecular biology of viruses; influenza and other families of viruses; immunology and antivirals; methodology (testing, diagnosing, clinical trials). CORD-19 publications published in 2020, especially focused on topics of pressing relevance (spread, infection, efficacy of counter-measures), are disproportionately popular on social media. While we fully endorse the initiative that led to CORD-19, we also advise to consider its relatively broad content critically.

title: Comprehensive Detection and Identification of Seven Animal Coronaviruses and Human Respiratory Coronavirus 229E with a Microarray Hybridization Assay
abstract: Based on microarray hybridization, a diagnostic test for coronavirus infection was developed using eight coronavirus strains: canine coronavirus (CCoV), feline infectious peritonitis virus (FIPV), feline coronavirus (FCoV), bovine coronavirus (BCoV), porcine respiratory coronavirus (PRCoV), turkey enteritis coronavirus (TCoV), transmissible gastroenteritis virus (TGEV), and human respiratory coronavirus (HRCoV). Up to 104 cDNA clones of eight viruses were obtained by reverse transcription PCR with different pairs of primers designed for each virus and a pair of universal primers designed for the RNA polymerase gene of coronavirus. Total RNAs extracted from virus were reverse transcribed, followed by multi-PCR amplification and labeled with Cy3-dCTP. All labeled cDNAs and prepared gene chips were subjected to specific hybridization. The results showed that extensive cross-reaction existed between CCoV, FCoV, FIPV, TGEV and PRCoV, while there was no cross-reaction between BCoV, TCoV and HRCoV. The ultimate specific gene chip was developed with DNA fragments reamplified from the chosen recombinant plasmids without cross-reaction between different coronaviruses. The hybridization results showed that this gene chip could specifically identify and distinguish the eight coronaviruses and the sensitivity of the chip may be 1,000× more sensitive than PCR, indicating that it can be used for the diagnosis of eight coronavirus infections at the same time.

title: Progress in public health risk communication in China: lessons learned from SARS to H7N9
abstract: Following the SARS outbreak, the World Health Organization revised the International Health Regulations to include risk communication as one of the core capacity areas. In 2006, the U.S. Centers for Disease Control and Prevention’s Global Disease Detection [GDD] program began collaborating with China to enhance China’s risk communication capacity to address gaps in the SARS communication response. This article describes tangible improvements in China’s public health emergency risk communication capacity between the SARS and H7N9 outbreaks; documents U.S. CDC GDD cooperative technical assistance during 2006–2017; and shares lessons learnt to benefit other countries and contribute to enhance global health security.  A questionnaire based on the WHO Joint External Evaluation tool [Risk Communication section] was developed. A key communications official from the China National Health Commission [NHC] completed the questionnaire retrospectively to reflect China’s capacity to manage communication response before, during and after the outbreaks of SARS in 2003, influenza H1N1 in 2009, and influenza H7N9 in 2013. A literature search was also conducted in English and Chinese to further substantiate the results of the questionnaire completed by NHC.  China demonstrated significantly improved risk communication capacities of pre-event, during event and post event responses to H7N9 when compared to the SARS response. China NHC improved its response through preparedness, availability of dedicated staff and resources for risk communication, internal clearance mechanisms, standard operating procedures with national response parties external to NHC, rumor management, communication with international agencies and consistent messaging with healthcare and private sectors. Correspondingly, the perceived level of trust that the public had in the NHC following outbreaks rose between the SARS and H7N9 response.  Risk communication capacities in China have increased during the ten years between the SARS outbreak of 2003 and the H7N9 outbreak of 2013. Long-term risk communication capacity building efforts in bilateral collaborations are uncommon. The U.S. CDC GDD project was one of the first such collaborations worldwide. The lessons learned from this project may benefit lower and middle-income countries as they build their national emergency risk communication capacity.  The online version of this article (10.1186/s12889-019-6778-1) contains supplementary material, which is available to authorized users.

title: Transmission potential of the novel coronavirus (COVID-19) onboard the diamond Princess Cruises Ship, 2020
abstract: An outbreak of COVID-19 developed aboard the Princess Cruises Ship during January–February 2020. Using mathematical modeling and time-series incidence data describing the trajectory of the outbreak among passengers and crew members, we characterize how the transmission potential varied over the course of the outbreak. Our estimate of the mean reproduction number in the confined setting reached values as high as ~11, which is higher than mean estimates reported from community-level transmission dynamics in China and Singapore (approximate range: 1.1–7). Our findings suggest that R(t) decreased substantially compared to values during the early phase after the Japanese government implemented an enhanced quarantine control. Most recent estimates of R(t) reached values largely below the epidemic threshold, indicating that a secondary outbreak of the novel coronavirus was unlikely to occur aboard the Diamond Princess Ship.

title: The Australian Institute of Sport Framework for Rebooting Sport in a COVID-19 Environment
abstract: Sport makes an important contribution to the physical, psychological and emotional well-being of Australians. The economic contribution of sport is equivalent to 2–3% of Gross Domestic Product (GDP). The COVID-19 pandemic has had devastating effects on communities globally, leading to significant restrictions on all sectors of society, including sport. Resumption of sport can significantly contribute to the re-establishment of normality in Australian society. The Australian Institute of Sport (AIS), in consultation with sport partners (National Institute Network (NIN) Directors, NIN Chief Medical Officers (CMOs), National Sporting Organisation (NSO) Presidents, NSO Performance Directors and NSO CMOs), has developed a framework to inform the resumption of sport. National Principles for Resumption of Sport were used as a guide in the development of ‘the AIS Framework for Rebooting Sport in a COVID-19 Environment’ (the AIS Framework); and based on current best evidence, and guidelines from the Australian Federal Government, extrapolated into the sporting context by specialists in sport and exercise medicine, infectious diseases and public health. The principles outlined in this document apply to high performance/professional, community and individual passive (non-contact) sport. The AIS Framework is a timely tool of minimum baseline of standards, for ‘how’ reintroduction of sport activity will occur in a cautious and methodical manner, based on the best available evidence to optimise athlete and community safety. Decisions regarding the timing of resumption (the ‘when’ ) of sporting activity must be made in close consultation with Federal, State/Territory and Local Public Health Authorities. The priority at all times must be to preserve public health, minimising the risk of community transmission.

title: COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches
abstract: COVID-19 is currently one the most life-threatening problems around the world. The fast and accurate detection of the COVID-19 infection is essential to identify, take better decisions and ensure treatment for the patients which will help save their lives. In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods. Both X-ray and CT scan images are considered to evaluate the proposed technique. We employ our Inception Residual Recurrent Convolutional Neural Network with Transfer Learning (TL) approach for COVID-19 detection and our NABLA-N network model for segmenting the regions infected by COVID-19. The detection model shows around 84.67% testing accuracy from X-ray images and 98.78% accuracy in CT-images. A novel quantitative analysis strategy is also proposed in this paper to determine the percentage of infected regions in X-ray and CT images. The qualitative and quantitative results demonstrate promising results for COVID-19 detection and infected region localization.

title: Prior and novel coronaviruses, Coronavirus Disease 2019 (COVID-19), and human reproduction: what is known?
abstract: To summarize current understanding of the effects of novel and prior coronaviruses on human reproduction, specifically male and female gametes, and in pregnancy.  Review of English publications in PubMed and Embase to April 6, 2020.  Articles were screened for reports including coronavirus, reproduction, pathophysiology, and pregnancy.  Reproductive outcomes, effects on gametes, pregnancy outcomes, and neonatal complications. RESULT(S): Seventy-nine reports formed the basis of the review. Coronavirus binding to cells involves the S1 domain of the spike protein to receptors present in reproductive tissues, including angiotensin-converting enzyme-2 (ACE2), CD26, Ezrin, and cyclophilins. Severe Acute Respiratory Syndrome Coronavirus 1 (SARS-CoV-1) may cause severe orchitis leading to germ cell destruction in males. Reports indicate decreased sperm concentration and motility for 72-90 days following Coronavirus Disease 2019 (COVID-19) infection. Gonadotropin-dependent expression of ACE2 was found in human ovaries, but it is unclear whether SARS-Coronavirus 2 (CoV-2) adversely affects female gametogenesis. Evidence suggests that COVID-19 infection has a lower maternal case fatality rate than SARS or Middle East respiratory syndrome (MERS), but anecdotal reports suggest that infected, asymptomatic women may develop respiratory symptoms postpartum. Coronavirus Disease 2019 infections in pregnancy are associated with preterm delivery. Postpartum neonatal transmission from mother to child has been reported. CONCLUSION(S): Coronavirus Disease 2019 infection may affect adversely some pregnant women and their offspring. Additional studies are needed to assess effects of SARS-CoV-2 infection on male and female fertility.

title: An OpenData portal to share COVID-19 drug repurposing data in real time
abstract: The National Center for Advancing Translational Sciences (NCATS) has developed an online open science data portal for its COVID-19 drug repurposing campaign – named OpenData – with the goal of making data across a range of SARS-CoV-2 related assays available in real-time. The assays developed cover a wide spectrum of the SARS-CoV-2 life cycle, including both viral and human (host) targets. In total, over 10,000 compounds are being tested in full concentration-response ranges from across multiple annotated small molecule libraries, including approved drug, repurposing candidates and experimental therapeutics designed to modulate a wide range of cellular targets. The goal is to support research scientists, clinical investigators and public health officials through open data sharing and analysis tools to expedite the development of SARS-CoV-2 interventions, and to prioritize promising compounds and repurposed drugs for further development in treating COVID-19.

title: CoVDB: a comprehensive database for comparative analysis of coronavirus genes and genomes
abstract: The recent SARS epidemic has boosted interest in the discovery of novel human and animal coronaviruses. By July 2007, more than 3000 coronavirus sequence records, including 264 complete genomes, are available in GenBank. The number of coronavirus species with complete genomes available has increased from 9 in 2003 to 25 in 2007, of which six, including coronavirus HKU1, bat SARS coronavirus, group 1 bat coronavirus HKU2, groups 2c and 2d coronaviruses, were sequenced by our laboratory. To overcome the problems we encountered in the existing databases during comparative sequence analysis, we built a comprehensive database, CoVDB (http://covdb.microbiology.hku.hk), of annotated coronavirus genes and genomes. CoVDB provides a convenient platform for rapid and accurate batch sequence retrieval, the cornerstone and bottleneck for comparative gene or genome analysis. Sequences can be directly downloaded from the website in FASTA format. CoVDB also provides detailed annotation of all coronavirus sequences using a standardized nomenclature system, and overcomes the problems of duplicated and identical sequences in other databases. For complete genomes, a single representative sequence for each species is available for comparative analysis such as phylogenetic studies. With the annotated sequences in CoVDB, more specific blast search results can be generated for efficient downstream analysis.

title: Genotyping coronavirus SARS-CoV-2: methods and implications
abstract: The emerging global infectious COVID-19 coronavirus disease by novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) presents critical threats to global public health and the economy since it was identified in late December 2019 in China. The virus has gone through various pathways of evolution. For understanding the evolution and transmission of SARS-CoV-2, genotyping of virus isolates is of great importance. We present an accurate method for effectively genotyping SARS-CoV-2 viruses using complete genomes. The method employs the multiple sequence alignments of the genome isolates with the SARS-CoV-2 reference genome. The SNP genotypes are then measured by Jaccard distances to track the relationship of virus isolates. The genotyping analysis of SARS-CoV-2 isolates from the globe reveals that specific multiple mutations are the predominated mutation type during the current epidemic. Our method serves a promising tool for monitoring and tracking the epidemic of pathogenic viruses in their gradual and local genetic variations. The genotyping analysis shows that the genes encoding the S proteins and RNA polymerase, RNA primase, and nucleoprotein, undergo frequent mutations. These mutations are critical for vaccine development in disease control.

title: A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19
abstract: The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researches to continue to maximize the advantages of AI and big data to fight COVID-19.

title: In silico design and validation of commercial kit GPS™ CoVID-19 dtec-RT-qPCR Test under criteria of UNE/EN ISO 17025:2005 and ISO/IEC 15189:2012
abstract: The Corona Virus Disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has become a serious infectious disease affecting human health worldwide and rapidly declared a pandemic by WHO. Early, several RT-qPCR were designed by using only the first SARS-CoV-2 genome sequence.  A few days later, when additional SARS-CoV-2 genome were retrieved, the kit GPS™ CoVID-19 dtec-RT-qPCR Test was designed to provide a highly specific detection method and commercially available worldwide. The kit was validated following criteria recommended by the UNE/EN ISO 17025:2005 and ISO/IEC 15189:2012.  The present study approached the in silico specificity of the GPS™ CoVID-19 dtec-RT-qPCR Test and RT-qPCR designs currently published. The empirical validation parameters specificity (inclusivity/exclusivity), quantitative phase analysis (10-106 copies), reliability (repeatability/reproducibility) and sensitivity (detection/quantification limits) were evaluated for a minimum of 10-15 assays. Diagnostic validation was achieved by two independent reference laboratories, the Instituto de Salud Carlos III (ISCIII), (Madrid, Spain) and the Public Health England (PHE; Colindale, London, UK).  The GPS™ RT-qPCR primers and probe showed the highest number of mismatches with the closet related non-SARS-CoV-2 coronavirus, including some indels. The kits passed all parameters of validation with strict acceptance criteria. Results from reference laboratories 100% correlated with these obtained by suing reference methods and received an evaluation with 100% of diagnostic sensitivity and specificity.  The GPS™ CoVID-19 dtec-RT-qPCR Test, available with full analytical and diagnostic validation, represents a case of efficient transfer of technology being successfully used since the pandemic was declared. The analysis suggested the GPS™ CoVID-19 dtec-RT-qPCR Test is the more exclusive by far.

title: Clinical features, Diagnosis, and Treatment of COVID-19: A systematic review of case reports and case series
abstract: The 2019 novel coronavirus (COVID-19) has been declared a public health emergency worldwide. The objective of this systematic review was to characterize the clinical, diagnostic, and treatment characteristics of patients presenting with COVID-19.  We conducted a structured search using PubMed/Medline, Embase, Web of Science and the Cochrane Library to collect both case reports and case series on COVID-19 published up to February 30, 2020.  Thirty-four articles were included analyzing a total of 99 patients with a mean age of 46.2 years. The most common presenting symptom in patients who tested positive for COVID-19 was fever, reported in up to 83% of patients from 76.4% of the analyzed studies. Other symptoms including rhinorrhea, dizziness, and chills were less frequently reported. Additionally, in studies which reported C-reactive protein (CRP) measurements (44%), a large majority of patients displayed an elevated CRP (73%). Progression to acute respiratory distress syndrome (ARDS) was the most common complication of patients testing positive for COVID-19 (33%). CT images displayed ground-glass opacification (GGO) patterns (80%) as well as bilateral lung involvement (71.0%). The most commonly used antiviral treatment modalities included, lopinavir (HIV protease inhibitor), arbidiol hydrochloride (influenza fusion inhibitor), and oseltamivir (neuraminidase inhibitor).  Development of ARDS may play a role in estimating disease progression and mortality risk. Early detection of elevations in serum CRP, combined with a clinical COVID-19 symptom presentation may be used as a surrogate marker for presence and severity of disease. There is a paucity of data surrounding the efficacy of treatments. There is currently not a well-established gold standard therapy for the treatment of diagnosed COVID-19. Further prospective investigations are necessary.

title: Can we distinguish respiratory viral infections based on clinical features? A prospective pediatric cohort compared to systematic literature review
abstract: Studies have shown that the predictive value of “clinical diagnoses” of influenza and other respiratory viral infections is low, especially in children. In routine care, pediatricians often resort to clinical diagnoses, even in the absence of robust evidence‐based criteria. We used a dual approach to identify clinical characteristics that may help to differentiate infections with common pathogens including influenza, respiratory syncytial virus, adenovirus, metapneumovirus, rhinovirus, bocavirus‐1, coronaviruses, or parainfluenza virus: (a) systematic review and meta‐analysis of 47 clinical studies published in Medline (June 1996 to March 2017,  comprising 49 858 individuals and (b) data‐driven analysis of an inception cohort of 6073 children with ILI (aged 0‐18 years, 56% male, December 2009 to March 2015) examined at the point of care in addition to blinded PCR testing. We determined pooled odds ratios for the literature analysis and compared these to odds ratios based on the clinical cohort dataset. This combined analysis suggested significant associations between influenza and fever or headache, as well as between respiratory syncytial virus infection and cough, dyspnea, and wheezing. Similarly, literature and cohort data agreed on significant associations between HMPV infection and cough, as well as adenovirus infection and fever. Importantly, none of the abovementioned features were unique to any particular pathogen but were also observed in association with other respiratory viruses. In summary, our “real‐world” dataset confirmed published literature trends, but no individual feature allows any particular type of viral infection to be ruled in or ruled out. For the time being, laboratory confirmation remains essential. More research is needed to develop scientifically validated decision models to inform best practice guidelines and targeted diagnostic algorithms.

title: Phase adjusted estimation of the number of 2019 novel coronavirus cases in Wuhan, China
abstract: An outbreak of clusters of viral pneumonia due to a novel coronavirus (2019-nCoV / SARS-CoV-2) happened in Wuhan, Hubei Province in China in December 2019. Since the outbreak, several groups reported estimated R0 of Coronavirus Disease 2019 (COVID-19) and generated valuable prediction for the early phase of this outbreak. After implementation of strict prevention and control measures in China, new estimation is needed. An infectious disease dynamics SEIR (Susceptible, Exposed, Infectious and Removed) model was applied to estimate the epidemic trend in Wuhan, China under two assumptions of Rt. In the first assumption, Rt was assumed to maintain over 1. The estimated number of infections would continue to increase throughout February without any indication of dropping with Rt = 1.9, 2.6 or 3.1. The number of infections would reach 11,044, 70,258 and 227,989, respectively, by 29 February 2020. In the second assumption, Rt was assumed to gradually decrease at different phases from high level of transmission (Rt = 3.1, 2.6 and 1.9) to below 1 (Rt = 0.9 or 0.5) owing to increasingly implemented public heath intervention. Several phases were divided by the dates when various levels of prevention and control measures were taken in effect in Wuhan. The estimated number of infections would reach the peak in late February, which is 58,077-84,520 or 55,869-81,393. Whether or not the peak of the number of infections would occur in February 2020 may be an important index for evaluating the sufficiency of the current measures taken in China. Regardless of the occurrence of the peak, the currently strict measures in Wuhan should be continuously implemented and necessary strict public health measures should be applied in other locations in China with high number of COVID-19 cases, in order to reduce Rt to an ideal level and control the infection.

title: Systematic review: surveillance systems for early detection of bioterrorism-related diseases.
abstract: Given the threat of bioterrorism and the increasing availability of electronic data for surveillance, surveillance systems for the early detection of illnesses and syndromes potentially related to bioterrorism have proliferated.  To critically evaluate the potential utility of existing surveillance systems for illnesses and syndromes related to bioterrorism. DATA SOURCES Databases of peer-reviewed articles (for example, MEDLINE for articles published from January 1985 to April 2002) and Web sites of relevant government and nongovernment agencies.  Reports that described or evaluated systems for collecting, analyzing, or presenting surveillance data for bioterrorism-related illnesses or syndromes.  From each included article, the authors abstracted information about the type of surveillance data collected; method of collection, analysis, and presentation of surveillance data; and outcomes of evaluations of the system. DATA SYNTHESIS 17,510 article citations and 8088 government and nongovernmental Web sites were reviewed. From these, the authors included 115 systems that collect various surveillance reports, including 9 syndromic surveillance systems, 20 systems collecting bioterrorism detector data, 13 systems collecting influenza-related data, and 23 systems collecting laboratory and antimicrobial resistance data. Only the systems collecting syndromic surveillance data and detection system data were designed, at least in part, for bioterrorism preparedness applications. Syndromic surveillance systems have been deployed for both event-based and continuous bioterrorism surveillance. Few surveillance systems have been comprehensively evaluated. Only 3 systems have had both sensitivity and specificity evaluated.  Data from some existing surveillance systems (particularly those developed by the military) may not be publicly available.  Few surveillance systems have been specifically designed for collecting and analyzing data for the early detection of a bioterrorist event. Because current evaluations of surveillance systems for detecting bioterrorism and emerging infections are insufficient to characterize the timeliness or sensitivity and specificity, clinical and public health decision making based on these systems may be compromised.

title: Coronavirus disease (COVID-19) in neurology and neurosurgery: A scoping review of the early literature
abstract: Coronavirus disease 2019 (COVID-19) is a devastating respiratory illness that has dramatically changed the medical landscape around the world. In parallel with a rise in the number of cases globally, the COVID-19 literature has rapidly expanded with experts around the world disseminating knowledge and collaborating on best practices. To date, the literature has predominantly consisted of case reports, case series, and systemic protocols for dealing with this deadly disease from a plethora of specialties with larger observational and randomized studies only now starting to emerge. This scoping review of MEDLINE, EMBASE, SCOPUS, and the Cochrane Library aims to evaluate and summarize the current status of the COVID-19 literature at it applies to neurology and neurosurgery. Neurological symptomatology, neurological risk factors for poor prognosis, pathophysiology for neuroinvasion, and actions taken by neurological or neurosurgical services to manage the current COVID-19 crisis are reviewed.

title: COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature.
abstract: The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. We also designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared clinical symptoms and clinical findings of COVID-19 vs. those of influenza to demonstrate the disease differences in the scientific publications. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.

title: Bulk and single-cell transcriptomics identify tobacco-use disparity in lung gene expression of ACE2, the receptor of 2019-nCov
abstract: In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed five large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn't find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker's lung compared to non-smoker's lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

title: Genomic variance of the 2019-nCoV coronavirus
abstract: There is rising global concern for the recently emerged novel Coronavirus (2019-nCov). Full genomic sequences have been released by the worldwide scientific community in the last few weeks in order to understand the evolutionary origin and molecular characteristics of this virus. Taking advantage of all the genomic information currently available, we constructed a phylogenetic tree including also representatives of other coronaviridae, such as Bat coronavirus (BCoV) and SARS. We confirm high sequence similarity (>99%) between all sequenced 2019-nCoVs genomes available, with the closest BCoV sequence sharing 96.2% sequence identity, confirming the notion of a zoonotic origin of 2019-nCoV. Despite the low heterogeneity of the 2019-nCoV genomes, we could identify at least two hyper-variable genomic hotspots, one of which is responsible for a Serine/Leucine variation in the viral ORF8-encoded protein. Finally, we perform a full proteomic comparison with other coronaviridae, identifying key aminoacidic differences to be considered for antiviral strategies deriving from previous anti-coronavirus approaches.

title: Genetic variability in the expression of the SARS-CoV-2 host cell entry factors across populations
abstract: The entry of SARS-CoV-2 into host cells is dependent upon angiotensin-converting enzyme 2 (ACE2), which serves as a functional attachment receptor for the viral spike glycoprotein, and the serine protease TMPRSS2 which allows fusion of the viral and host cell membranes. We devised a quantitative measure to estimate genetic determinants of ACE2 and TMPRSS2 expression and applied this measure to >2,500 individuals. Our data show significant variability in genetic determinants of ACE2 and TMPRSS2 expression among individuals and between populations, and demonstrate a genetic predisposition for lower expression levels of both key viral entry genes in African populations. These data suggest that genetic factors might lead to lower susceptibility for SARS-CoV-2 infection in African populations and that host genetics might help explain inter-individual variability in disease susceptibility and severity of COVID-19.

title: Preying on Public Fears and Anxieties in a Pandemic: Businesses Selling Unproven and Unlicensed “Stem Cell Treatments” for COVID-19
abstract: In the midst of a global public health emergency, some businesses are taking advantage of widespread fears by marketing purported stem cell treatments for COVID-19. Such businesses target prospective clients with misleading claims, expose patients to potentially risky stem cells-based products, and undermine efforts to develop evidence-based treatments for COVID-19.

title: The dark cloud with a silver lining: Assessing the impact of the SARS COVID-19 pandemic on the global environment
abstract: The Severe Acute Respiratory Syndrome-Coronavirus Disease 2019 (COVID-19) pandemic caused by a novel coronavirus known as SARS-CoV-2 has caused tremendous suffering and huge economic losses. We hypothesized that extreme measures of partial-to-total shutdown might have influenced the quality of the global environment because of decreased emissions of atmospheric pollutants. We tested this hypothesis using satellite imagery, climatic datasets (temperature, and absolute humidity), and COVID-19 cases available in the public domain. While the majority of the cases were recorded from Western countries, where mortality rates were strongly positively correlated with age, the number of cases in tropical regions was relatively lower than European and North American regions, possibly attributed to faster human-to-human transmission. There was a substantial reduction in the level of nitrogen dioxide (NO2: 0.00002 mol m-2), a low reduction in CO (<0.03 mol m-2), and a low-to-moderate reduction in Aerosol Optical Depth (AOD: ~0.1-0.2) in the major hotspots of COVID-19 outbreak during February-March 2020, which may be attributed to the mass lockdowns. Our study projects an increasing coverage of high COVID-19 hazard at absolute humidity levels ranging from 4 to 9 g m-3 across a large part of the globe during April-July 2020 due to a high prospective meteorological suitability for COVID-19 spread. Our findings suggest that there is ample scope for restoring the global environment from the ill-effects of anthropogenic activities through temporary shutdown measures.

title: A Big Data Based Framework for Executing Complex Query Over COVID-19 Datasets (COVID-QF)
abstract: COVID-19's rapid global spread has driven innovative tools for Big Data Analytics. These have guided organizations in all fields of the health industry to track and minimized the effects of virus. Researchers are required to detect coronaviruses through artificial intelligence, machine learning, and natural language processing, and to gain a complete understanding of the disease. COVID-19 takes place in different countries in the world, with which only big data application and the work of NOSQL databases are suitable. There is a great number of platforms used for processing NOSQL Databases model like: Spark, H2O and Hadoop HDFS/MapReduce, which are proper to control and manage the enormous amount of data. Many challenges faced by large applications programmers, especially those that work on the COVID-19 databases through hybrid data models through different APIs and query. In this context, this paper proposes a storage framework to handle both SQL and NOSQL databases named (COVID-QF) for COVID-19 datasets in order to treat and handle the problems caused by virus spreading worldwide by reducing treatment times. In case of NoSQL database, COVID-QF uses Hadoop HDFS/Map Reduce and Apache Spark. The COVID-QF consists of three Layers: data collection layer, storage layer, and query Processing layer. The data is collected in the data collection layer. The storage layer divides data into collection of data-saving and processing blocks, and it connects the Connector of the spark with different databases engine to reduce time of saving and retrieving. While the Processing layer executes the request query and sends results. The proposed framework used three datasets increased for time for COVID-19 data (COVID-19-Merging, COVID-19-inside-Hubei and COVID-19-ex-Hubei) to test experiments of this study. The results obtained insure the superiority of the COVID-QF framework.

title: Early dynamics of transmission and control of COVID-19: a mathematical modelling study
abstract: An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced.  We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020.  We estimated that the median daily reproduction number (R(t)) in Wuhan declined from 2·35 (95% CI 1·15–4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41–2·39) 1 week after. Based on our estimates of R(t), assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population.  Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually.

title: Noninvasive ventilation in acute respiratory failure due to H1N1 influenza: A word of caution

title: Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset
abstract: The objective of this work is to explore popular discourse about the COVID-19 pandemic and policies implemented to manage it. Using Natural Language Processing, Text Mining, and Network Analysis to analyze corpus of tweets that relate to the COVID-19 pandemic, we identify common responses to the pandemic and how these responses differ across time. Moreover, insights as to how information and misinformation were transmitted via Twitter, starting at the early stages of this pandemic, are presented. Finally, this work introduces a dataset of tweets collected from all over the world, in multiple languages, dating back to January 22nd, when the total cases of reported COVID-19 were below 600 worldwide. The insights presented in this work could help inform decision makers in the face of future pandemics, and the dataset introduced can be used to acquire valuable knowledge to help mitigate the COVID-19 pandemic.

title: CAiRE-COVID: A Question Answering and Multi-Document Summarization System for COVID-19 Research
abstract: To address the need for refined information in COVID-19 pandemic, we propose a deep learning-based system that uses state-of-the-art natural language processing (NLP) question answering (QA) techniques combined with summarization for mining the available scientific literature. Our system leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from the existing literature given a query. Fluent summaries are also provided to help understand the content in a more efficient way. In this paper, we describe our CAiRE-COVID system architecture and methodology for building the system. To bootstrap the further study, the code for our system is available at

title: Artificial Intelligence-Powered Search Tools and Resources in the Fight Against COVID-19
abstract: Emerging technologies are set to play an important role in our response to the COVID-19 pandemic. This paper explores three prominent initiatives: COVID-19 focused datasets (e.g., CORD-19); Artificial intelligence-powered search tools (e.g., WellAI, SciSight); and contact tracing based on mobile communication technology. We believe that increasing awareness of these tools will be important in future research into the disease, COVID-19, and the virus, SARS-CoV-2.

title: Mortality Rate of Infection With COVID-19 in Korea From the Perspective of Underlying Disease
abstract: On December 31, 2019 the China National Health Commission (NHC) reported that an unknown cause of pneumonia had been detected in Wuhan in Hubei province. On February 12, the disease caused by the novel coronavirus (2019-nCoV) was given a formal name, COVID-19. On January 20, 2020, the first case of COVID-19 was confirmed in Korea. The age-specific death rate was the highest among patients over 70 years of age, with underlying diseases in their circulatory system, such as myocardial infarction, cerebral infraction, arrythmia, and hypertension. Patients with underlying disease who are 70 years of age or older should recognize that there is a high possibility of developing a serious disease in case of viral infection and follow strict precautions.

title: No more business as usual: agile and effective responses to emerging pathogen threats require open data and open analytics
abstract: The current state of much of the Wuhan pneumonia virus (COVID-19) research shows a regrettable lack of data sharing and considerable analytical obfuscation. This impedes global research cooperation, which is essential for tackling public health emergencies, and requires unimpeded access to data, analysis tools, and computational infrastructure. Here we show that community efforts in developing open analytical software tools over the past ten years, combined with national investments into scientific computational infrastructure, can overcome these deficiencies and provide an accessible platform for tackling global health emergencies in an open and transparent manner. Specifically, we use all COVID-19 genomic data available in the public domain so far to (1) underscore the importance of access to raw data and to (2) demonstrate that existing community efforts in curation and deployment of biomedical software can reliably support rapid, reproducible research during global health crises. All our analyses are fully documented at https://github.com/galaxyproject/SARS-CoV-2.

title: Combining Absolute and Relative Information with Frequency Distributions for Ordinal Classification
abstract: A large amount of labelled data (absolute information) is usually needed for an ordinal classifier to attain a good performance. As shown in a recent paper by the present authors, the lack of a large amount of absolute information can be overcome by additionally considering some side information in the form of relative information, thus augmenting the method of nearest neighbors. In this paper, we adapt the method of nearest neighbors for dealing with a specific type of relative information: frequency distributions of pairwise comparisons (rather than a single pairwise comparison). We test the proposed method on some classical machine learning datasets and demonstrate its effectiveness.

title: The Coronavirus 2019 pandemic in Canada: the impact of public health interventions on the course of the outbreak in Alberta and other provinces
abstract: The SARS-CoV-2 disease 2019 (COVID-19) pandemic has spread across the world with varying impact on health systems and outcomes. We assessed how the type and timing of public- health interventions impacted the course of the outbreak in Alberta and other Canadian provinces.  We used publicly-available data to summarize rates of laboratory data and mortality in relation to measures implemented to contain the outbreak and testing strategy. We estimated the transmission potential of SARS-CoV-2 before the state of emergency declaration for each province (R0) and at the study end date (Rt).  The first cases were confirmed in Ontario (January 25) and British Columbia (January 28). All provinces implemented the same health-policy measures between March 12 and March 30. Alberta had a higher percentage of the population tested (3.8%) and a lower mortality rate (3/100,000) than Ontario (2.6%; 11/100,000) or Quebec (3.1%; 31/100,000). British Columbia tested fewer people (1.7%) and had similar mortality as Alberta. Data on provincial testing strategies were insufficient to inform further analyses. Mortality rates increased with increasing rates of lab- confirmed cases in Ontario and Quebec, but not in Alberta. R0 was similar across all provinces, but varied widely from 2.6 (95% confidence intervals 1.9-3.4) to 6.4 (4.3-8.5), depending on the assumed time interval between onset of symptoms in a primary and a secondary case (serial interval). The outbreak is currently under control in Alberta, British Columbia and Nova Scotia (Rt <1).  COVID-19-related health outcomes varied by province despite rapid implementation of similar health-policy interventions across Canada. Insufficient information about provincial testing strategies and a lack of primary data on serial interval are major limitations of existing data on the Canadian COVID-19 outbreak.

title: Signals of T(h)2 immune response from COVID-19 patients requiring intensive care

title: Nonstructural proteins NS7b and NS8 are likely to be phylogenetically associated with evolution of 2019-nCoV
abstract: The seventh novel human infecting Betacoronavirus that causes pneumonia (2019 novel coronavirus, 2019-nCoV) originated in Wuhan, China. The evolutionary relationship between 2019-nCoV and the other human respiratory illness-causing coronavirus is not closely related. We sought to characterize the relationship of the translated proteins of 2019-nCoV with other species of Orthocoronavirinae. A phylogenetic tree was constructed from the genome sequences. A cluster tree was developed from the profiles retrieved from the presence and absence of homologs of ten 2019-nCoV proteins. The combined data were used to characterize the relationship of the translated proteins of 2019-nCoV to other species of Orthocoronavirinae. Our analysis reliably suggests that 2019-nCoV is most closely related to BatCoV RaTG13 and belongs to subgenus Sarbecovirus of Betacoronavirus, together with SARS coronavirus and Bat-SARS-like coronavirus. The phylogenetic profiling cluster of homolog proteins of one annotated 2019-nCoV protein against other genome sequences revealed two clades of ten 2019-nCoV proteins. Clade 1 consisted of a group of conserved proteins in Orthocoronavirinae comprising Orf1ab polyprotein, Nucleocapsid protein, Spike glycoprotein, and Membrane protein. Clade 2 comprised six proteins exclusive to Sarbecovirus and Hibecovirus. Two of six Clade 2 nonstructural proteins, NS7b and NS8, were exclusively conserved among 2019-nCoV, BetaCoV_RaTG, and BatSARS-like Cov. NS7b and NS8 have previously been shown to affect immune response signaling in the SARS-CoV experimental model. Thus, we speculated that knowledge of the functional changes in the NS7b and NS8 proteins during evolution may provide important information to explore the human infective property of 2019-nCoV.

title: RetinaMask: A Face Mask detector
abstract: Coronavirus disease 2019 has affected the world seriously. One major protection method for people is to wear masks in public areas. Furthermore, many public service providers require customers to use the service only if they wear masks correctly. However, there are only a few research studies about face mask detection based on image analysis. In this paper, we propose RetinaFaceMask, which is a high-accuracy and efficient face mask detector. The proposed RetinaFaceMask is a one-stage detector, which consists of a feature pyramid network to fuse high-level semantic information with multiple feature maps, and a novel context attention module to focus on detecting face masks. In addition, we also propose a novel cross-class object removal algorithm to reject predictions with low confidences and the high intersection of union. Experiment results show that RetinaFaceMask achieves state-of-the-art results on a public face mask dataset with $2.3\%$ and $1.5\%$ higher than the baseline result in the face and mask detection precision, respectively, and $11.0\%$ and $5.9\%$ higher than baseline for recall. Besides, we also explore the possibility of implementing RetinaFaceMask with a light-weighted neural network MobileNet for embedded or mobile devices.

title: Keep genome data freely accessible

title: Persistence of intestinal SARS-CoV-2 infection in patients with COVID-19 leads to re-admission after pneumonia resolved
abstract: The current reports of COVID-19 focus on the respiratory system, but intestinal infections caused by SARS-CoV-2 are also worthy of attention. Here we report persistence of intestinal SARS-CoV-2 infection in three cases with COVID-19 leads to re-admission after pneumonia resolved.

title: Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
abstract: The explosion of disinformation related to the COVID-19 pandemic has overloaded fact-checkers and media worldwide. To help tackle this, we developed computational methods to support COVID-19 disinformation debunking and social impacts research.  the currently largest available manually annotated COVID-19 disinformation category dataset; and 2) a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework. We demonstrate that CANTM efficiently improves classification performance with low resources, and is scalable. In addition, the classification-aware topics help researchers and end-users to better understand the classification results.

title: Global academic response to COVID-19: Cross-sectional study
abstract: To describe the global academic response to COVID-19 during its early stages. The responsiveness of investigators, editorial teams, and publishers was explored.  A parallel search of Middle East Respiratory Syndrome (MERS) literature was performed for comparison of outcomes.  MEDLINE and EMBASE databases. The search for COVID-19 studies was performed between 1st November 2019 and 24th March 2020. The search for MERS studies was performed one year earlier between 1st November 2018 and 24th March 2019.  Investigator-responsiveness was assessed by measuring the volume and type of published research. Editorial-responsiveness was assessed by measuring the time from manuscript submission to acceptance and the availability of original data to support the study results. Publisher-responsiveness was assessed by measuring the time from manuscript acceptance to first publication and the provision of open access.  In total, 398 of 2835 COVID-19 and 55 of 1513 MERS search results were eligible. Most COVID-19 studies were clinical reports (n=242; 60.8%) and the majority of these were case series (n=105; 43.4%) and single cases (n=65; 26.9%). The times from manuscript submission to acceptance (median: 5 days (IQR: 3-11) vs 71.5 days (38-106); P<0.001) and acceptance to publication (median: 5 days (IQR: 2-8) vs. 22.5 days (4-48.5-; P<0.001) were strikingly shorter for COVID-19. Almost all COVID-19 (n=396; 99.5%) and MERS (n=55; 100%) studies were available with open-access. Data sharing was infrequent, with original data available for 104 (26.1%) COVID-19 and 10 (18.2%) MERS studies (P=0.203).  The early academic response to COVID-19 was characterised by investigators aiming to define the disease. These studies were made rapidly and openly available by editorial and publishing teams. Data sharing practises are an essential target for improvement as the pandemic progresses.

title: Brief Review on COVID-19: The 2020 Pandemic Caused by SARS-CoV-2
abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for the coronavirus disease of 2019 (COVID-19). First identified in Wuhan (Hubei, China) in December of 2019, it has since been declared a pandemic by the World Health Organization in March of 2020. In this study, we will provide a brief review of viral origin, identification, symptoms, transmission, diagnosis, and potential treatment strategies for the newly identified SARS-CoV-2 strain.

title: Rapidly Bootstrapping a Question Answering Dataset for COVID-19
abstract: We present CovidQA, the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. To our knowledge, this is the first publicly available resource of its type, and intended as a stopgap measure for guiding research until more substantial evaluation resources become available. While this dataset, comprising 124 question-article pairs as of the present version 0.1 release, does not have sufficient examples for supervised machine learning, we believe that it can be helpful for evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19. This paper describes our methodology for constructing the dataset and presents the effectiveness of a number of baselines, including term-based techniques and various transformer-based models. The dataset is available at

title: Statistical analysis and visualization of the potential cases of pandemic coronavirus
abstract: A local outbreak of initially unknown cause pneumonia was detected in Wuhan (Hubei, China) in December 2019 and a novel coronavirus, the severe acute respiratory syndrome coronavirus 2, was quickly found to be causing it. Since then, the epidemic has spread to all of China's mainland provinces as well as 58 other countries and territories, with more than 87,137 confirmed cases around the globe, including 79,968 from China, 7169 from other countries as of 1 March 2020, as stated by the World Health Organization in the COVID-19 situation report-41. In response to this current public health emergency, this study done a statistical analysis and visualized reported cases of coronavirus disease 2019 (COVID-19) based on the open data collection provided by Johns Hopkins University. Where the location and number of confirmed infected cases have been shown, there have also been deaths, recovered cases and comparisons of the growth rates between the Globe countries. This was intended to provide researchers, public health officials and the general public with exposure to the epidemic.

title: Emergence of a Novel Coronavirus (COVID-19): Protocol for Extending Surveillance Used by the Royal College of General Practitioners Research and Surveillance Centre and Public Health England
abstract: The Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) and Public Health England (PHE) have successfully worked together on the surveillance of influenza and other infectious diseases for over 50 years, including three previous pandemics. With the emergence of the international outbreak of the coronavirus infection (COVID-19), a UK national approach to containment has been established to test people suspected of exposure to COVID-19. At the same time and separately, the RCGP RSC's surveillance has been extended to monitor the temporal and geographical distribution of COVID-19 infection in the community as well as assess the effectiveness of the containment strategy.  The aims of this study are to surveil COVID-19 in both asymptomatic populations and ambulatory cases with respiratory infections, ascertain both the rate and pattern of COVID-19 spread, and assess the effectiveness of the containment policy.  The RCGP RSC, a network of over 500 general practices in England, extract pseudonymized data weekly. This extended surveillance comprises of five components: (1) Recording in medical records of anyone suspected to have or who has been exposed to COVID-19. Computerized medical records suppliers have within a week of request created new codes to support this. (2) Extension of current virological surveillance and testing people with influenza-like illness or lower respiratory tract infections (LRTI)-with the caveat that people suspected to have or who have been exposed to COVID-19 should be referred to the national containment pathway and not seen in primary care. (3) Serology sample collection across all age groups. This will be an extra blood sample taken from people who are attending their general practice for a scheduled blood test. The 100 general practices currently undertaking annual influenza virology surveillance will be involved in the extended virological and serological surveillance. (4) Collecting convalescent serum samples. (5) Data curation. We have the opportunity to escalate the data extraction to twice weekly if needed. Swabs and sera will be analyzed in PHE reference laboratories.  General practice clinical system providers have introduced an emergency new set of clinical codes to support COVID-19 surveillance. Additionally, practices participating in current virology surveillance are now taking samples for COVID-19 surveillance from low-risk patients presenting with LRTIs. Within the first 2 weeks of setup of this surveillance, we have identified 3 cases: 1 through the new coding system, the other 2 through the extended virology sampling.  We have rapidly converted the established national RCGP RSC influenza surveillance system into one that can test the effectiveness of the COVID-19 containment policy. The extended surveillance has already seen the use of new codes with 3 cases reported. Rapid sharing of this protocol should enable scientific critique and shared learning.

title: Unsupervised Bootstrapping of Active Learning for Entity Resolution
abstract: Entity resolution is one of the central challenges when integrating data from large numbers of data sources. Active learning for entity resolution aims to learn high-quality matching models while minimizing the human labeling effort by selecting only the most informative record pairs for labeling. Most active learning methods proposed so far, start with an empty set of labeled record pairs and iteratively improve the prediction quality of a classification model by asking for new labels. The absence of adequate labeled data in the early active learning iterations leads to unstable models of low quality which is known as the cold start problem. In our work we solve the cold start problem using an unsupervised matching method to bootstrap active learning. We implement a thresholding heuristic that considers pre-calculated similarity scores and assigns matching labels with some degree of noise at no manual labeling cost. The noisy labels are used for initializing the active learning process and throughout the whole active learning cycle for model learning and query selection. We evaluate our pipeline with six datasets from three different entity resolution settings using active learning with a committee-based query strategy and show it successfully deals with the cold start problem. Comparing our method against two active learning baselines without bootstrapping, we show that it can additionally lead to overall improved learned models in terms of  see text] score and stability.

title: Viral and host factors related to the clinical outcome of COVID-19
abstract: In December 2019, coronavirus disease 2019 (COVID-19), which is caused by the new coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in Wuhan (Hubei province, China)1; it soon spread across the world. In this ongoing pandemic, public health concerns and the urgent need for effective therapeutic measures require a deep understanding of the epidemiology, transmissibility and pathogenesis of COVID-19. Here we analysed clinical, molecular and immunological data from 326 patients with confirmed SARS-CoV-2 infection in Shanghai. The genomic sequences of SARS-CoV-2, assembled from 112 high-quality samples together with sequences in the Global Initiative on Sharing All Influenza Data (GISAID) dataset, showed a stable evolution and suggested that there were two major lineages with differential exposure history during the early phase of the outbreak in Wuhan. Nevertheless, they exhibited similar virulence and clinical outcomes. Lymphocytopenia, especially reduced CD4+ and CD8+ T cell counts upon hospital admission, was predictive of disease progression. High levels of interleukin (IL)-6 and IL-8 during treatment were observed in patients with severe or critical disease and correlated with decreased lymphocyte count. The determinants of disease severity seemed to stem mostly from host factors such as age and lymphocytopenia (and its associated cytokine storm), whereas viral genetic variation did not significantly affect outcomes.

title: Tracing open data in emergencies: the case of the COVID-19 pandemic
abstract: The COVID-19 pandemic constitutes an ongoing, burning Public Health Emergency of International Concern (PHEIC). In 2015, the World Health Organization (WHO) adopted an open-data policy recommendation in such situations.  The present cross-sectional meta-research study aimed to assess the availability of open data and metrics and of articles pertaining to the COVID-19 outbreak in five high-impact journals.  All articles regarding the SARS-CoV-2, published in five high impact journals (Ann Intern Med, BMJ, JAMA, NEJM and Lancet) until March 14, 2020 were retrieved. Meta-data (namely the type of article, number of authors, number of patients, citations, errata, news and social media mentions) were extracted for each article in each journal in a systematic way. Google Scholar and Scopus were used for citations and author metrics respectively, and Altmetrics and PlumX were used for news and social media mentions retrieval. The degree of adherence to the PHEIC open data call was also evaluated.  A total of 140 articles were published until March 14, 2020, mostly opinion papers. Sixteen errata followed these publications. The number of authors in each article ranged from 1 to 63, whereas the number of patients with a laboratory-confirmed SARS-CoV-2 infection reached 2,645. The impact of these publications reached a total of 4,210 cumulative crude citations and 342,790 news and social media mentions. Only one publication (0.7%) provided complete open data, while 32 (22.9%) included patient data.  Even though a large number of manuscripts was produced since the pandemic, availability of open data remains restricted.

title: Genomic characterization of a novel SARS-CoV-2
abstract: A new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) associated with human to human transmission and extreme human sickness has been as of late announced from the city of Wuhan in China. Our objectives were to mutation analysis between recently reported genomes at various times and locations and to characterize the genomic structure of SARS-CoV-2 using bioinformatics programs. Information on the variation of viruses is of considerable medical and biological impacts on the prevention, diagnosis, and therapy of infectious diseases. To understand the genomic structure and variations of the SARS-CoV-2. The study analyzed 95 SARS-CoV-2 complete genome sequences available in GenBank, National MicrobiologyData Center (NMDC) and NGDC Genome Warehouse from December-2019 until 05 of April-2020. The genomic signature analysis demonstrates that a strong association between the time of sample collection, location of sample and accumulation of genetic diversity. We found 116 mutations, the three most common mutations were 8782C>T in ORF1ab gene, 28144T>C in ORF8 gene and 29095C>T in the N gene. The mutations might affect the severity and spread of the SARS-CoV-2. The finding heavily supports an intense requirement for additional prompt, inclusive investigations that combine genomic detail, epidemiological information and graph records of the clinical features of patients with COVID-19.

title: What are the main patient safety concerns of healthcare stakeholders: a mixed-method study of Web-based text
abstract: Various healthcare stakeholders define quality of care in different ways. Public policy could advocate all these concerns. This study was conducted to identify the main themes on patient safety of stakeholders expressed before and after the Patient Safety Act was enacted in Korea in 2015.  Longitudinal observational study of the interests of healthcare stakeholders generated between January 2014 and September 2018.  Text data were collected from 2,487 documents on 18 websites that were identified as representative healthcare stakeholder groups of consumers, providers, government, and researchers. A Korean natural language processing (NLP) package, manual review, and synonym dictionary were used for data preprocessing, and we adopted the unsupervised NLP method of probabilistic topic modeling and latent Dirichlet allocation. A linear trend analysis over time, a qualitative step involving two external experts, and original text reviews were performed to validate the identified topics.  Forty-one topics were identified, and the most common concerns of stakeholders were institutional infection control as triggered by the Middle East respiratory syndrome outbreak in early 2015, and infusion-related infection from late 2017 until the middle of 2018. The other top-three concerns of the stakeholder groups were highly similar, while research topics were limited to the perceptions of providers and the activities and culture of hospitals. Five topics showed statistically significant increasing trends over time, while another five showed decreasing trends (both P < 0.05). In the qualitative step, we confirmed 35 themes and revised the other 6.  A common concern among stakeholders was hospital infection control, ranging from nosocomial infections to those brought in by family visiting patients. Government policies and systemic approaches to patient safety were highlighted by different stakeholders. Researchers were focused on hospital sociocultural factors at both the organizational and clinician levels. These identified concerns all should be advocated by the public health policy.

title: [Expression analysis of 2019-nCoV related ACE2 and TMPRSS2 in eye tissues]
abstract: Angiotensin converting enzyme 2 (ACE2) and Transmembrane serine protease 2 (TMPRSS2) are the key proteins for 2019-nCoV entry into host cells. To evaluate the potential infection risk of 2019-nCoV on ocular surface, we compared ACE2 and TMPRSS2 expression among different eye tissues.  Experimental article. Thirty mice were assigned to male, female, aging, diabetic and non-diabetic groups, with 6 mice in each group. Real-time PCR was performed to quantify ACE2 and TMPRSS2 gene expression in conjunctiva, cornea, lacrimal gland, iris, lens, retina, lung, heart, kidney, and liver from male mice. Immunohistochemistry staining was applied to visualize the distribution of the two proteins in different mice tissues, and in human corneal and conjunctival sections. Published transcriptome datasets were extracted to generate the expression comparasion of ACE2 and TMPRSS2 between human conjunctival and corneal tissues, and results were analyzed with Mann-Whitney U test. Female mice, aging mice, STZ-induced diabetic mice, diabetic group control mice were also subjected to ACE2 expression analysis. Results were analyzed using Student's t-test.  The expression of ACE2 and TMPRSS2 genes were the highest in conjunctiva among all the six mice eye tissues explored. The expression of these two genes in conjunctiva were lower than that in kidney and lung. ACE2 and TMPRSS2 shared similar expression pattern with the staining concentrated in corneal epithelium, conjunctival epithelium and lacrimal gland serous cells. The expression levels of ACE2 showed sex-difference. Female mice had lower ACE2 in conjunctiva and cornea than male mice, with the expression levels taking only 43% (t=3.269, P=0.031) and 63% (t=4.080, P=0.015) of that in the male conjunctiva and cornea, respectively. Diabetic mice expressed more ACE2 in conjunctiva (1.21-fold, P>0.05) and lacrimal gland (1.10-fold, P>0.05) compared with the control group. No significant difference on ACE2 expression was found between the aging and young adult mice. The expression level of human conjunctiva ACE2 and TMPRSS2 were significantly higher than that in the cornea (P=0.007), with 5.74-fold and 12.84-fold higher in the conjunctiva than in the corneal epithelium cells, which resembled the situation in mice.  The observation of high-level ACE2 and TMPRSS2 expression in conjunctiva among the 6 eye tissues examined suggests that conjunctiva serves as an infection target tissue of 2019-nCoV. (Chin J Ophthalmol, 2020, 56:).

title: How related is SARS-CoV-2 to other coronaviruses?

title: Strengthening Global Public Health Surveillance through Data and Benefit Sharing
abstract: Equitable sharing of public health surveillance data can help prevent or mitigate the effect of infectious diseases. Equitable data sharing includes working toward more equitable sharing of the public health benefits that data sharing brings and requires the engagement of those providing the data, those interpreting and using the data generated by others, those facilitating the data-sharing process, and those deriving and contributing to the benefit. An expert consultation conducted by Chatham House outlined 7 principles to encourage the process of equitable data sharing: 1) building trust; 2) articulating the value; 3) planning for data sharing; 4) achieving quality data; 5) understanding the legal context; 6) creating data-sharing agreements; and 7) monitoring and evaluation. Sharing of public health surveillance data is best done taking into account these principles, which will help to ensure data are shared optimally and ethically, while fulfilling stakeholder expectations and facilitating equitable distribution of benefits.

title: Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens
abstract: Processing and presentation of major histocompatibility complex class I antigens to cytotoxic T-lymphocytes is crucial for immune surveillance against intracellular bacteria, parasites, viruses and tumors. Identification of antigenic regions on pathogen proteins will play a pivotal role in designer vaccine immunotherapy. We have developed a system that not only identifies high binding T-cell antigenic epitopes, but also class I T-cell antigenic clusters termed immunological hot spots.  MULTIPRED, a computational system for promiscuous prediction of HLA class I binders, uses artificial neural networks (ANN) and hidden Markov models (HMM) as predictive engines. The models were rigorously trained, tested and validated using experimentally identified HLA class I T-cell epitopes from human melanoma related proteins and human papillomavirus proteins E6 and E7. We have developed a scoring scheme for identification of immunological hot spots for HLA class I molecules, which is the sum of the highest four predictions within a window of 30 amino acids.  Our predictions against experimental data from four melanoma-related proteins showed that MULTIPRED ANN and HMM models could predict T-cell epitopes with high accuracy. The analysis of proteins E6 and E7 showed that ANN models appear to be more accurate for prediction of HLA-A3 hot spots and HMM models for HLA-A2 predictions. For illustration of its utility we applied MULTIPRED for prediction of promiscuous T-cell epitopes in all four SARS coronavirus structural proteins. MULTIPRED predicted HLA-A2 and HLA-A3 hot spots in each of these proteins.

title: COVID-19 and the Social Distancing Paradox: dangers and solutions
abstract: Without proven effect treatments and vaccines, Social Distancing is the key protection factor against COVID-19. Social distancing alone should have been enough to protect again the virus, yet things have gone very differently, with a big mismatch between theory and practice. What are the reasons? A big problem is that there is no actual social distancing data, and the corresponding people behavior in a pandemic is unknown. We collect the world-first dataset on social distancing during the COVID-19 outbreak, so to see for the first time how people really implement social distancing, identify dangers of the current situation, and find solutions against this and future pandemics.  Using a sensor-based social distancing belt we collected social distance data from people in Italy for over two months during the most critical COVID-19 outbreak. Additionally, we investigated if and how wearing various Personal Protection Equipment, like masks, influences social distancing.  Without masks, people adopt a counter-intuitively dangerous strategy, a paradox that could explain the relative lack of effectiveness of social distancing. Using masks radically changes the situation, breaking the paradoxical behavior and leading to a safe social distance behavior. In shortage of masks, DIY (Do It Yourself) masks can also be used: even without filtering protection, they provide social distancing protection. Goggles should be recommended for general use, as they give an extra powerful safety boost. Generic Public Health policies and media campaigns do not work well on social distancing: explicit focus on the behavioral problems of necessary mobility are needed.

title: Applications of Google Search Trends for risk communication in infectious disease management: A case study of the COVID-19 outbreak in Taiwan
abstract: An emerging outbreak of a novel coronavirus, COVID-19, has now been detected in at least 211 countries worldwide. Given this pandemic situation, robust risk communication is urgently needed, particularly in affected countries. Therefore, this study explored the potential use of Google Trends (GT) to monitor public restlessness toward COVID-19 infection in Taiwan.  We retrieved GT data for the specific locations and subregions in Taiwan nationwide using defined search terms related to the coronavirus, handwashing, and face masks.  Searches related to COVID-19 and face masks in Taiwan rapidly increased following the announcements of Taiwan's first imported case and reached a peak as locally acquired cases were reported. However, searches for handwashing gradually increased during the period of face-mask shortage. Moreover, high to moderate correlations between Google relative search volumes (RSVs) and COVID-19 cases were found in Taipei (lag-3), New Taipei (lag-2), Taoyuan (lag-2), Tainan (lag-1), Taichung (lag0), and Kaohsiung (lag0).  In response to the ongoing outbreak, our results demonstrated that GT could potentially define the proper timing and location for practicing appropriate risk communication strategies for affected populations.

title: COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology
abstract: The past few weeks have witnessed a worldwide mobilization of the research community in response to the novel coronavirus (COVID-19). This global response has led to a burst of publications on the pathophysiology of the virus, yet without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats. Availability The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de. Contact alpha.tom.kodamullil@scai.fraunhofer.de Supplementary information Supplementary data are available online.

title: Age-dependence of mortality from novel coronavirus disease (COVID-19) in highly exposed populations: New York transit workers and residents and Diamond Princess passengers.
abstract: Populations heavily exposed to the novel coronavirus provide an opportunity to estimate the mortality from COVID-19 in different age groups.  The mortality reported by May 13 from COVID-19 among Diamond Princess cruise ship passengers, and New York residents and Metropolitan Transit Authority (MTA) workers was estimated based on publicly available information.  The mortality among children (age 0 to 17 yrs) in New York City was 1 in 172,692. The mortality in New York state was 1 in 322,217 for ages 10-19 yrs., and 1 in 36,725 for ages 20-29 yrs. The mortality among New York transit workers was estimated to be 1 in 7,329 for ages 30-39 years; 1 in 1,075 for ages 40-49 yrs.; 1 in 343 for ages 50-59 yrs.; and 1 in 178 for ages 60-69 yrs. Among Diamond Princess passengers, the mortality was estimated to be 1 in 145 for ages 70-79, and 1 in 54 for ages 80-89.  Mortality among populations exposed to the novel coronavirus increases with age, ranging from about 1 in 170,000 below the age of 18 years, to 1 in 54 above the age of 80 years.

title: Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England
abstract: The NHS Pathways triage system collates data on enquiries to 111 and 999 services in England. Since the 18th of March 2020, these data have been made publically available for potential COVID-19 symptoms self-reported by members of the public. Trends in such reports over time are likely to reflect behaviour of the ongoing epidemic within the wider community, potentially capturing valuable information across a broader severity profile of cases than hospital admission data. We present a fully reproducible analysis of temporal trends in NHS Pathways reports until 14th May 2020, nationally and regionally, and demonstrate that rates of growth/decline and effective reproduction number estimated from these data may be useful in monitoring transmission. This is a particularly pressing issue as lockdown restrictions begin to be lifted and evidence of disease resurgence must be constantly reassessed. We further assess the correlation between NHS Pathways reports and a publicly available NHS dataset of COVID-19-associated deaths in England, finding that enquiries to 111/999 were strongly associated with daily deaths reported 16 days later. Our results highlight the potential of NHS Pathways as the basis of an early warning system. However, this dataset relies on self-reported symptoms, which are at risk of being severely biased. Further detailed work is therefore necessary to investigate potential behavioural issues which might otherwise explain our conclusions.

title: Computer Vision For COVID-19 Control: A Survey
abstract: The COVID-19 pandemic has triggered an urgent need to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of Artificial Intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at work to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources and an indication of future research directions. We want to make it available to computer vision researchers to save precious time. This survey paper is intended to provide a preliminary review of the available literature on the computer vision efforts against COVID-19 pandemic.

title: Sequencing needs for viral diagnostics.
abstract: We built a system to guide decisions regarding the amount of genomic sequencing required to develop diagnostic DNA signatures, which are short sequences that are sufficient to uniquely identify a viral species. We used our existing DNA diagnostic signature prediction pipeline, which selects regions of a target species genome that are conserved among strains of the target (for reliability, to prevent false negatives) and unique relative to other species (for specificity, to avoid false positives). We performed simulations, based on existing sequence data, to assess the number of genome sequences of a target species and of close phylogenetic relatives (near neighbors) that are required to predict diagnostic signature regions that are conserved among strains of the target species and unique relative to other bacterial and viral species. For DNA viruses such as variola (smallpox), three target genomes provide sufficient guidance for selecting species-wide signatures. Three near-neighbor genomes are critical for species specificity. In contrast, most RNA viruses require four target genomes and no near-neighbor genomes, since lack of conservation among strains is more limiting than uniqueness. Severe acute respiratory syndrome and Ebola Zaire are exceptional, as additional target genomes currently do not improve predictions, but near-neighbor sequences are urgently needed. Our results also indicate that double-stranded DNA viruses are more conserved among strains than are RNA viruses, since in most cases there was at least one conserved signature candidate for the DNA viruses and zero conserved signature candidates for the RNA viruses.

title: Mesenchymal stromal cells for COVID-19: A living systematic review protocol
abstract: To determine the impact of mesenchymal stromal cells outcomes important to patients with COVID-19.  This is the protocol of a living systematic review.  We will conduct searches in PubMed/Medline, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), grey literature and in a centralised repository in L-OVE (Living OVerview of Evidence). L-OVE is a platform that maps PICO questions to evidence from Epistemonikos database. In response to the COVID-19 emergency, L-OVE was adapted to expand the range of evidence it covers and customised to group all COVID-19 evidence in one place. The search will cover the period until the day before submission to a journal.  We adapted an already published common protocol for multiple parallel systematic reviews to the specificities of this question. We will include randomised trials evaluating the effect of mesenchymal stromal cells versus placebo or no treatment in patients with COVID-19. Randomised trials evaluating other coronavirus infections, such as MERS-CoV and SARS-CoV, and non-randomised studies in COVID-19 will be searched in case we find no direct evidence from randomised trials, or if the direct evidence provides low- or very low-certainty for critical outcomes. Two reviewers will independently screen each study for eligibility, extract data, and assess the risk of bias. We will pool the results using meta-analysis and will apply the GRADE system to assess the certainty of the evidence for each outcome. A living, web-based version of this review will be openly available during the COVID-19 pandemic. We will resubmit it every time the conclusions change or whenever there are substantial updates.  No ethics approval is considered necessary. The results of this review will be widely disseminated via peer-reviewed publications, social networks and traditional media.

title: Twitter sentiment classification for measuring public health concerns
abstract: An important task of public health officials is to keep track of health issues, such as spreading epidemics. In this paper, we are addressing the issue of spreading public concern about epidemics. Public concern about a communicable disease can be seen as a problem of its own. Keeping track of trends in concern about public health and identifying peaks of public concern are therefore crucial tasks. However, monitoring public health concerns is not only expensive with traditional surveillance systems, but also suffers from limited coverage and significant delays. To address these problems, we are using Twitter messages, which are available free of cost, are generated world-wide, and are posted in real time. We are measuring public concern using a two-step sentiment classification approach. In the first step, we distinguish Personal tweets from News (i.e., Non-Personal) tweets. In the second step, we further separate Personal Negative from Personal Non-Negative tweets. Both these steps consist themselves of two sub-steps. In the first sub-step (of both steps), our programs automatically generate training data using an emotion-oriented, clue-based method. In the second sub-step, we are training and testing three different Machine Learning (ML) models with the training data from the first sub-step; this allows us to determine the best ML model for different datasets. Furthermore, we are testing the already trained ML models with a human annotated, disjoint dataset. Based on the number of tweets classified as Personal Negative, we compute a Measure of Concern (MOC) and a timeline of the MOC. We attempt to correlate peaks of the MOC timeline to peaks of the News (Non-Personal) timeline. Our best accuracy results are achieved using the two-step method with a Naïve Bayes classifier for the Epidemic domain (six datasets) and the Mental Health domain (three datasets).

title: Serial interval of novel coronavirus (2019-nCoV) infections
abstract: To estimate the serial interval of novel coronavirus (COVID-19) from information on 28 infector-infectee pairs.  We collected dates of illness onset for primary cases (infectors) and secondary cases (infectees) from published research articles and case investigation reports. We subjectively ranked the credibility of the data and performed analyses on both the full dataset (n=28) and a subset of pairs with highest certainty in reporting (n=18). In addition, we adjusting for right truncation of the data as the epidemic is still in its growth phase.  Accounting for right truncation and analyzing all pairs, we estimated the median serial interval at 4.0 days (95% credible interval [CrI]: 3.1, 4.9). Limiting our data to only the most certain pairs, the median serial interval was estimated at 4.6 days (95% CrI: 3.5, 5.9).  The serial interval of COVID-19 is shorter than its median incubation period. This suggests that a substantial proportion of secondary transmission may occur prior to illness onset. The COVID-19 serial interval is also shorter than the serial interval of severe acute respiratory syndrome (SARS), indicating that calculations made using the SARS serial interval may introduce bias.

title: All eyes on Coronavirus—What do we need to know as ophthalmologists

title: Clinical and High-Resolution CT Features of the COVID-19 Infection: Comparison of the Initial and Follow-up Changes
abstract: In late December 2019, an outbreak of coronavirus disease (COVID-19) in Wuhan, China was caused by a novel coronavirus, newly named severe acute respiratory syndrome coronavirus 2. We aimed to quantify the severity of COVID-19 infection on high-resolution chest computed tomography (CT) and to determine its relationship with clinical parameters.  From January 11, 2020, to February 5, 2020, the clinical, laboratory, and high-resolution CT features of 42 patients (26–75 years, 25 males) with COVID-19 were analyzed. The initial and follow-up CT, obtained a mean of 4.5 days and 11.6 days from the illness onset were retrospectively assessed for the severity and progression of pneumonia. Correlations among clinical parameters, initial CT features, and progression of opacifications were evaluated with Spearman correlation and linear regression analysis.  Thirty-five patients (83%) exhibited a progressive process according to CT features during the early stage from onset. Follow-up CT findings showed progressive opacifications, consolidation, interstitial thickening, fibrous strips, and air bronchograms, compared with initial CT (all P < 0.05). Before regular treatments, there was a moderate correlation between the days from onset and sum score of opacifications (R = 0.68, P < 0.01). The C-reactive protein, erythrocyte sedimentation rate, and lactate dehydrogenase showed significantly positive correlation with the severity of pneumonia assessed on initial CT (R(range), 0.36–0.75; P < 0.05). The highest temperature and the severity of opacifications assessed on initial CT were significantly related to the progression of opacifications on follow-up CT (P = 0.001–0.04).  Patients with the COVID-19 infection usually presented with typical ground glass opacities and other CT features, which showed significant correlations with some clinical and laboratory measurements. Follow-up CT images often demonstrated progressions during the early stage from illness onset.

title: Seeding COVID-19 across sub-Saharan Africa: an analysis of reported importation events across 40 countries
abstract: The first case of COVID-19 in sub-Saharan Africa (SSA) was reported by Nigeria on February 27, 2020. While case counts in the entire region remain considerably less than those being reported by individual countries in Europe, Asia, and the Americas, SSA countries remain vulnerable to significant COVID morbidity and mortality due to systemic healthcare weaknesses, less financial resources and infrastructure to address the new crisis, and untreated comorbidities. Variation in preparedness and response capacity as well as in data availability has raised concerns about undetected transmission events.  Confirmed cases reported by SSA countries were line-listed to capture epidemiological details related to early transmission events into and within countries. Data were retrieved from publicly available sources, including institutional websites, situation reports, press releases, and social media accounts, with supplementary details obtained from news articles. A data availability score was calculated for each imported case in terms of how many indicators (sex, age, travel history, date of arrival in country, reporting date of confirmation, and how detected) could be identified. We assessed the relationship between time to first importation and overall Global Health Security Index (GHSI) using Cox regression. K-means clustering grouped countries according to healthcare capacity and health and demographic risk factors.  A total of 2417 confirmed cases of COVID-19 were reported by 40 countries in sub-Saharan Africa during the 30 days after the first known introduction to the region. Out of the 876 cases for which information was publicly available, 677 (77.3%) were considered importation events. At the regional level, imported cases tended to be male (67.3%), were a median 43.0 years old (Range: 6 weeks - 88 years), and most frequently had recent travel history from Europe (43.3%). The median time to reporting an introduction was 19 days; a country's time to report its first importation was not related to GHSI. Mean data availability scores were lowest for countries that had, on average, the highest case fatality rates, lowest healthcare capacity, and highest probability of premature death due to non-communicable diseases.  Countries with systemic, demographic, and pre-existing health vulnerabilities to severe COVID-related morbidity and mortality are less likely to report any cases or are reporting with limited public availability of information. Reporting of information on COVID detection and response efforts, as well as on trends in non-COVID illness and care-seeking behavior, is critical to assessing direct and indirect consequences and capacity needs in resource-constrained settings. Such assessments aid in the ability to make data-driven decisions about interventions, country priorities, and risk assessment.

title: The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
abstract: The present outbreak of a coronavirus-associated acute respiratory disease called coronavirus disease 19 (COVID-19) is the third documented spillover of an animal coronavirus to humans in only two decades that has resulted in a major epidemic. The Coronaviridae Study Group (CSG) of the International Committee on Taxonomy of Viruses, which is responsible for developing the classification of viruses and taxon nomenclature of the family Coronaviridae, has assessed the placement of the human pathogen, tentatively named 2019-nCoV, within the Coronaviridae. Based on phylogeny, taxonomy and established practice, the CSG recognizes this virus as forming a sister clade to the prototype human and bat severe acute respiratory syndrome coronaviruses (SARS-CoVs) of the species Severe acute respiratory syndrome-related coronavirus, and designates it as SARS-CoV-2. In order to facilitate communication, the CSG proposes to use the following naming convention for individual isolates: SARS-CoV-2/host/location/isolate/date. While the full spectrum of clinical manifestations associated with SARS-CoV-2 infections in humans remains to be determined, the independent zoonotic transmission of SARS-CoV and SARS-CoV-2 highlights the need for studying viruses at the species level to complement research focused on individual pathogenic viruses of immediate significance. This will improve our understanding of virus–host interactions in an ever-changing environment and enhance our preparedness for future outbreaks.

title: Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus
abstract: A large number of SARS-related coronaviruses (SARSr-CoV) have been detected in horseshoe bats since 2005 in different areas of China. However, these bat SARSr-CoVs show sequence differences from SARS coronavirus (SARS-CoV) in different genes (S, ORF8, ORF3, etc) and are considered unlikely to represent the direct progenitor of SARS-CoV. Herein, we report the findings of our 5-year surveillance of SARSr-CoVs in a cave inhabited by multiple species of horseshoe bats in Yunnan Province, China. The full-length genomes of 11 newly discovered SARSr-CoV strains, together with our previous findings, reveals that the SARSr-CoVs circulating in this single location are highly diverse in the S gene, ORF3 and ORF8. Importantly, strains with high genetic similarity to SARS-CoV in the hypervariable N-terminal domain (NTD) and receptor-binding domain (RBD) of the S1 gene, the ORF3 and ORF8 region, respectively, were all discovered in this cave. In addition, we report the first discovery of bat SARSr-CoVs highly similar to human SARS-CoV in ORF3b and in the split ORF8a and 8b. Moreover, SARSr-CoV strains from this cave were more closely related to SARS-CoV in the non-structural protein genes ORF1a and 1b compared with those detected elsewhere. Recombination analysis shows evidence of frequent recombination events within the S gene and around the ORF8 between these SARSr-CoVs. We hypothesize that the direct progenitor of SARS-CoV may have originated after sequential recombination events between the precursors of these SARSr-CoVs. Cell entry studies demonstrated that three newly identified SARSr-CoVs with different S protein sequences are all able to use human ACE2 as the receptor, further exhibiting the close relationship between strains in this cave and SARS-CoV. This work provides new insights into the origin and evolution of SARS-CoV and highlights the necessity of preparedness for future emergence of SARS-like diseases.

title: POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
abstract: With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in complementing conventional diagnostic tools from molecular biology, and, using deep learning techniques, several automatic systems were demonstrated promising performances using CT or X-ray data. Here, we advocate a more prominent role of point-of-care ultrasound imaging to guide COVID-19 detection. Ultrasound is non-invasive and ubiquitous in medical facilities around the globe. Our contribution is threefold. First, we gather a lung ultrasound (POCUS) dataset consisting of (currently) 1103 images (654 COVID-19, 277 bacterial pneumonia and 172 healthy controls), sampled from 64 videos. While this dataset was assembled from various online sources and is by no means exhaustive, it was processed specifically to feed deep learning models and is intended to serve as a starting point for an open-access initiative. Second, we train a deep convolutional neural network (POCOVID-Net) on this 3-class dataset and achieve an accuracy of 89% and, by a majority vote, a video accuracy of 92% . For detecting COVID-19 in particular, the model performs with a sensitivity of 0.96, a specificity of 0.79 and F1-score of 0.92 in a 5-fold cross validation. Third, we provide an open-access web service (POCOVIDScreen) that is available at: https://pocovidscreen.org. The website deploys the predictive model, allowing to perform predictions on ultrasound lung images. In addition, it grants medical staff the option to (bulk) upload their own screenings in order to contribute to the growing public database of pathological lung ultrasound images. Dataset and code are available from: https://github.com/jannisborn/covid19_pocus_ultrasound

title: Epidemiological and Clinical Aspects of COVID-19; a Narrative Review
abstract: There are significant misconceptions and many obstacles in the way of illuminating the epidemiological and clinical aspects of COVID-19 as a new emerging epidemic. In addition, usefulness of some evidence published in the context of the recent epidemic for decision making in clinic as well as public health is questionable. However, misinterpreting or ignoring strong evidence in clinical practice and public health probably results in less effective and somehow more harmful decisions for individuals as well as subgroups in general populations of countries in the initial stages of this epidemic. Accordingly, our narrative review appraised epidemiological and clinical aspects of the disease including genetic diversity of coronavirus genus, mode of transmission, incubation period, infectivity, pathogenicity, virulence, immunogenicity, diagnosis, surveillance, clinical case management and also successful measures for preventing its spread in some communities.

title: Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set
abstract: At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter.  In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository.  We started this ongoing data collection on January 28, 2020, leveraging Twitter's streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter's search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020.  Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events.  It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic-and undoubtedly more.

title: COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs
abstract: In the last few months, the novel COVID19 pandemic has spread all over the world. Due to its easy transmission, developing techniques to accurately and easily identify the presence of COVID19 and distinguish it from other forms of flu and pneumonia is crucial. Recent research has shown that the chest Xrays of patients suffering from COVID19 depicts certain abnormalities in the radiography. However, those approaches are closed source and not made available to the research community for re-producibility and gaining deeper insight. The goal of this work is to build open source and open access datasets and present an accurate Convolutional Neural Network framework for differentiating COVID19 cases from other pneumonia cases. Our work utilizes state of the art training techniques including progressive resizing, cyclical learning rate finding and discriminative learning rates to training fast and accurate residual neural networks. Using these techniques, we showed the state of the art results on the open-access COVID-19 dataset. This work presents a 3-step technique to fine-tune a pre-trained ResNet-50 architecture to improve model performance and reduce training time. We call it COVIDResNet. This is achieved through progressively re-sizing of input images to 128x128x3, 224x224x3, and 229x229x3 pixels and fine-tuning the network at each stage. This approach along with the automatic learning rate selection enabled us to achieve the state of the art accuracy of 96.23% (on all the classes) on the COVIDx dataset with only 41 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of three different infection types from along with Normal individuals. This model can help in the early screening of COVID19 cases and help reduce the burden on healthcare systems.

title: Enhanced COVID-19 data for improved prediction of survival
abstract: The current COVID-19 pandemic, caused by the rapid world-wide spread of the SARS-CoV-2 virus, is having severe consequences for human health and the world economy. The virus effects individuals quite differently, with many infected patients showing only mild symptoms, and others showing critical illness. To lessen the impact of the pandemic, one important question is which factors predict the death of a patient? Here, we construct an enhanced COVID-19 dataset by processing two existing databases (from Kaggle and WHO) and using natural language processing methods to enhance the data by adding local weather conditions and research sentiment.  In this study, we contribute an enhanced COVID-19 dataset, which contains 183 samples and 43 features. Application of Extreme Gradient Boosting (XGBoost) on the enhanced dataset achieves 95% accuracy in predicting patients survival, with country-wise research sentiment, and then age and local weather, showing the most importance. All data and source code are available at http://ab.inf.uni-tuebingen.de/publications/papers/COVID-19.

title: SARS-CoV-2 receptor and entry genes are expressed by sustentacular cells in the human olfactory neuroepithelium
abstract: Various reports indicate an association between COVID-19 and anosmia, suggesting an infection of the olfactory sensory epithelium, and thus a possible direct virus access to the brain. To test this hypothesis, we generated RNA-seq libraries from human olfactory neuroepithelia, in which we found substantial expression of the genes coding for the virus receptor angiotensin-converting enzyme-2 (ACE2), and for the virus internalization enhancer TMPRSS2. We analyzed a human olfactory single-cell RNA-seq dataset and determined that sustentacular cells, which maintain the integrity of olfactory sensory neurons, express ACE2 and TMPRSS2. We then observed that the ACE2 protein was highly expressed in a subset of sustentacular cells in human and mouse olfactory tissues. Finally, we found ACE2 transcripts in specific brain cell types, both in mice and humans. Sustentacular cells thus represent a potential entry door for SARS-CoV-2 in a neuronal sensory system that is in direct connection with the brain.

title: Responding to the COVID-19 pandemic in complex humanitarian crises

title: Domain-Independent Extraction of Scientific Concepts from Research Articles
abstract: We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. First, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process. This set of concepts is utilised to annotate a corpus of scientific abstracts from 10 domains of Science, Technology and Medicine at the phrasal level in a joint effort with domain experts. The resulting dataset is used in a set of benchmark experiments to (a) provide baseline performance for this task, (b) examine the transferability of concepts between domains. Second, we present a state-of-the-art deep learning baseline. Further, we propose the active learning strategy for an optimal selection of instances from among the various domains in our data. The experimental results show that (1) a substantial agreement is achievable by non-experts after consultation with domain experts, (2) the baseline system achieves a fairly high F1 score, (3) active learning enables us to nearly halve the amount of required training data.

title: Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection&Patient Monitoring using Deep Learning CT Image Analysis
abstract: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients.  Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score. The study includes a testing set of 157 international patients (China and U.S).  Classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies were 0.996 AUC (95%CI: 0.989-1.00) ; on datasets of Chinese control and infected patients. Possible working point: 98.2% sensitivity, 92.2% specificity. For time analysis of Coronavirus patients, the system output enables quantitative measurements for smaller opacities (volume, diameter) and visualization of the larger opacities in a slice-based heat map or a 3D volume display. Our suggested Corona score measures the progression of disease over time.  This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.

title: COVID Faster R-CNN: A Novel Framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray Images
abstract: COVID-19 or novel coronavirus disease, which has already been declared as a worldwide pandemic, at first had an outbreak in a small town of China, named Wuhan. More than two hundred countries around the world have already been affected by this severe virus as it spreads by human interaction. Moreover, the symptoms of novel coronavirus are quite similar to the general flu. Screening of infected patients is considered as a critical step in the fight against COVID-19. Therefore, it is highly relevant to recognize positive cases as early as possible to avoid further spreading of this epidemic. However, there are several methods to detect COVID-19 positive patients, which are typically performed based on respiratory samples and among them one of the critical approach which is treated as radiology imaging or X-Ray imaging. Recent findings from X-Ray imaging techniques suggest that such images contain relevant information about the SARS-CoV-2 virus. In this article, we have introduced a Deep Neural Network (DNN) based Faster Regions with Convolutional Neural Networks (Faster R-CNN) framework to detect COVID-19 patients from chest X-Ray images using available open-source dataset. Our proposed approach provides a classification accuracy of 97.36%, 97.65% of sensitivity, and a precision of 99.28%. Therefore, we believe this proposed method might be of assistance for health professionals to validate their initial assessment towards COVID-19 patients.

title: Chronic renal failure in Sri Lanka caused by elevated dietary cadmium: Trojan horse of the green revolution
abstract: The endemic of chronic renal failure (CRF) emerged in 2002 in the farming provinces of Sri Lanka. An estimate of dietary cadmium intake was between 15 and 28μg/kg body weight per week. The mean urinary cadmium in patients diagnosed with stage 5 kidney failure was 7.6μg/g creatinine and 11.6μg/g for asymptomatic persons. The agrochemical triple superphosphate (TSP) fertilizer containing 23.5–71.7mgCd/kg was the source of cadmium added to soils. Mean Cd content in cultivated vs. uncultivated soils in Anuradhapura district was 0.02±0.01 vs. 0.11±0.19mg/kg while in Polonnaruwa district, it was 0.005±0.004 vs. 0.016±0.005mg/kg. Prior to the Green Revolution, the amount of fertilizer used in rice cultivation in 1970 was 32,000 metric tons (Mts) rising to 74,000Mts in 1975. Up to 68.9Mts of Cd could have entered into the rice-cascade reservoir environment from TSP use since 1973. Diversion of the Mahaweli River in 1970–1980 further increased cadmium input. Cadmium transfer from Upper Mahaweli water to Polgolla was 72.13kg/day. Cadmium content of the sediments from reservoirs collecting cadmium from irrigated TSP fertilized crop fields (rice and vegetables) was 1.8–2.4mg/kg.

title: COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes
abstract: This resource paper describes a large dataset covering over 63 million coronavirus-related Twitter posts from more than 13 million unique users since 28 January to 1 July 2020. As strong concerns and emotions are expressed in the tweets, we analyzed the tweets content using natural language processing techniques and machine-learning based algorithms, and inferred seventeen latent semantic attributes associated with each tweet, including 1) ten attributes indicating the tweet's relevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness) and emotional intensities across four primary emotions of fear, anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively. To illustrate how the dataset can be used, we present descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss possible applications in communication, psychology, public health, economics and epidemiology.

title: Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique
abstract: Coronavirus (COVID-19) emerged towards the end of 2019. World Health Organization (WHO) was identified it as a global epidemic. Consensus occurred in the opinion that using Computerized Tomography (CT) techniques for early diagnosis of pandemic disease gives both fast and accurate results. It was stated by expert radiologists that COVID-19 displays different behaviours in CT images. In this study, a novel method was proposed as fusing and ranking deep features to detect COVID-19 in early phase. 16x16 (Subset-1) and 32x32 (Subset-2) patches were obtained from 150 CT images to generate sub-datasets. Within the scope of the proposed method, 3000 patch images have been labelled as CoVID-19 and No finding for using in training and testing phase. Feature fusion and ranking method have been applied in order to increase the performance of the proposed method. Then, the processed data was classified with a Support Vector Machine (SVM). According to other pre-trained Convolutional Neural Network (CNN) models used in transfer learning, the proposed method shows high performance on Subset-2 with 98.27% accuracy, 98.93% sensitivity, 97.60% specificity, 97.63% precision, 98.28% F1-score and 96.54% Matthews Correlation Coefficient (MCC) metrics.

title: COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
abstract: Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.

title: Medical imaging informatics research and development trends—an editorial

title: Increasing the Signal-to-Noise Ratio: COVID-19 Clinical Synopsis for Outpatient Providers
abstract: The novel coronavirus (SARS-CoV-2), which is the cause of coronavirus disease (COVID-19 formally 2019-nCoV), has received widespread attention from the medical community. Despite the rapid publication of research on the virus and the disease it causes, there is a lack of concise and relevant material to help busy medical providers navigate recognition and management of the disease in the ambulatory setting. This review article aims to bridge this gap by briefly reviewing the key points of the evaluation and treatment of patients with COVID-19 in the ambulatory clinic environment.

title: Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection
abstract: It has been known that, the novel Coronavirus, 2019-nCoV, which is considered similar to SARS-CoV and originated from Wuhan (China), invades human cells via the receptor angiotensin converting enzyme II (ACE2). Moreover, lung cells that have ACE2 expression may be the main target cells during 2019-nCoV infection. However, some patients also exhibit non-respiratory symptoms, such as kidney failure, implying that 2019-nCoV could also invade other organs. To construct a risk map of different human organs, we analyzed the single-cell RNA sequencing (scRNA-seq) datasets derived from major human physiological systems, including the respiratory, cardiovascular, digestive, and urinary systems. Through scRNA-seq data analyses, we identified the organs at risk, such as lung, heart, esophagus, kidney, bladder, and ileum, and located specific cell types (i.e., type II alveolar cells (AT2), myocardial cells, proximal tubule cells of the kidney, ileum and esophagus epithelial cells, and bladder urothelial cells), which are vulnerable to 2019-nCoV infection. Based on the findings, we constructed a risk map indicating the vulnerability of different organs to 2019-nCoV infection. This study may provide potential clues for further investigation of the pathogenesis and route of 2019-nCoV infection.

title: Exploring a Proposed WHO Method to Determine Thresholds for Seasonal Influenza Surveillance
abstract: Health authorities find thresholds useful to gauge the start and severity of influenza seasons. We explored a method for deriving thresholds proposed in an influenza surveillance manual published by the World Health Organization (WHO).  For 2002-2011, we analysed two routine influenza-like-illness (ILI) datasets, general practice sentinel surveillance and a locum medical service sentinel surveillance, plus laboratory data and hospital admissions for influenza. For each sentinel dataset, we created two composite variables from the product of weekly ILI data and the relevant laboratory data, indicating the proportion of tested specimens that were positive. For all datasets, including the composite datasets, we aligned data on the median week of peak influenza or ILI activity and assigned three threshold levels: seasonal threshold, determined by inspection; and two intensity thresholds termed average and alert thresholds, determined by calculations of means, medians, confidence intervals (CI) and percentiles. From the thresholds, we compared the seasonal onset, end and intensity across all datasets from 2002-2011. Correlation between datasets was assessed using the mean correlation coefficient.  The median week of peak activity was week 34 for all datasets, except hospital data (week 35). Means and medians were comparable and the 90% upper CIs were similar to the 95(th) percentiles. Comparison of thresholds revealed variations in defining the start of a season but good agreement in describing the end and intensity of influenza seasons, except in hospital admissions data after the pandemic year of 2009. The composite variables improved the agreements between the ILI and other datasets. Datasets were well correlated, with mean correlation coefficients of >0.75 for a range of combinations.  Thresholds for influenza surveillance are easily derived from historical surveillance and laboratory data using the approach proposed by WHO. Use of composite variables is helpful for describing influenza season characteristics.

title: Towards Contactless Patient Positioning.
abstract: The ongoing COVID-19 pandemic, caused by the highly contagious SARS-CoV-2 virus, has overwhelmed healthcare systems worldwide, putting medical professionals at a high risk of getting infected themselves due to a global shortage of personal protective equipment. This has in-turn led to understaffed hospitals unable to handle new patient influx. To help alleviate these problems, we design and develop a contactless patient positioning system that can enable scanning patients in a completely remote and contactless fashion. Our key design objective is to reduce the physical contact time with a patient as much as possible, which we achieve with our contactless workflow. Our system comprises automated calibration, positioning, and multi-view synthesis components that enable patient scan without physical proximity. Our calibration routine ensures system calibration at all times and can be executed without any manual intervention. Our patient positioning routine comprises a novel robust dynamic fusion (RDF) algorithm for accurate 3D patient body modeling. With its multi-modal inference capability, RDF can be trained once and used across different applications (without re-training) having various sensor choices, a key feature to enable system deployment at scale. Our multi-view synthesizer ensures multi-view positioning visualization for the technician to verify positioning accuracy prior to initiating the patient scan. We conduct extensive experiments with publicly available and proprietary datasets to demonstrate efficacy. Our system has already been used, and had a positive impact on, hospitals and technicians on the front lines of the COVID-19 pandemic, and we expect to see its use increase substantially globally.

title: The IMEx Coronavirus interactome: an evolving map of Coronaviridae-Host molecular interactions
abstract: The current Coronavirus Disease 2019 (COVID-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions enables studying fine-grained resolution of the mechanisms behind the virus biology and the human organism response. Here we present a curated dataset of physical molecular interactions, manually extracted by IMEx Consortium curators focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family. Currently, the dataset comprises over 2,200 binarized interactions extracted from 86 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website (www.ebi.ac.uk/intact), and will be continuously updated as research on COVID-19 progresses.

title: Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review
abstract: The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging, Autonomous Systems and other real-world applications is the requirement of feeding a large annotated and labelled datasets, prepared by an expert human to train the network model. ZSL is known for having minimal human intervention by mainly relying only on previously known concepts and current auxiliary information. This is an ever-growing research for the cases where we have very limited or no datasets available and at the same time, the detection/recognition system has human-like characteristics in learning new concepts. Therefore, it makes it applicable in real-world scenarios, from developing autonomous vehicles to medical imaging and COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we present the definition of the problem, we review over fundamentals, and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions as well as our recommended solution, motivations behind each approach, and their advantages over each category to guide the researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we introduce a novel and broaden solution called one/few-shot learning. We then review through different image datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex computer vision learning tasks more similar to the way humans learn.

title: Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection
abstract: To review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population.  Rapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection. Data sources PubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24th March 2020. Study selection Studies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text.  Data from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST. Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance.  2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China. Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in infected patients included age, sex, features derived from CT, C-reactive protein, lactic dehydrogenase, and lymphocyte count. Estimated C-index estimates for the prediction models ranged from 0.73 to 0.81 in those for the general population (reported for all 3 general population models), from 0.81 to > 0.99 in those for diagnosis (reported for 13 of the 18 diagnostic models), and from 0.85 to 0.98 in those for prognosis (reported for 6 of the 10 prognostic models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and poor statistical analysis, including high risk of model overfitting. Reporting quality varied substantially between studies. A description of the study population and intended use of the models was absent in almost all reports, and calibration of predictions was rarely assessed. Conclusion COVID-19 related prediction models are quickly entering the academic literature, to support medical decision making at a time where this is urgently needed. Our review indicates proposed models are poorly reported and at high risk of bias. Thus, their reported performance is likely optimistic and using them to support medical decision making is not advised. We call for immediate sharing of the individual participant data from COVID-19 studies to support collaborative efforts in building more rigorously developed prediction models and validating (evaluating) existing models. The aforementioned predictors identified in multiple included studies could be considered as candidate predictors for new models. We also stress the need to follow methodological guidance when developing and validating prediction models, as unreliable predictions may cause more harm than benefit when used to guide clinical decisions. Finally, studies should adhere to the TRIPOD statement to facilitate validating, appraising, advocating and clinically using the reported models.

title: The global spread of 2019-nCoV: a molecular evolutionary analysis
abstract: The global spread of the 2019-nCoV is continuing and is fast moving, as indicated by the WHO raising the risk assessment to high. In this article, we provide a preliminary phylodynamic and phylogeographic analysis of this new virus. A Maximum Clade Credibility tree has been built using the 29 available whole genome sequences of 2019-nCoV and two whole genome sequences that are highly similar sequences from Bat SARS-like Coronavirus available in GeneBank. We are able to clarify the mechanism of transmission among the countries which have provided the 2019-nCoV sequence isolates from their patients. The Bayesian phylogeographic reconstruction shows that the 2019–2020 nCoV most probably originated from the Bat SARS-like Coronavirus circulating in the Rhinolophus bat family. In agreement with epidemiological observations, the most likely geographic origin of the new outbreak was the city of Wuhan, China, where 2019-nCoV time of the most recent common ancestor emerged, according to molecular clock analysis, around November 25(th), 2019. These results, together with previously recorded epidemics, suggest a recurring pattern of periodical epizootic outbreaks due to Betacoronavirus. Moreover, our study describes the same population genetic dynamic underlying the SARS 2003 epidemic, and suggests the urgent need for the development of effective molecular surveillance strategies of Betacoronavirus among animals and Rhinolophus of the bat family.

title: Rapid implementation of mobile technology for real-time epidemiology of COVID-19
abstract: The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic (COVID-19) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (COPE) consortium to bring together scientists with expertise in big data research and epidemiology to develop a COVID-19 Symptom Tracker mobile application that we launched in the UK on March 24, 2020 and the US on March 29, 2020 garnering more than 2.8 million users as of May 2, 2020. This mobile application offers data on risk factors, herald symptoms, clinical outcomes, and geographical hot spots. This initiative offers critical proof-of-concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis which is critical for a data-driven response to this public health challenge.

title: Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19
abstract: Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, training datasets, deep learning architectures and analysis strategies have been experimented from publicly open sets of chest X-ray images. Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases. It is assumed that viral pneumonia cases detected during an epidemic COVID-19 context have a high probability to presume COVID-19 infections. Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia. Chest X-ray test images of COVID-19 infected patients are successfully diagnosed through detection models retained for their performances. The efficiency of proposed health indicators is highlighted through simulated scenarios of patients presenting infections and health problems by combining real and synthetic health data.

title: Phylodynamics of SARS-CoV-2 transmission in Spain
abstract: SARS-CoV-2 whole-genome analysis has identified three large clades spreading worldwide, designated G, V and S. This study aims to analyze the diffusion of SARS-CoV-2 in Spain/Europe.  Maximum likelihood phylogenetic and Bayesian phylodynamic analyses have been performed to estimate the most probable temporal and geographic origin of different phylogenetic clusters and the diffusion pathways of SARS-CoV-2.  Phylogenetic analyses of the first 28 SARS-CoV-2 whole genome sequences obtained from patients in Spain revealed that most of them are distributed in G and S clades (13 sequences in each) with the remaining two sequences branching in the V clade. Eleven of the Spanish viruses of the S clade and six of the G clade grouped in two different monophyletic clusters (S-Spain and G-Spain, respectively), with the S-Spain cluster also comprising 8 sequences from 6 other countries from Europe and the Americas. The most recent common ancestor (MRCA) of the SARS-CoV-2 pandemic was estimated in the city of Wuhan, China, around November 24, 2019, with a 95% highest posterior density (HPD) interval from October 30-December 17, 2019. The origin of S-Spain and G-Spain clusters were estimated in Spain around February 14 and 18, 2020, respectively, with a possible ancestry of S-Spain in Shanghai.  Multiple SARS-CoV-2 introductions have been detected in Spain and at least two resulted in the emergence of locally transmitted clusters, with further dissemination of one of them to at least 6 other countries. These results highlight the extraordinary potential of SARS-CoV-2 for rapid and widespread geographic dissemination.

title: Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China
abstract: The purpose of the present study is to explore the associations between novel coronavirus disease 2019 (COVID-19) case counts and meteorological factors in 30 provincial capital cities of China. We compiled a daily dataset including confirmed case counts, ambient temperature (AT), diurnal temperature range (DTR), absolute humidity (AH) and migration scale index (MSI) for each city during the period of January 20th to March 2nd, 2020. First, we explored the associations between COVID-19 confirmed case counts, meteorological factors, and MSI using non-linear regression. Then, we conducted a two-stage analysis for 17 cities with more than 50 confirmed cases. In the first stage, generalized linear models with negative binomial distribution were fitted to estimate city-specific effects of meteorological factors on confirmed case counts. In the second stage, the meta-analysis was conducted to estimate the pooled effects. Our results showed that among 13 cities that have less than 50 confirmed cases, 9 cities locate in the Northern China with average AT below 0 °C, 12 cities had average AH below 4 g/m3, and one city (Haikou) had the highest AH (14.05 g/m3). Those 17 cities with 50 and more cases accounted for 90.6% of all cases in our study. Each 1 °C increase in AT and DTR was related to the decline of daily confirmed case counts, and the corresponding pooled RRs were 0.80 (95% CI: 0.75, 0.85) and 0.90 (95% CI: 0.86, 0.95), respectively. For AH, the association with COVID-19 case counts were statistically significant in lag 07 and lag 014. In addition, we found the all these associations increased with accumulated time duration up to 14 days. In conclusions, meteorological factors play an independent role in the COVID-19 transmission after controlling population migration. Local weather condition with low temperature, mild diurnal temperature range and low humidity likely favor the transmission.

title: Role of Intelligent Computing in COVID-19 Prognosis: A State-of-the-Art Review
abstract: The World Health Organization (WHO) declared novel coronavirus 2019 (COVID-19), an infectious epidemic caused by SARS-CoV-2, as Pandemic in March 2020. It has affected more than 40 million people in 216 countries. Almost in all the affected countries, the number of infected and deceased patients has been enhancing at a distressing rate. As the early prediction can reduce the spread of the virus, it is highly desirable to have intelligent prediction and diagnosis tools. The inculcation of efficient forecasting and prediction models may assist the government in implementing better design strategies to prevent the spread of virus. In this paper, a state-of-the-art analysis of the ongoing machine learning (ML) and deep learning (DL) methods in the diagnosis and prediction of COVID-19 has been done. Moreover, a comparative analysis on the impact of machine learning and other competitive approaches like mathematical and statistical models on COVID-19 problem has been conducted. In this study, some factors such as type of methods(machine learning, deep learning, statistical & mathematical) and the impact of COVID research of the nature of data used for the forecasting and prediction of pandemic using computing approaches has been presented. Finally some important research directions for further research on COVID-19 are highlighted which may facilitate the researchers and technocrats to develop competent intelligent models for the prediction and forecasting of COVID-19 real time data.

title: PathoLive – Real-time pathogen identification from metagenomic Illumina datasets
abstract: Over the past years, NGS has become a crucial workhorse for open-view pathogen diagnostics. Yet, long turnaround times result from using massively parallel high-throughput technologies as the analysis can only be performed after sequencing has finished. The interpretation of results can further be challenged by contaminations, clinically irrelevant sequences, and the sheer amount and complexity of the data.  We implemented PathoLive, a real-time diagnostics pipeline for the detection of pathogens from clinical samples hours before sequencing has finished. Based on real-time alignment with HiL-ive2, mappings are scored with respect to common contaminations, low-entropy areas, and sequences of widespread, non-pathogenic organisms. The results are visualized using an interactive taxonomic tree that provides an easily interpretable overview of the relevance of hits. For a human plasma sample that was spiked in vitro with six pathogenic viruses, all agents were clearly detected after only 40 of 200 sequencing cycles. For a real-world sample from Sudan the results correctly indicated the presence of Crimean-Congo hemorrhagic Fever Virus. In a second real-world dataset from the 2019 SARS-CoV-2 outbreak in Wuhan, we found the presence of a SARS Coronavirus as the most relevant hit without the novel virus reference genome being included in the database. For all samples, clinically irrelevant hits were correctly de-emphasized. Our approach is valuable to obtain fast and accurate NGS-based pathogen identifications and correctly prioritize and visualize them based on their clinical significance. Availability PathoLive is open source and available on GitLab (https://gitlab.com/rkibioinformatics/PathoLive) and BioConda (conda install –c bioconda patholive). Contact Bernhard.Renard@hpi.de, NitscheA@rki.de

title: Online Databases and Non-coding RNAs in Cardiovascular Diseases
abstract: Cardiovascular disease is characterized by its highest morbidity and mortality. One of the main pathological basis of this disease is the dysregulation of gene expression. Non-coding RNA (ncRNA) is a kind of functional RNA, which is transcript from DNA but not translated into proteins. More and more studies have established the important roles of ncRNAs, including transcription, RNA maturation, translation, protein degradation, and their involvement in the pathogenesis of diseases such as cancer and cardiovascular diseases. This chapter will focus on the biological functions of ncRNAs and their advances in cardiovascular disease. With the development of sequencing and computer technology, more and more databases can be easily obtained on the internet. In another part of this chapter, we will summarize some commonly used non-coding RNA databases, which can be easily and quickly used for relevant research.

title: Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
abstract: The need to streamline patient management for COVID-19 has become more pressing than ever. Chest X-rays provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU.  Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task.  This study finds that training a regression model on a subset of the outputs from an this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE.  These results indicate that our model's ability to gauge severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the intensive care unit (ICU). A proper clinical trial is needed to evaluate efficacy. To enable this we make our code, labels, and data available online at https://github.com/mlmed/torchxrayvision/tree/master/scripts/covid-severity and https://github.com/ieee8023/covid-chestxray-dataset

title: Genetic inactivation of COPI coatomer separately inhibits vesicular stomatitis virus entry and gene expression.
abstract: Viruses coopt cellular membrane transport to invade cells, establish intracellular sites of replication, and release progeny virions. Recent genome-wide RNA interference (RNAi) screens revealed that genetically divergent viruses require biosynthetic membrane transport by the COPI coatomer complex for efficient replication. Here we found that disrupting COPI function by RNAi inhibited an early stage of vesicular stomatitis virus (VSV) replication. To dissect which replication stage(s) was affected by coatomer inactivation, we used visual and biochemical assays to independently measure the efficiency of viral entry and gene expression in hamster (ldlF) cells depleted of the temperature-sensitive ε-COP subunit. We show that ε-COP depletion for 12 h caused a primary block to virus internalization and a secondary defect in viral gene expression. Using brefeldin A (BFA), a chemical inhibitor of COPI function, we demonstrate that short-term (1-h) BFA treatments inhibit VSV gene expression, while only long-term (12-h) treatments block virus entry. We conclude that prolonged coatomer inactivation perturbs cellular endocytic transport and thereby indirectly impairs VSV entry. Our results offer an explanation of why COPI coatomer is frequently identified in screens for cellular factors that support cell invasion by microbial pathogens.

title: Using the Kalman filter with ARIMA for the COVID-19 pandemic dataset of Pakistan
abstract: The current pandemic of the Novel Corona virus (COVID-19) has resulted in multifold challenges related to health, economy, and society, etc. for the entire world. Many mathematical epidemiological models have been tried for the available data of the COVID-19 pandemic with the core objective to observe the trend and trajectories of infected cases, recoveries, and deaths, etc. However, these models have their own assumptions and parameters and vary with regional demography. This article suggests the use of a more pragmatic approach of the Kalman filter with the Autoregressive Integrated Moving Average (ARIMA) models in order to obtain more precise forecasts for the figures of prevalence, active cases, recoveries, and deaths related to the COVID-19 outbreak in Pakistan.

title: Modulation of TNF-alpha-converting enzyme by the spike protein of SARS-CoV and ACE2 induces TNF-alpha production and facilitates viral entry.
abstract: Severe acute respiratory syndrome coronavirus (SARS-CoV) is a high-risk infectious pathogen. In the proposed model of respiratory failure, SARS-CoV down-regulates its receptor, angiotensin-converting enzyme 2 (ACE2), but the mechanism involved is unknown. We found that the spike protein of SARS-CoV (SARS-S) induced TNF-alpha-converting enzyme (TACE)-dependent shedding of the ACE2 ectodomain. The modulation of TACE activity by SARS-S depended on the cytoplasmic domain of ACE2, because deletion mutants of ACE2 lacking the carboxyl-terminal region did not induce ACE2 shedding or TNF-alpha production. In contrast, the spike protein of HNL63-CoV (NL63-S), a CoV that uses ACE2 as a receptor and mainly induces the common cold, caused neither of these cellular responses. Intriguingly, viral infection, judged by real-time RT-PCR analysis of SARS-CoV mRNA expression, was significantly attenuated by deletion of the cytoplasmic tail of ACE2 or knock-down of TACE expression by siRNA. These data suggest that cellular signals triggered by the interaction of SARS-CoV with ACE2 are positively involved in viral entry but lead to tissue damage. These findings may lead to the development of anti-SARS-CoV agents.

title: Fact or Fiction: Verifying Scientific Claims
abstract: We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that supports or refutes a given scientific claim, and to identify rationales justifying each decision. To study this task, we construct SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. We develop baseline models for SciFact, and demonstrate that these models benefit from combined training on a large dataset of claims about Wikipedia articles, together with the new SciFact data. We show that our claim verification system is able to identify plausible evidence for 23 / 36 claims relevant to COVID-19 on the CORD-19 corpus. Our results and experiments strongly suggest that our new task and data will support significant future research efforts.

title: Cerebrovascular Accident in a Pediatric Patient Presenting With Influenza
abstract: Acute ischemic stroke (AIS) in pediatric populations accounts for more than half of pediatric strokes and is associated with significant morbidity and mortality. Pediatric AIS can present with nonspecific symptoms or symptoms that mimic alternate pathology.  A 4-month-old female presented to the emergency department for fever, decreased oral intake, and “limp” appearance after antibiotic administration. She was febrile, tachypneic, and hypoxic. Her skin was mottled with 3-s capillary refill, her anterior fontanelle was tense, and she had mute Babinski reflex bilaterally but was moving all extremities. The patient was hyponatremic, thrombocytopenic, and tested positive for influenza A. A computed tomography scan of the brain revealed an acute infarction involving the right frontal, parietal, temporal, and occipital lobes in addition to hyperdensities concerning for thrombosed cortical veins. The patient was transferred for specialty evaluation and was discharged 2 weeks later on levetiracetam. Why Should an Emergency Physician be Aware of This? Pediatric AIS can present with nonspecific symptoms that mimic alternate pathology. A high level of suspicion is needed so as not to miss the diagnosis of pediatric AIS in the emergency department. A thorough neurologic assessment is warranted, and subtle abnormalities should be investigated further.

title: The First French COVID19 Confinement Twitter Dataset
abstract: In this paper, we present a mainly French coronavirus Twitter dataset that we have been continuously collecting since confinement restrictions have been enacted in France (in March 17, 2020). We offer our datasets and sentiment analysis annotations, that have been obtained using high performance computing (HPC) capabilities of our university's datacenter, to the research community at https://github.com/calciu/C0VID19-ContainmentFr. We think that our contribution can facilitate analysis of online conversation dynamics reflecting people sentiments when facing severe home confinement restrictions determined by the outbreak of this world wide epidemic. We hope that our contribution will help decode shared experience and mood but also test the sensitivity of sentiment measurement instruments and incite the development of new instruments, methods and approaches.

title: Challenges in Combating COVID-19 Infodemic -- Data, Tools, and Ethics
abstract: While the COVID-19 pandemic continues its global devastation, numerous accompanying challenges emerge. One important challenge we face is to efficiently and effectively use recently gathered data and find computational tools to combat the COVID-19 infodemic, a typical information overloading problem. Novel coronavirus presents many questions without ready answers; its uncertainty and our eagerness in search of solutions offer a fertile environment for infodemic. It is thus necessary to combat the infodemic and make a concerted effort to confront COVID-19 and mitigate its negative impact in all walks of life when saving lives and maintaining normal orders during trying times. In this position paper of combating the COVID-19 infodemic, we illustrate its need by providing real-world examples of rampant conspiracy theories, misinformation, and various types of scams that take advantage of human kindness, fear, and ignorance. We present three key challenges in this fight against the COVID-19 infodemic where researchers and practitioners instinctively want to contribute and help. We demonstrate that these three challenges can and will be effectively addressed by collective wisdom, crowdsourcing, and collaborative research.

title: Progression of Mental Health Services during the COVID-19 Outbreak in China
abstract: The novel coronavirus disease (COVID-19) has been rapidly transmitted in China, Macau, Hong Kong, and other Asian and European counterparts. This COVID-19 epidemic has aroused increasing attention nationwide. Patients, health professionals, and the general public are under insurmountable psychological pressure which may lead to various psychological problems, such as anxiety, fear, depression, and insomnia. Psychological crisis intervention plays a pivotal role in the overall deployment of the disease control. The National Health Commission of China has summoned a call for emergency psychological crisis intervention and thus, various mental health associations and organizations have established expert teams to compile guidelines and public health educational articles/videos for mental health professionals and the general public alongside with online mental health services. In addition, mental health professionals and expert groups are stationed in designated isolation hospitals to provide on-site services. Experts have reached a consensus on the admission of patients with severe mental illness during the COVID-19 outbreak in mental health institutions. Nevertheless, the rapid transmission of the COVID-19 has emerged to mount a serious challenge to the mental health service in China.

title: Prevention and Control Strategies for SARS-CoV-2 Infection
abstract: The population of 168 countries all over the world is struggling with the outbreak of COVID-19. The outbreak is declared as pandemic and public health emergency of international concern declared by WHO. SARS-CoV-2 responsible for the present health emergency exhibited close resemblance with SARS-CoV. Both the viruses are zoonotic and belong to a large family of viruses Coronaviridae. The complete virus particle is made up of four major structural proteins, namely spikes (S), nucleocapsid (N), membrane (M), and envelope (E) encoded by virus genome. The S protein of virus shows similarity to S protein of SARS-CoV. COVID-19 spreads from person to person, and this makes it more vulnerable for causing infection. Several efforts are taken to find prevention strategies for COVID-19. Researchers across the globe are working to find effective vaccination for SARS-CoV-2. There is no vaccine or medication available till date for COVID-19. Preventive measures such as social distancing, awareness, maintenance of hygiene, isolation, and movement restrictions can help in control of COVID-19 spread. Proper sanitization and cleaned and sanitized public transport can be effective in inhibiting the spread of the virus. In the present situation of medical emergency, cooperation and support by following advices from the WHO and government only facilitate everyone to come over.

title: Optimization Method for Forecasting Confirmed Cases of COVID-19 in China
abstract: In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (, and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

title: Reproducibility and reporting practices in COVID-19 preprint manuscripts
abstract: The novel coronavirus, COVID-19, has sparked an outflow of scientific research seeking to understand the virus, its spread, and best practices in prevention and treatment. If this international research effort is going to be as swift and effective as possible, it will need to rely on a principle of open science. When researchers share data, code, and software and generally make their work as transparent as possible, it allows other researchers to verify and expand upon their work. Furthermore, it allows public officials to make informed decisions. In this study, we analyzed 535  articles related to COVID-19 for eight transparency criteria and recorded study location and funding information. We found that individual researchers have lined up to help during this crisis, quickly tackling important public health questions, often without funding or support from outside organizations. However, most authors could improve their data sharing and scientific reporting practices. The contrast between the commitment of researchers to doing important research and their reporting practices reveals underlying weaknesses in reporting habits, but not necessarily their science.

title: A pharmacological interactome between COVID-19 patient samples and human sensory neurons reveals potential drivers of neurogenic pulmonary dysfunction
abstract: The SARS-CoV-2 virus infects cells of the airway and lungs in humans causing the disease COVID-19. This disease is characterized by cough, shortness of breath, and in severe cases causes pneumonia and acute respiratory distress syndrome (ARDS) which can be fatal. Bronchial alveolar lavage fluid (BALF) and plasma from mild and severe cases of COVID-19 have been profiled using protein measurements and bulk and single cell RNA sequencing. Onset of pneumonia and ARDS can be rapid in COVID-19, suggesting a potential neuronal involvement in pathology and mortality. We hypothesized that SARS-CoV-2 infection drives changes in immune cell-derived factors that then interact with receptors expressed by the sensory neuronal innervation of the lung to further promote important aspects of disease severity, including ARDS. We sought to quantify how immune cells might interact with sensory innervation of the lung in COVID-19 using published data from patients, existing RNA sequencing datasets from human dorsal root ganglion neurons and other sources, and a genome-wide ligand-receptor pair database curated for pharmacological interactions relevant for neuro-immune interactions. Our findings reveal a landscape of ligand-receptor interactions in the lung caused by SARS-CoV-2 viral infection and point to potential interventions to reduce the burden of neurogenic inflammation in COVID-19 pulmonary disease. In particular, our work highlights opportunities for clinical trials with existing or under development rheumatoid arthritis and other (e.g. CCL2, CCR5 or EGFR inhibitors) drugs to treat high risk or severe COVID-19 cases.

title: Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
abstract: In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.

title: New statistical model for misreported data with application to current public health challenges
abstract: The main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and two real data applications on human papillomavirus incidence in Girona (Catalunya, Spain) and COVID-19 incidence in the Chinese region of Heilongjiang.

title: Mediator-Based Architecture for Integrated Access to Biological Databases
abstract: The amount of biological information accessible via the internet is growing at a tremendous rate. The biologists often encounter problems in accessing huge amount of widely spread data due to disparate formats, remotely dispersed and varying implementation on different platforms and data models. Besides that, the custom browsing and querying mechanism implemented in different data sources requires the users to uniquely query each individual database. Instead of having different custom interfaces to these public bioinformatics databases, an intuitive common integrated data access method is proposed to provide uniform transparent access to disparate biological databases. The proposed mediator-based architecture consists of three conceptual layers namely Query Formulation, Query Transformation and Query Execution.

title: A distinct phylogenetic cluster of Indian SARS-CoV-2 isolates
abstract: From an isolated epidemic, COVID-19 has now emerged as a global pandemic. The availability of genomes in the public domain following the epidemic provides a unique opportunity to understand the evolution and spread of the SARS-CoV-2 virus across the globe. The availability of whole genomes from multiple states in India prompted us to analyse the phylogenetic clusters of genomes in India. We performed whole-genome sequencing for 64 genomes making a total of 361 genomes from India, followed by phylogenetic clustering, substitution analysis, and dating of the different phylogenetic clusters of viral genomes. We describe a distinct phylogenetic cluster (Clade I / A3i) of SARS-CoV-2 genomes from India, which encompasses 41% of all genomes sequenced and deposited in the public domain from multiple states in India. Globally 3.5% of genomes, which till date could not be mapped to any distinct known cluster fall in this newly defined clade. The cluster is characterized by a core set of shared genetic variants – C6312A (T2016K), , C23929T, and C28311T (P13L). Further, the cluster is also characterized by a nucleotide substitution rate of 1.4 × 10−3 variants per site per year, lower than the prevalent A2a cluster, and predominantly driven by variants in the E and N genes and relative sparing of the S gene. Epidemiological assessments suggest that the common ancestor emerged in the month of February 2020 and possibly resulted in an outbreak followed by countrywide spread, as evidenced by the low divergence of the genomes from across the country. To the best of our knowledge, this is the first comprehensive study characterizing the distinct and predominant cluster of SARS-CoV-2 in India.

title: Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making
abstract: In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used documented data of 117,000 patients world-wide with laboratory-confirmed COVID-19. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 93% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.

title: Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches
abstract: This study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using random sampling. We applied several machine learning and deep learning methods including Convolutional Neural Networks (CNN) along with classical machine learners. In deep learning procedure, several pre-trained models were also employed transfer learning in this dataset. Our proposed CNN model showed the highest accuracy (94.03%), AUC (95.52%), f-measure (94.03%), sensitivity (94.03%) and specificity (97.01%) as well as the lowest fall out (4.48%) and miss rate (2.98%) respectively. We also evaluated specificity and fall out rate along with accuracy to identify non-COVID-19 individuals more accurately. As a result, our new models might help to early detect COVID-19 patients and prevent community transmission compared to traditional methods.

title: Thyroid disease in the time of COVID-19
abstract: The novel coronavirus disease COVID-19 is produced by SARS-CoV-2. WHO has declared COVID-19 as a public health emergency, with the most susceptible populations (requiring ventilation) being the elderly, pregnant women and people with associated co-morbidities including heart failure, uncontrolled diabetes, chronic obstructive pulmonary disease, asthma and cancer. However, such general guidance does not provide information regarding COVID-19 risks in patients with suffering from pre-existing thyroid problems, and furthermore, we do not know whether patients with COVID-19 (symptomatic or without symptoms), who have not previously had thyroid issues develop endocrine thyroid dysfunction after infection. The European Society for Endocrinology recently published a statement on COVID-19 and endocrine diseases (Endocrine, 2020); however, thyroid diseases were not mentioned specifically. We have therefore reviewed the current literature on thyroid diseases (excluding cancer) and COVID-19, including data from the previous coronavirus pandemic caused by the SARS-associated coronavirus (SARS-CoV), a member of the same family Coronaviridae leading to severe acute respiratory syndrome (SARS). At the moment there are no data suggesting that thyroid patients are at higher risk of COVID-19, but this requites further research and data analysis.

title: COVID-19 Government Response Event Dataset (CoronaNet v.1.0)
abstract: Governments worldwide have implemented countless policies in response to the COVID-19 pandemic. We present an initial public release of a large hand-coded dataset of over 13,000 such policy announcements across more than 195 countries. The dataset is updated daily, with a 5-day lag for validity checking. We document policies across numerous dimensions, including the type of policy, national versus subnational enforcement, the specific human group and geographical region targeted by the policy, and the time frame within which each policy is implemented. We further analyse the dataset using a Bayesian measurement model, which shows the quick acceleration of the adoption of costly policies across countries beginning in mid-March 2020 through 24 May 2020. We believe that these data will be instrumental for helping policymakers and researchers assess, among other objectives, how effective different policies are in addressing the spread and health outcomes of COVID-19.

title: CD8+ T cell cross-reactivity against SARS-CoV-2 conferred by other coronavirus strains and influenza virus
abstract: While individuals infected with coronavirus disease 2019 (COVID-19) manifested a broad range in susceptibility and severity to the disease, the pre-existing immune memory of related pathogens can influence the disease outcome. Here, we investigated the potential extent of T cell cross-reactivity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can be conferred by other coronaviruses and influenza virus, and generated a map of public and private predicted CD8+ T cell epitopes between coronaviruses. Moreover, to assess the potential risk of self-reactivity and/or diminished T cell response for peptides identical or highly similar to the host, we identified predicted epitopes with high sequence similarity with human proteome. Lastly, we compared predicted epitopes from coronaviruses with epitopes from influenza virus deposited in IEDB to support vaccine development against different virus strains. We believe the comprehensive in silico profile of private and public predicted epitopes across coronaviruses and influenza viruses will facilitate design of vaccines capable of protecting against various viral infections.

title: Genomic Analysis of Viral Outbreaks.
abstract: Genomic analysis is a powerful tool for understanding viral disease outbreaks. Sequencing of viral samples is now easier and cheaper than ever before and can supplement epidemiological methods by providing nucleotide-level resolution of outbreak-causing pathogens. In this review, we describe methods used to answer crucial questions about outbreaks, such as how they began and how a disease is transmitted. More specifically, we explain current techniques for viral sequencing, phylogenetic analysis, transmission reconstruction, and evolutionary investigation of viral pathogens. By detailing the ways in which genomic data can help us understand viral disease outbreaks, we aim to provide a resource that will facilitate the response to future outbreaks.

title: Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes
abstract: Genome detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next-generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present the Genome Detective Coronavirus Typing Tool that can accurately identify the novel severe acute respiratory syndrome (SARS)-related coronavirus (SARS-CoV-2) sequences isolated in China and around the world. The tool can accept up to 2000 sequences per submission and the analysis of a new whole-genome sequence will take approximately 1 min. The tool has been tested and validated with hundreds of whole genomes from 10 coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for SARS-CoV-2. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines to stop the COVID-19 disease.  koen@emweb.be or deoliveira@ukzn.ac.za.  Supplementary data are available at Bioinformatics online.

title: Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net
abstract: The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. Due to rising skepticism towards the sensitivity of RT-PCR as screening method, medical imaging like computed tomography offers great potential as alternative. For this reason, automated image segmentation is highly desired as clinical decision support for quantitative assessment and disease monitoring. However, publicly available COVID-19 imaging data is limited which leads to overfitting of traditional approaches. To address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods and exploiting extensive data augmentation. For further reduction of the overfitting risk, we implemented a standard 3D U-Net architecture instead of new or computational complex neural network architectures. Through a 5-fold cross-validation on 20 CT scans of COVID-19 patients, we were able to develop a highly accurate as well as robust segmentation model for lungs and COVID-19 infected regions without overfitting on the limited data. Our method achieved Dice similarity coefficients of 0.956 for lungs and 0.761 for infection. We demonstrated that the proposed method outperforms related approaches, advances the state-of-the-art for COVID-19 segmentation and improves medical image analysis with limited data. The code and model are available under the following link: https://github.com/frankkramer-lab/covid19.MIScnn

title: Advances in the Relationship Between Coronavirus Infection and Cardiovascular Diseases
abstract: The outbreak of coronavirus disease 2019 (COVID-19) has once again aroused people's concern about coronavirus. Seven human coronaviruses (HCoVs) have been discovered so far, including HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU115, severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus and severe acute respiratory syndrome coronavirus 2. Existing studies show that the cardiovascular disease increased the incidence and severity of coronavirus infection. At the same time, myocardial injury caused by coronavirus infection is one of the main factors contributing to poor prognosis. In this review, the recent clinical findings about the relationship between coronaviruses and cardiovascular diseases and the underlying pathophysiological mechanisms are discussed. This review aimed to provide assistance for the prevention and treatment of COVID-19.

title: Unsupervised cluster analysis of SARS-CoV-2 genomes reflects its geographic progression and identifies distinct genetic subgroups of SARS-CoV-2 virus
abstract: Over 10,000 viral genome sequences of the SARS-CoV-2 virus have been made readily available during the ongoing coronavirus pandemic since the initial genome sequence of the virus was released on the open access Virological website (http://virological.org/) early on January 11. We utilize the published data on the single stranded RNAs of 11, 132 SARS-CoV-2 patients in the GISAID (Elbe and Buckland-Merrett, 2017; Shu and McCauley, 2017) database, which contains fully or partially sequenced SARS-CoV-2 samples from laboratories around the world. Among many important research questions which are currently being investigated, one aspect pertains to the genetic characterization/classification of the virus. We analyze data on the nucleotide sequencing of the virus and geographic information of a subset of 7, 640 SARS-CoV-2 patients without missing entries that are available in the GISAID database. Instead of modelling the mutation rate, applying phylogenetic tree approaches, etc., we here utilize a model-free clustering approach that compares the viruses at a genome-wide level. We apply principal component analysis to a similarity matrix that compares all pairs of these SARS-CoV-2 nucleotide sequences at all loci simultaneously, using the Jaccard index (Jaccard, 1901; Tan et al., 2005; Prokopenko et al., 2016; Schlauch et al., 2017). Our analysis results of the SARS-CoV-2 genome data illustrates the geographic and chronological progression of the virus, starting from the first cases that were observed in China to the current wave of cases in Europe and North America. We also observe that, based on their sequence data, the SARS-CoV-2 viruses cluster in distinct genetic subgroups. It is the subject of ongoing research to examine whether the genetic subgroup could be related to diseases outcome and its potential implications for vaccine development.

title: Artificial Intelligence in Public Health and Epidemiology
abstract: To introduce and summarize current research in the field of Public Health and Epidemiology Informatics.  The 2017 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to provide the editorial team with an enlightened vision to select the best papers.  Among the 843 references retrieved from PubMed and Web of Science, two were finally selected as best papers. The first one analyzes the relationship between the disease, social/mass media, and public emotions to understand public overreaction (leading to a noticeable reduction of social and economic activities) in the context of a nation-wide outbreak of Middle East Respiratory Syndrome (MERS) in Korea in 2015. The second paper concerns a new methodology to de-identify patient notes in electronic health records based on artificial neural networks that outperformed existing methods.  Surveillance is still a productive topic in public health informatics but other very important topics in Public Health are appearing. For example, the use of artificial intelligence approaches is increasing.

title: The 2019 novel coronavirus resource
abstract: An ongoing outbreak of a novel coronavirus infection in Wuhan, China since December 2019 has led to 31,516 infected persons and 638 deaths across 25 countries (till 16:00 on February 7, 2020). The virus causing this pneumonia was then named as the 2019 novel coronavirus (2019-nCoV) by the World Health Organization. To promote the data sharing and make all relevant information of 2019-nCoV publicly available, we construct the 2019 Novel Coronavirus Resource (2019nCoVR, https://bigd.big.ac.cn/ncov). 2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the Global Initiative on Sharing All Influenza Data, National Center for Biotechnology Information, China National GeneBank, National Microbiology Data Center and China National Center for Bioinformation (CNCB)/National Genomics Data Center (NGDC). It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains. Moreover, by linking seamlessly with related databases in CNCB/NGDC, 2019nCoVR offers virus data submission and sharing services for raw sequence reads and assembled sequences. In this report, we provide comprehensive descriptions on data deposition, management, release and utility in 2019nCoVR, laying important foundations in aid of studies on virus classification and origin, genome variation and evolution, fast detection, drug development and pneumonia precision prevention and therapy.

title: A curated transcriptome dataset collection to investigate the blood transcriptional response to viral respiratory tract infection and vaccination.
abstract: The human immune defense mechanisms and factors associated with good versus poor health outcomes following viral respiratory tract infections (VRTI), as well as correlates of protection following vaccination against respiratory viruses, remain incompletely understood. To shed further light into these mechanisms, a number of systems-scale studies have been conducted to measure transcriptional changes in blood leukocytes of either naturally or experimentally infected individuals, or in individual’s post-vaccination. Here we are making available a public repository, for research investigators for interpretation, a collection of transcriptome datasets obtained from human whole blood and peripheral blood mononuclear cells (PBMC) to investigate the transcriptional responses following viral respiratory tract infection or vaccination against respiratory viruses. In total, Thirty one31 datasets, associated to viral respiratory tract infections and their related vaccination studies, were identified and retrieved from the NCBI Gene Expression Omnibus (GEO) and loaded in a custom web application designed for interactive query and visualization of integrated large-scale data. Quality control checks, using relevant biological markers, were performed. Multiple sample groupings and rank lists were created to facilitate dataset query and interpretation. Via this interface, users can generate web links to customized graphical views, which may be subsequently inserted into manuscripts to report novel findings. The GXB tool enables browsing of a single gene across projects, providing new perspectives on the role of a given molecule across biological systems in the diagnostic and prognostic following VRTI but also in identifying new correlates of protection. This dataset collection is available at: http://vri1.gxbsidra.org/dm3/geneBrowser/list.

title: From SARS-CoV to SARS-CoV-2: safety and broad-spectrum are important for coronavirus vaccine development
abstract: The global pandemic of COVID-19 caused by SARS-CoV-2 (also known as 2019-nCoV and HCoV-19) has posed serious threats to public health and economic stability worldwide, thus calling for development of vaccines against SARS-CoV-2 and other emerging and reemerging coronaviruses. Since SARS-CoV-2 and SARS-CoV have high similarity of their genomic sequences and share the same cellular receptor (ACE2), it is essential to learn the lessons and experiences from the development of SARS-CoV vaccines for the development of SARS-CoV-2 vaccines. In this review, we summarized the current knowledge on the advantages and disadvantages of the SARS-CoV vaccine candidates and prospected the strategies for the development of safe, effective and broad-spectrum coronavirus vaccines for prevention of infection by currently circulating SARS-CoV-2 and other emerging and reemerging coronaviruses that may cause future epidemics or pandemics.

title: TOWARD A COVID-19 SCORE-RISK ASSESSMENTS AND REGISTRY
abstract: Importance Critical care resources like ventilators, used to manage the current COVID-19 pandemic, are potentially inadequate. Established triage standards and guidelines may not contain the most appropriate severity assessment and outcome prediction models.  Develop a draft pandemic specific triage assessment score for the current COVID-19 pandemic. Design a website where initial Toward a COVID-19 Scores (TACS) can be quickly calculated and used to compare various treatment strategies. Create a TACS Registry where data and outcomes for suspected and confirmed COVID-19 patients can be recorded. Use the TACS Registry to develop an influenza epidemic specific database and score for use in future respiratory based epidemics.  Retrospective analysis of 3,301 ICU admissions with respiratory failure admitted to 41 U.S. Intensive Care Units from 2015-19. Independent external validation on 1,175 similar ICU Admissions using identical entry criteria from Barnes Jewish Hospital (BJH), Washington University from 2016-2019.  TACS was created with 16 readily available predictive variables for risk assessment of hospital mortality 24 hours after ICU Admission and the need for prolonged assisted mechanical ventilation (PAMV) ( >>96 hours) at 24- and 48-hours post ICU admission.  TACS achieved an Area Under the Curve (AUC) for hospital mortality after 24 hours of 0.80 in the development dataset; 0.81 in the internal validation dataset. At a probability of 50% hospital mortality, positive predictive value (PPV) was 0.55, negative predictive value (NPV) 0.89; sensitivity 22%, specificity 97%. For PAMV after 24 hours, the AUC was 0.84 in the development dataset, 0.81 in the validation dataset. For PAMV after 48 hours, the AUC was 0.82 in the development dataset, 0.78 in the validation dataset. In the external validation the AUC for TACS was 0.76 +/- 0.024. We launched a website that is scaled for mobile device use ( https://covid19score.azurewebsites.net/) that provides open access to a user-friendly TACS Calculator for all predictions. We also designed a voluntary TACS Registry for collection of data and outcomes on ICU Admissions with COVID-19.  Toward a COVID-19 score is a starting point for an epidemic specific triage assessment that could be used to evaluate various approaches to treatment. The TACS Registry provides the ability to establish a respiratory specific outcomes database that can be used to create a triage approach for future such pandemics.

title: The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak
abstract: Coronavirus disease (COVID-19) is caused by SARS-COV2 and represents the causative agent of a potentially fatal disease that is of great global public health concern. Based on the large number of infected people that were exposed to the wet animal market in Wuhan City, China, it is suggested that this is likely the zoonotic origin of COVID-19. Person-to-person transmission of COVID-19 infection led to the isolation of patients that were subsequently administered a variety of treatments. Extensive measures to reduce person-to-person transmission of COVID-19 have been implemented to control the current outbreak. Special attention and efforts to protect or reduce transmission should be applied in susceptible populations including children, health care providers, and elderly people. In this review, we highlights the symptoms, epidemiology, transmission, pathogenesis, phylogenetic analysis and future directions to control the spread of this fatal disease.

title: The aging transcriptome and cellular landscape of the human lung in relation to SARS-CoV-2
abstract: Since the emergence of SARS-CoV-2 in December 2019, Coronavirus Disease-2019 (COVID-19) has rapidly spread across the globe. Epidemiologic studies have demonstrated that age is one of the strongest risk factors influencing the morbidity and mortality of COVID-19. Here, we interrogate the transcriptional features and cellular landscapes of the aging human lung through integrative analysis of bulk and single-cell transcriptomics. By intersecting these age-associated changes with experimental data on host interactions between SARS-CoV-2 or its relative SARS-CoV, we identify several age-associated factors that may contribute to the heightened severity of COVID-19 in older populations. We observed that age-associated gene expression and cell populations are significantly linked to the heightened severity of COVID-19 in older populations. The aging lung is characterized by increased vascular smooth muscle contraction, reduced mitochondrial activity, and decreased lipid metabolism. Lung epithelial cells, macrophages, and Th1 cells decrease in abundance with age, whereas fibroblasts, pericytes and CD4+ Tcm cells increase in abundance with age. Several age-associated genes have functional effects on SARS-CoV replication, and directly interact with the SARS-CoV-2 proteome. Interestingly, age-associated genes are heavily enriched among those induced or suppressed by SARS-CoV-2 infection. These analyses illuminate potential avenues for further studies on the relationship between the aging lung and COVID-19 pathogenesis, which may inform strategies to more effectively treat this disease.

title: Recurrence of positive SARS-CoV-2 RNA in COVID-19: A case report
abstract: The ongoing outbreak of COVID-19 that began in Wuhan, China, has constituted a Public Health Emergency of International Concern, with cases confirmed in multiple countries. Currently, patients are the primary source of infection. We report a confirmed case of COVID-19 whose oropharyngeal swab test of SARS-CoV-2 RNA turned positive in convalescence. This case highlights the importance of active surveillance of SARS-CoV-2 RNA for infectivity assessment.

title: Clinical characteristics and diagnostic challenges of pediatric COVID-19: A systematic review and meta-analysis
abstract: Current studies on pediatric coronavirus disease 2019 (COVID-19) are rare. The clinical characteristics and spectrum are still unknown. Facing this unknown and emerging pathogen, we aimed to collect current evidence about COVID-19 in children.  We performed a systematic review in PubMed and Embase to find relevant case series. Because some reports were published in Chinese journals, the journals and publications of the Chinese Medical Association related to COVID-19 were completely reviewed. A random effects model was used to pool clinical data in the meta-analysis.  Nine case series were included. In the pooled data, most of patients (75%) had a household contact history. The disease severity was mainly mild to moderate (98%). Only 2 children (2%) received intensive care. Fever occurred in 59% of the patients, while cough in 46%. Gastrointestinal symptoms (12%) were uncommon. There are 26% children are asymptomatic. The most common radiographic finding was ground glass opacities (48%). Currently, there is no evidence of vertical transmission to neonates born to mothers with COVID-19. Compared with the most relevant virus, SARS-CoV, SARS-CoV-2 causes less severe disease. Conclusion COVID-19 has distinct features in children. The disease severity is mild. Current diagnosis is based mainly on typical ground glass opacities on chest CT, epidemiological suspicion and contact tracing.

title: An epidemiologic profile of COVID-19 patients in Vietnam
abstract: There is a paucity of data on the COVID-19 pandemic in Vietnam. In this paper, we sought to provide an epidemiologic description of patients who were infected with SARS-Cov-2 in Vietnam.  Data were abstracted from the wikipedia's COVID-19 information resource and Johns Hopkins University Dashboard. Demographic data and treatment status were obtained for each patient in each day. The coverage period was from 23/1/2020 to 10/4/2020. Descriptive analyses of incident cases were stratified by gender and age group. The estimation of the reproduction ratio was done with a bootstrap method using the R statistical environment.  During the coverage period, Vietnam has recorded 257 cases of COVID-19. Approximately 54% of the cases were women. The median age of patients was 30 years (range: 3 months to 88 years), with 78% of patients aged 49 or younger. About 66% (n = 171) of patients were overseas tourists (20%) and Vietnamese students or workers returning from overseas (46%). Approximately 57% (n = 144) of patients have been recovered and discharged from hospitals. There have been no mortality. The reproduction ratio was estimated to range between 0.95 and 1.24.  These data indicate that a majority of COVID-19 patients in Vietnam was imported cases in overseas tourists and young students and workers who had returned from overseas.

title: Analyzing the epidemiological outbreak of COVID-19: A visual exploratory data analysis approach
abstract: There is an obvious concern globally regarding the fact about the emerging coronavirus 2019 novel coronavirus (2019-nCoV) as a worldwide public health threat. As the outbreak of COVID-19 causes by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) progresses within China and beyond, rapidly available epidemiological data are needed to guide strategies for situational awareness and intervention. The recent outbreak of pneumonia in Wuhan, China, caused by the SARS-CoV-2 emphasizes the importance of analyzing the epidemiological data of this novel virus and predicting their risks of infecting people all around the globe. In this study, we present an effort to compile and analyze epidemiological outbreak information on COVID-19 based on the several open datasets on 2019-nCoV provided by the Johns Hopkins University, World Health Organization, Chinese Center for Disease Control and Prevention, National Health Commission, and DXY. An exploratory data analysis with visualizations has been made to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China. Overall, at the outset of an outbreak like this, it is highly important to readily provide information to begin the evaluation necessary to understand the risks and begin containment activities.

title: Feeling Positive About Reopening?New Normal Scenarios from COVID-19 ReopenSentiment Analytics
abstract: The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to 'reopen' the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based four scenarios framework, which will remain useful for future crisis analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.

title: Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm
abstract: The global COVID-19 outbreak is worrisome both for its high rate of spread, and the high case fatality rate reported by early studies and now in Italy. We report a new methodology, the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The estimated days from hospital admission to death was 13 (standard deviation (SD), 6 days). The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The death rate of COVID-19 ranges from 0.75% to 3% and may decrease in the future. The results showed that the real death numbers had fallen into the predicted ranges. In addition, using the preliminary data from China, the PIBA method was successfully used to estimate the death rate and predict the death numbers of the Korean population. In conclusion, PIBA can be used to efficiently estimate the death rate of a new infectious disease in real-time and to predict future deaths. The spread of 2019-nCoV and its case fatality rate may vary in regions with different climates and temperatures from Hubei and Wuhan. PIBA model can be built based on known information of early patients in different countries.

title: We have to write and share valid and reliable information on COVID-19

title: Indian Publications on SARS-CoV-2: A bibliometric study of WHO COVID-19 database
abstract: Nowadays, the whole World is under threat of Coronavirus disease (COVID-19). The ongoing COVID-19 pandemic has resulted in many fatalities and forced scientific communities to foster their Research and Development (R&D) activities. As a result, there is an enormous growth of scholarly literature on the subject. We here in this study have assessed the Indian publications contributions on COVID-19.  WHO is curating global scientific literature on coronavirus since it declared COVID-19 a global pandemic through Global Research Database on COVID-19. The present study analyzed Indian publications on SARS-CoV-2 as found in WHO COVID-19 database. The research data was restricted for the period of March 2, 2020 to May 12, 2020.  The study found that there is a considerable and constant growth of Indian publications on COVID-19 from mid-April. It is interesting to note that, the most prolific authors belong to either AIIMS or ICMR institutes. Delhi state contributed highest number of publications on COVID-19. The AIIMS, New Delhi was the most productive institution in terms of publications. The Indian Journal of Medical Research has emerged as the productive journal contributing highest number of the publications. In terms of research area, the majority of the publications were related to Epidemiology.  The highly cited publications were of evidenced based studies. It is observed that the studies pertaining to virology, diagnosis and treatment, clinical features etc. have received highest citations than general studies on epidemiology or pandemic.

title: Outbreak Trends of Coronavirus Disease–2019 in India: A Prediction
abstract: The objective of this paper is to prepare the government and citizens of India to take or implement the control measures proactively to reduce the impact of coronavirus disease 2019 (COVID-19).  In this work, the COVID-19 outbreak in India has been predicted based on the pattern of China using a machine learning approach. The model is built to predict the number of confirmed cases, recovered cases, and death cases based on the data available between January 22, 2020, and April 3, 2020. The time series forecasting method is used for prediction models.  The COVID-19 effects are predicted to be at peak between the third and fourth weeks of April 2020 in India. This outbreak is predicted to be controlled around the end of May 2020. The total number of predicted confirmed cases of COVID-19 might reach around 68 978, and the number of deaths due to COVID-19 are predicted to be 1557 around April 25, 2020, in India. If this outbreak is not controlled by the end of May 2020, then India will face a severe shortage of hospitals, and it will make this outbreak even worse.  The COVID-19 pandemic may be controlled if the Government of India takes proactive steps to aggressively implement a lockdown in the country and extend it further. This presented epidemiological model is an effort to predict the future forecast of COVID-19 spread, based on the present scenario, so that the government can frame policy decisions, and necessary actions can be initiated.

title: Curbing the AI-induced enthusiasm in diagnosing COVID-19 on chest X-Rays: the present and the near-future
abstract: In the current context of COVID-19 pandemic, a rapid and accessible screening tool based on image processing of chest X-rays (CXRs) using machine learning (ML) approaches would be much needed. Initially, we intended to create and validate an ML software solution able to discriminate on the basis of the CXR between SARS-CoV-2-induced bronchopneumonia and other bronchopneumonia etiologies. A systematic search of PubMed, Scopus and arXiv databases using the following search terms ["artificial intelligence" OR "deep learning" OR "neural networks"], AND ["COVID-19" OR "SARS-CoV-2"] AND ["chest X-ray" OR "CXR" OR "X-ray"] found 14 recent studies. Most of them declared to be able to confidently identify COVID-19 based on CXRs using deep neural networks. Firstly, weaknesses of artificial intelligence (AI) solutions were analyzed, tackling the issues with datasets (from both medical and technical points of view) and the vulnerability of used algorithms. Then, arguments were provided for why our study design is stronger and more realistic than the previously quoted papers, balancing the possible false expectations with facts. The authors consider that the potential of AI use in COVID-19 diagnosis on CXR is real. However, scientific community should be careful in interpreting statements, results and conclusions regarding AI use in imaging. It is therefore necessary to adopt standards for research and publication of data, because it seems that in the recent months scientific reality suffered manipulations and distortions. Also, a call for responsible approaches to the imaging methods in COVID-19 is raised. It seems mandatory to follow some rigorous approaches in order to provide with adequate results in daily routine. In addition, the authors intended to raise public awareness about the quality of AI protocols and algorithms and to encourage public sharing of as many CXR images with common quality standards.

title: Corona Virus (COVID-19) “Infodemic” and Emerging Issues through a Data Lens: The Case of China
abstract: Coronavirus (COVID-19) is a humanitarian emergency, which started in Wuhan in China in early December 2019, brought into the notice of the authorities in late December, early January 2020, and, after investigation, was declared as an emergency in the third week of January 2020. The WHO declared this as Public Health Emergency of International Concern (PHEIC) on 31th of January 2020, and finally a pandemic on 11th March 2020. As of March 24th, 2020, the virus has caused a casualty of over 16,600 people worldwide with more than 380,000 people confirmed as infected by it, of which more than 10,000 cases are serious. Mainly based on Chinese newspapers, social media and other digital platform data, this paper analyzes the timeline of the key actions taken by the government and people over three months in five different phases. It found that although there was an initial delay in responding, a unique combination of strong governance, strict regulation, strong community vigilance and citizen participation, and wise use of big data and digital technologies, were some of the key factors in China’s efforts to combat this virus. Being inviable and non-measurable (unlike radioactive exposure), appropriate and timely information is very important to form the basic foundation of mitigation and curative measures. Infodemic, as it is termed by WHO, is a key word, where different stakeholder’s participation, along with stricter regulation, is required to reduce the impact of fake news in this information age and social media. Although different countries will need different approaches, focusing on its humanitarian nature and addressing infodemic issues are the two critical factors for future global mitigation efforts.

title: The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences
abstract: Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work, we explored the application of dimensionality reduction methods to numerically represent high-throughput sequence data for three important biological applications of virus sequence data: reference-based mapping, short sequence classification and de novo assembly. Leveraging highly compressed sequence transformations to accelerate sequence comparison, our approach yielded comparable accuracy to existing approaches, further demonstrating its suitability for sequences originating from diverse virus populations. We assessed the application of our methodology using both synthetic and real viral pathogen sequences. Our results show that the use of highly compressed sequence approximations can provide accurate results, with analytical performance retained and even enhanced through appropriate dimensionality reduction of sequence data.

title: Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters
abstract: Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China in December 2019, and is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a pandemic with an estimated death rate between 1% and 5%; and an estimated R 0 between 2.2 and 6.7 according to various sources. As of March 28th, 2020, there were over 649,000 confirmed cases and 30,249 total deaths, globally. In the United States, there were over 115,500 cases and 1891 deaths and this number is likely to increase rapidly. It is critical to detect clusters of COVID-19 to better allocate resources and improve decision-making as the outbreaks continue to grow. Using daily case data at the county level provided by Johns Hopkins University, we conducted a prospective spatial-temporal analysis with SaTScan. We detect statistically significant space-time clusters of COVID-19 at the county level in the U.S. between January 22nd-March 9th, 2020, and January 22nd-March 27th, 2020. The space-time prospective scan statistic detected “active” and emerging clusters that are present at the end of our study periods – notably, 18 more clusters were detected when adding the updated case data. These timely results can inform public health officials and decision makers about where to improve the allocation of resources, testing sites; also, where to implement stricter quarantines and travel bans. As more data becomes available, the statistic can be rerun to support timely surveillance of COVID-19, demonstrated here. Our research is the first geographic study that utilizes space-time statistics to monitor COVID-19 in the U.S.

title: Increased generalized anxiety, depression and distress during the COVID-19 pandemic: a cross-sectional study in Germany
abstract: Since the first cases of the novel coronavirus disease SARS-CoV-2 were reported in December 2019 in China, the virus has spread in most countries. The aim of the present study was to assess initial data on the mental health burden of the German public during the COVID-19 pandemic.  A cross-sectional study was conducted in Germany and collected complete datasets from 15 704 German residents aged 18 years and over. Besides demographics, generalized anxiety (GAD-7), depression (PHQ-2) and psychological distress (DT) were assessed. Furthermore, COVID-19-related fear, trust in governmental actions to face COVID-19 and the subjective level of information regarding COVID-19 were covered.  Significantly increased symptoms were highly prevalent in all dimensions: generalized anxiety (44.9%), depression (14.3%), psychological distress (65.2%) and COVID-19-related fear (59%). Females and younger people reported higher mental burden. Trust in governmental actions to face COVID-19 and the subjective level of information regarding COVID-19 are negatively associated with mental health burden. However, the subjective level of information regarding COVID-19 is positively associated with increased COVID-19-related fear.  The provision of appropriate psychological interventions for those in need and the provision of transparency and comprehensible information are crucial during the current pandemic.

title: Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal
abstract: To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia.  Rapid systematic review and critical appraisal.  PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020.  Studies that developed or validated a multivariable covid-19 related prediction model.  At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).  2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed.  Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

title: COVID-19: a public health approach to manage domestic violence is needed

title: Metagenomic ventures into outer sequence space
abstract: Sequencing DNA or RNA directly from the environment often results in many sequencing reads that have no homologs in the database. These are referred to as “unknowns," and reflect the vast unexplored microbial sequence space of our biosphere, also known as “biological dark matter." However, unknowns also exist because metagenomic datasets are not optimally mined. There is a pressure on researchers to publish and move on, and the unknown sequences are often left for what they are, and conclusions drawn based on reads with annotated homologs. This can cause abundant and widespread genomes to be overlooked, such as the recently discovered human gut bacteriophage crAssphage. The unknowns may be enriched for bacteriophage sequences, the most abundant and genetically diverse component of the biosphere and of sequence space. However, it remains an open question, what is the actual size of biological sequence space? The de novo assembly of shotgun metagenomes is the most powerful tool to address this question.

title: Systematic Analysis of ACE2 and TMPRSS2 Expression in Salivary Glands Reveals Underlying Transmission Mechanism Caused by SARS‐CoV‐2
abstract: Coronavirus disease 2019 (COVID‐19) is a global pandemic that has caused severe health threats and fatalities in almost all communities. Studies have detected SARS‐CoV‐2 in saliva with a viral load that lasts for a long period. However, researchers are yet to establish whether SARS‐CoV‐2 can directly enter the salivary glands. Therefore, this study aimed to assess the presence of ACE2/TMPRSS2 expression in salivary glands using publicly available databases.  The distribution of ACE2 and TMPRSSs family in salivary gland tissue and other tissues was analyzed. The GTEx dataset was employed to explore the ACE2 and TMPRSS2 expression in various body organs and salivary glands in a healthy population. The single‐cell sequencing data for salivary gland samples (including submandibular salivary gland and parotid gland) from mice were collected and analyzed. The components and proportions of salivary gland cells expressing the key protease TMPRSSs family were analyzed.  Transcriptome data analysis showed that ACE2 and TMPRSS2 were expressed in salivary glands. The expression levels of ACE2 and TMPRSS2 were marginal without significant differences in different age groups or between men and women. Single‐cell RNA sequence analysis indicated that TMPRSS2 was mainly expressed in salivary gland epithelial cells.  We speculate that SARS‐CoV‐2 may be entered in salivary glands. This article is protected by copyright. All rights reserved.

title: Human monoclonal IgG selection of Plasmodium falciparum for the expression of placental malaria-specific variant surface antigens
abstract: Pregnancy-associatedPlasmodium falciparum malaria (PAM) is a major cause of morbidity and mortality in African women and their offspring. PAM is characterized by accumulation of infected erythrocytes (IEs) that adhere to chondroitin sulphate A (CSA) in the placental intervillous space. We show here that human monoclonal IgG antibodies with specificity for variant surface antigens (VSA) specifically expressed by CSA-adhering IEs (VSA(PAM)) can be used in vitro to select parasites from nonpregnant donors to express VSA(PAM) and that this selection for VSA(PAM) expression results in preferential transcription of var2csa. The results corroborate current efforts to develop PAM-specific vaccines based on VAR2CSA.

title: A distinct name is needed for the new coronavirus

title: Is the use of RAS inhibitors safe in the current era of COVID-19 pandemic?
abstract: Antihypertensive drugs are one of the most widely used pharmacologic agent in the world and it is predominantly used in the elderly subjects. Pneumonia is the most common cause of death in the extremely old subject. During infection and its complication such as sepsis, hypotension could be exacerbated by antihypertensive drugs because homeostasis mechanisms such as sodium balance, renin angiotensin aldosterone system and/or sympathetic nervous system can be mitigated by antihypertensive drug therapy. Severe Acute Respiratory Syndrome-Coronavirus-1 and 2 viral surface protein is known to attach angiotensin converting enzyme 2 (ACE2) on the cell membrane to facilitate viral entry into the cytoplasm. Despite the theoretical concerns of increased ACE2 expression by Renin-Angiotensin-Aldosterone system (RAS) blockade, there is no evidence that RAS inhibitors are harmful during COVID-19 infection and have in fact been shown to be beneficial in animal studies. Therefore, it is recommended to maintain RAS blockade during the current corona virus pandemic.

title: Severe Coronavirus Infections in Pregnancy: A Systematic Review.
abstract: To inform the current coronavirus disease 2019 (COVID-19) outbreak, we conducted a systematic literature review of case reports of Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, during pregnancy and summarized clinical presentation, course of illness, and pregnancy and neonatal outcomes.  We searched MEDLINE and ClinicalTrials.gov from inception to April 23, 2020.  We included articles reporting case-level data on MERS-CoV, SARS-CoV, and SARS-CoV-2 infection in pregnant women. Course of illness, indicators of severe illness, maternal health outcomes, and pregnancy outcomes were abstracted from included articles. TABULATION, INTEGRATION, AND RESULTS We identified 1,328 unique articles, and 1,253 articles were excluded by title and abstract review. We completed full-text review on 75, and 29 articles were excluded by full-text review. Among 46 publications reporting case-level data, eight described 12 cases of MERS-CoV infection, seven described 17 cases of SARS-CoV infection, and 31 described 98 cases of SARS-CoV-2 infection. Clinical presentation and course of illness ranged from asymptomatic to severe fatal disease, similar to the general population of patients. Severe morbidity and mortality among women with MERS-CoV, SARS-CoV, or SARS-CoV-2 infection in pregnancy and adverse pregnancy outcomes, including pregnancy loss, preterm delivery, and laboratory evidence of vertical transmission, were reported.  Understanding whether pregnant women may be at risk for adverse maternal and neonatal outcomes from severe coronavirus infections is imperative. Data from case reports of SARS-CoV, MERS-CoV, and SAR-CoV-2 infections during pregnancy are limited, but they may guide early public health actions and clinical decision-making for COVID-19 until more rigorous and systematically collected data are available. The capture of critical data is needed to better define how this infection affects pregnant women and neonates. This review was not registered with PROSPERO.

title: Comparing and Integrating US COVID-19 Daily Data from Multiple Sources: A County-Level Dataset with Local Characteristics
abstract: Over the past several months, the outbreak of COVID-19 has been expanding over the world. A reliable and accurate dataset of the cases is vital for scientists to conduct related research and for policy-makers to make better decisions. We collect the COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, and compare the similarities and differences among them. In addition, we examine the following problems which occur frequently: (1) the order dependencies violation, (2) the delay-reported issue on weekends and/or holidays, and (3) abnormal data point or data period. We also integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources, such as health infrastructure, demographic, socioeconomic, and environment information, which are important for understanding the spread of the virus.

title: Impact of weather indicators on the COVID-19 outbreak: A multi-state study in India
abstract: The present study examines the impact of weather indicators on the COVID-19 outbreak in the majorly affected states of India. In this study, we hypothesize that the weather indicators could significantly influence the impact of the corona virus. The Kendall and Spearman rank correlation tests were chosen to conduct the statistical analysis. In this regard, we compiled a daily dataset including confirmed case counts, Recovered case counts, Deceased cases, Average Temperature, Maximum Relative Humidity, Maximum Wind Speed for six most affected states of India during the period of March 25, 2020 to April 24, 2020. We investigated that the average Humidity and Average Temperature seven days ago play a significant role in the recovery of coronavirus cases. The rise in average temperature will improve the recovery rate in the days to come. The cities with very high humidity levels or dry weather conditions have high probabilities of recovery from COVID-19. The findings of this research will help the policymakers to identify risky geographic areas and enforce timely preventive measures.

title: Scientific Opinion on porcine epidemic diarrhoea and emerging porcine deltacoronavirus
abstract: In the last decade, many porcine epidemic diarrhoea (PED) outbreaks have been reported by several countries in Asia whereas only a few Member States of the European Union (EU) have reported PED clinical cases and/or PED virus (PEDV)‐seropositive animals. This alphacoronavirus was first reported in the USA in May 2013, followed by rapid spread throughout the country and outbreaks reported by several countries in the Americas. The recent PEDV‐EU isolates have high level of sequence identity to PEDV‐Am isolates. Based on nucleotide sequencing, multiple variants of PEDV are circulating in Europe, the Americas and Asia but any difference in virulence and antigenicity is currently unknown. Serological cross‐reactivity has been reported between PEDV isolated in Europe and in the Americas; however no data regarding cross‐protection are available. The impact of different PEDV strains is difficult to compare between one country and another, since impact is dependent not only on pathogenicity but also on factors such as biosecurity, farm management, sanitary status or herd immune status. However, the clinical signs of PEDV infections in naive pigs are similar in different countries with mortalities up to 100% in naive newborn piglets. The impact of recently reported PED outbreaks in Asia and the USA seems to be more severe than what has been described in Europe. Infected animals, faeces, feed and objects contaminated with faeces are matrices that have been reported to transmit PEDV between farms. Infectious PEDV has been detected in spray‐dried porcine plasma (SDPP) in one study but the origin of the infectious PEDV in SDPP is not clear. Detection of porcine deltacoronavirus (PDCoV) has been reported in a few countries but only limited testing has been done. Based on the currently available information, it seems that PDCoV would have a lower impact than PEDV.

title: Spectral Clustering by Subspace Randomization and Graph Fusion for High-Dimensional Data
abstract: Subspace clustering has been gaining increasing attention in recent years due to its promising ability in dealing with high-dimensional data. However, most of the existing subspace clustering methods tend to only exploit the subspace information to construct a single affinity graph (typically for spectral clustering), which often lack the ability to go beyond a single graph to explore multiple graphs built in various subspaces in high-dimensional space. To address this, this paper presents a new spectral clustering approach based on subspace randomization and graph fusion (SC-SRGF) for high-dimensional data. In particular, a set of random subspaces are first generated by performing random sampling on the original feature space. Then, multiple K-nearest neighbor (K-NN) affinity graphs are constructed to capture the local structures in the generated subspaces. To fuse the multiple affinity graphs from multiple subspaces, an iterative similarity network fusion scheme is utilized to achieve a unified graph for the final spectral clustering. Experiments on twelve real-world high-dimensional datasets demonstrate the superiority of the proposed approach. The MATLAB source code is available at https://www.researchgate.net/publication/338864134.

title: Closteroviridae
abstract: This chapter focuses on Closteroviridae family whose member genuses are Closterovirus, Ampelovirus, and Crinivirus. The virions are helically constructed filaments with a pitch of the primary helix in the range of 3.4–3.8 nm, containing about 10 protein subunits per turn of the helix and showing a central hole of 3–4 nm. The very flexuous and open structure of the particles is the most conspicuous trait of members of the family. The virions have a diameter of about 12 nm and their length ranges from 650 nm in case of species with fragmented genome, to over 2000 nm in case of species with monopartite genome. The fragility of virions and a tendency to end-to-end aggregation contribute to the fact that a range of lengths is often given for single viruses. The virions of several species are degraded by CsCl and are unstable in high salt concentration, resist moderately high temperatures and organic solvents, but are sensitive to RNase and chelation. Regardless of the genome type, monopartite or fragmented, virions contain a single molecule of linear, positive sense, single stranded RNA, constituting 5–6% of the particle weight. The structural proteins of most members of the family consist of a major CP and of a diverged copy of it denoted minor CP (CPm), with a size ranging from 22 to 46 kDa (CP) and 23 to 80 kDa (CPm). The members of the family have one of the largest genomes among plant viruses because of sequence duplication and acquisition of nonviral coding sequences such as protease, and HSP70 protein via RNA recombination.

title: Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19
abstract: The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world and it is affecting the whole society. The current gold standard test for screening COVID-19 patients is the polymerase chain reaction test. However, the COVID-19 test kits are not widely available and time-consuming. Thus, as an alternative, chest X-rays are being considered for quick screening. Since the presentation of COVID-19 in chest X-rays is varied in features and specialization in reading COVID-19 chest X-rays are required thus limiting its use for diagnosis. To address this challenge of reading chest X-rays by radiologists quickly, we present a multi-channel transfer learning model based on ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray. Three ResNet-based models (Models a, b, and c) were retrained using Dataset_A (1579 normal and 4429 diseased), Dataset_B (4245 pneumonia and 1763 non-pneumonia), and Dataset_C (184 COVID-19 and 5824 Non-COVID19), respectively, to classify (a) normal or diseased, (b) pneumonia or non-pneumonia, and (c) COVID-19 or non-COVID19. Finally, these three models were ensembled and fine-tuned using Dataset_D (1579 normal, 4245 pneumonia, and 184 COVID-19) to classify normal, pneumonia, and COVID-19 cases. Our results show that the ensemble model is more accurate than the single ResNet model, which is also re-trained using Dataset_D as it extracts more relevant semantic features for each class. Our approach provides a precision of 94 % and a recall of 100%. Thus, our method could potentially help clinicians in screening patients for COVID-19, thus facilitating immediate triaging and treatment for better outcomes.

title: Probable Pangolin Origin of SARS-CoV-2 Associated with the COVID-19 Outbreak
abstract: An outbreak of coronavirus disease 2019 (COVID-19) caused by the 2019 novel coronavirus (SARS-CoV-2) began in the city of Wuhan in China and has widely spread worldwide. Currently, it is vital to explore potential intermediate hosts of SARS-CoV-2 to control COVID-19 spread. Therefore, we reinvestigated published data from pangolin lung samples from which SARS-CoV-like CoVs were detected by Liu et al. . We found genomic and evolutionary evidence of the occurrence of a SARS-CoV-2-like CoV (named Pangolin-CoV) in dead Malayan pangolins. Pangolin-CoV is 91.02% and 90.55% identical to SARS-CoV-2 and BatCoV RaTG13, respectively, at the whole-genome level. Aside from RaTG13, Pangolin-CoV is the most closely related CoV to SARS-CoV-2. The S1 protein of Pangolin-CoV is much more closely related to SARS-CoV-2 than to RaTG13. Five key amino acid residues involved in the interaction with human ACE2 are completely consistent between Pangolin-CoV and SARS-CoV-2, but four amino acid mutations are present in RaTG13. Both Pangolin-CoV and RaTG13 lost the putative furin recognition sequence motif at S1/S2 cleavage site that can be observed in the SARS-CoV-2. Conclusively, this study suggests that pangolin species are a natural reservoir of SARS-CoV-2-like CoVs.

title: PolicyCLOUD: Analytics as a Service Facilitating Efficient Data-Driven Public Policy Management
abstract: While several application domains are exploiting the added-value of analytics over various datasets to obtain actionable insights and drive decision making, the public policy management domain has not yet taken advantage of the full potential of the aforementioned analytics and data models. Diverse and heterogeneous datasets are being generated from various sources, which could be utilized across the complete policies lifecycle (i.e. modelling, creation, evaluation and optimization) to realize efficient policy management. To this end, in this paper we present an overall architecture of a cloud-based environment that facilitates data retrieval and analytics, as well as policy modelling, creation and optimization. The environment enables data collection from heterogeneous sources, linking and aggregation, complemented with data cleaning and interoperability techniques in order to make the data ready for use. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders.

title: Automated detection of COVID-19 cases using deep neural networks with X-ray images
abstract: The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.

title: Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel Coronavirus
abstract: A pandemic of a novel Coronavirus emerged in December of 2019 (COVID-19), causing devastating public health impact across the world. In the absence of a safe and effective vaccine or antivirals, strategies for con- trolling and mitigating the burden of the pandemic are focused on non-pharmaceutical interventions, such as social-distancing, contact-tracing, quarantine, isolation and the use of face-masks in public. We develop a new mathematical model for assessing the population-level impact of the aforementioned control and mitigation strategies. Rigorous analysis of the model shows that the disease-free equilibrium is locally-asymptotically stable if a certain epidemiological threshold, known as the reproduction number (denoted by Rc), is less than unity. This equilibrium is globally-asymptotically stable, for a special case of the model where quarantined-susceptible individuals do not acquire COVID-19 infection during quarantine, when Rc is less than unity. The epidemiological consequence of this theoretical result is that, the community-wide implementation of control interventions that can bring (and maintain) Rc to a value less than unity will lead to the effective control (or elimination) of COVID-19 in the community. Simulations of the model, using data relevant to COVID-19 transmission dynamics in the US state of New York and the entire US, show that the pandemic burden will peak in mid and late April, respectively. The worst-case scenario projections for cumulative mortality (based on baseline levels of interventions) are 105, 100 for New York state and 164, 000 for the entire US by the end of the pandemic. These numbers dramatically decreased by 80% and 64%, respectively, if adherence to strict social-distancing measures is improved and maintained until the end of May or June. The duration and timing of the relaxation or termination of the strict social-distancing measures are crucially important in determining the future trajectory of the COVID-19 pandemic. This study shows that early termination of the strict social-distancing measures could trigger a devastating second wave with burden similar to those projected before the onset of the strict social-distance measures were implemented. The use of efficacious face-masks (such as surgical masks, with estimated efficacy ≥ 70%) in public could lead to the elimination of the pandemic if at least 70% of the residents of New York state use such masks in public consistently (nationwide, a compliance of at least 80% will be required using such masks). The use of low efficacy masks, such as cloth masks (of estimated efficacy less than 30%), could also lead to significant reduction of COVID-19 burden (albeit, they are not able to lead to elimination). Combining low efficacy masks with improved levels of the other anti-COVID-19 intervention strategies can lead to the elimination of the pandemic. This study emphasizes the important role social-distancing plays in curtailing the burden of COVID-19. Increases in the adherence level of social-distancing protocols result in dramatic reduction of the burden of the pandemic, and the timely implementation of social-distancing measures in numerous states of the US may have averted a catastrophic outcome with respect to the burden of COVID-19. Using face-masks in public (including the low efficacy cloth masks) is very useful in minimizing community transmission and burden of COVID-19, provided their coverage level is high. The masks coverage needed to eliminate COVID-19 decreases if the masks-based intervention is combined with the strict social-distancing strategy.

title: Translational Knowledge Map of COVID-19
abstract: A translational knowledge map of COVID-19, based on the analysis of scientific papers and networks citation concurrence of terms and keywords of the terms: covid- 19, 2019-ncov and sars-cov-2 in leading databases (MEDLINE, web of Science and Scopus), was constructed. Some fields of the research on covid-19 are connected together, differing in structure, content and evolution.

title: Understanding Depression from Psycholinguistic Patterns in Social Media Texts
abstract: The World Health Organization reports that half of all mental illnesses begin by the age of 14. Most of these cases go undetected and untreated. The expanding use of social media has the potential to leverage the early identification of mental health diseases. As data gathered via social media are already digital, they have the ability to power up faster automatic analysis. In this article we evaluate the impact that psycholinguistic patterns can have on a standard machine learning approach for classifying depressed users based on their writings in an online public forum. We combine psycholinguistic features in a rule-based estimator and we evaluate their impact on this classification problem, along with three other standard classifiers. Our results on the Reddit Self-reported Depression Diagnosis dataset outperform some previously reported works on the same dataset. They stand for the importance of extracting psychologically motivated features when processing social media texts with the purpose of studying mental health.

title: A hypervariable region within the 3' cis-acting element of the murine coronavirus genome is nonessential for RNA synthesis but affects pathogenesis.
abstract: The 3' cis-acting element for mouse hepatitis virus (MHV) RNA synthesis resides entirely within the 301-nucleotide 3' untranslated region (3' UTR) of the viral genome and consists of three regions. Encompassing the upstream end of the 3' UTR are a bulged stem-loop and an overlapping RNA pseudoknot, both of which are essential to MHV and common to all group 2 coronaviruses. At the downstream end of the genome is the minimal signal for initiation of negative-strand RNA synthesis. Between these two ends is a hypervariable region (HVR) that is only poorly conserved between MHV and other group 2 coronaviruses. Paradoxically, buried within the HVR is an octanucleotide motif (oct), 5'-GGAAGAGC-3', which is almost universally conserved in coronaviruses and is therefore assumed to have a critical biological function. We conducted an extensive mutational analysis of the HVR. Surprisingly, this region tolerated numerous deletions, rearrangements, and point mutations. Most striking, a mutant deleted of the entire HVR was only minimally impaired in tissue culture relative to the wild type. By contrast, the HVR deletion mutant was highly attenuated in mice, causing no signs of clinical disease and minimal weight loss compared to wild-type virus. Correspondingly, replication of the HVR deletion mutant in the brains of mice was greatly reduced compared to that of the wild type. Our results show that neither the HVR nor oct is essential for the basic mechanism of MHV RNA synthesis in tissue culture. However, the HVR appears to play a significant role in viral pathogenesis.

title: The Pace and Pulse of the Fight against Coronavirus across the US, A Google Trends Approach
abstract: The coronavirus pandemic is impacting our lives at unprecedented speed and scale - including how we eat and work, what we worry about, how much we move, and our ability to earn. Google Trends can be used as a proxy for what people are thinking, needing, and planning. We use it to provide both insights into, and potential indicators of, important changes in information-seeking patterns during pandemics like COVID-19. Key questions we address are: (1) What is the relationship between the coronavirus outbreak and internet searches related to healthcare seeking, government support programs, media sources of different ideologies, planning around social activities, travel, and food, and new coronavirus-specific behaviors and concerns?; (2) How does the popularity of search terms differ across states and regions and can we explain these differences?; (3) Can we find distinct, tangible search patterns across states suggestive of policy gaps to inform pandemic response? (4) Does Google Trends data correlate with and potentially precede real-life events? We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions (NPIs) and recommend the development of a real-time dashboard as a decision-making tool. Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states during March 1st to April 15th, 2020; and Principal Component Analyses (PCA) to extract search patterns across states.

title: Epidemic characteristics of the COVID-19 outbreak in Tianjin, a well-developed city in China
abstract: Coronavirus disease 2019 (COVID-19) is already a pandemic. Few studies investigated the epidemic characteristics of the COVID-19 outbreak in the well-developed cities.  Epidemiological data of 136 confirmed COVID-19 cases were collected from the dataset of COVID-19 in Tianjin. All confirmed cases were categorized according to their potential infection sources. Daily numbers of confirmed cases of each category were plotted by date of onset, and the epidemic form of each category was inferred.  Among the 136 confirmed COVID-19 cases, 48 cases were categorized as imported cases and their close contacts, which were the majority of early cases. A total of 43 cases were found an epidemiological link to the Baodi department store, and they were inferred to be a common-source outbreak. Additionally, 35 cases were considered as familial clusters of COVID-19 cases, and 10 cases were sporadic. The 45 cases were inferred to be a propagated epidemic.  Local transmission of COVID-19 mainly occurred within families and a poorly ventilated public place in Tianjin. Besides the imported cases, the pattern of local transmission of COVID-19 was a mixture of the propagated epidemic and the common-source outbreak in Tianjin.

title: Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes
abstract: Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has significantly impacted on people's lives, it is essential to capture how people react to public health interventions and understand their concerns. In this paper, we aim to investigate people's reactions and concerns about COVID-19 in North America, especially focusing on Canada. We analyze COVID-19 related tweets using topic modeling and aspect-based sentiment analysis, and interpret the results with public health experts. We compare timeline of topics discussed with timing of implementation of public health interventions for COVID-19. We also examine people's sentiment about COVID-19 related issues. We discuss how the results can be helpful for public health agencies when designing a policy for new interventions. Our work shows how Natural Language Processing (NLP) techniques could be applied to public health questions with domain expert involvement.

title: Comparative Global Epidemiological Investigation of SARS-CoV-2 and SARS-CoV Diseases Using Meta-MUMS Tool Through Incidence, Mortality, and Recovery Rates
abstract: COVID-19 is a novel coronavirus that was reported by the world health organization in late December 2019. As an unexplained respiratory disease epidemic, which is similar to respiratory syndrome coronavirus SARS-CoV, it rapidly spread all over the world. The study aims to compare several parameters of COVID-19 and SARS-CoV infectious diseases in terms of incidence, mortality, and recovery rates. The publicly available dataset Worldometer (extracted on April 5, 2020) confirmed by WHO report was available for meta-analysis purposes using the Meta-MUMS tool. And, the reported outcomes of the analysis used a random-effects model to evaluate the event rate, and risk ratios thorough subgroup analysis forest plots. Seventeen countries for COVID-19 and eight countries of SARS infections, including COVID-19 group n = 1124243, and SARS-CoV group n = 8346, were analyzed. In this meta-analysis, a random effect model of relations of incidence, mortality, and recovery rates of COVID-19 and SARS world infections were determined. The meta-analysis and forest plots of two viral world infections showed that the incidence rate of COVID-19 infection is more than SARS infections, while recovery and mortality event rates of SARS-CoV are more than COVID-19 infection. And subgroup analysis showed that the mortality and recovery rates were higher in both SARS-CoV wand COVID-19 in comparison to incidence and mortality rates, respectively. In conclusion, the meta-analysis approach on the abovementioned dataset revealed the epidemiological and statistical analyses for comparing COVID-19 and SARS-CoV outbreaks.

title: SLEDGE: A Simple Yet Effective Baseline for Coronavirus Scientific Knowledge Search
abstract: With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of literature on the virus. Clinicians, researchers, and policy-makers need a way to effectively search these articles. In this work, we present a search system called SLEDGE, which utilizes SciBERT to effectively re-rank articles. We train the model on a general-domain answer ranking dataset, and transfer the relevance signals to SARS-CoV-2 for evaluation. We observe SLEDGE's effectiveness as a strong baseline on the TREC-COVID challenge (topping the learderboard with an nDCG@10 of 0.6844). Insights provided by a detailed analysis provide some potential future directions to explore, including the importance of filtering by date and the potential of neural methods that rely more heavily on count signals. We release the code to facilitate future work on this critical task at https://github.com/Georgetown-IR-Lab/covid-neural-ir

title: COVID-19 Mortality Risk Assessment: An International Multi-Center Study
abstract: Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients.  De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts.  The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation ([≤] 93%), elevated levels of C-reactive protein ([≥] 130 mg/L), blood urea nitrogen ([≥] 18 mg/dL), and blood creatinine ([≥] 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84-0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use.  The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.

title: Context-Guided Learning to Rank Entities
abstract: We propose a method for learning entity orders, for example, safety, popularity, and livability orders of countries. We train linear functions by using samples of ordered entities as training data, and attributes of entities as features. An example of such functions is f(Entity)  see text] (Police budget)  see text] (Crime rate), for ordering countries in terms of safety. As the size of training data is typically small in this task, we propose a machine learning method referred to as context-guided learning (CGL) to overcome the over-fitting problem. Exploiting a large amount of contexts regarding relations between the labeling criteria (e.g. safety) and attributes, CGL guides learning in the correct direction by estimating a roughly appropriate weight for each attribute by the contexts. This idea was implemented by a regularization approach similar to support vector machines. Experiments were conducted with 158 kinds of orders in three datasets. The experimental results showed high effectiveness of the contextual guidance over existing ranking methods.

title: Coronavirus Disease (COVID-19): A primer for emergency physicians
abstract: Rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has resulted in a global pandemic.  This review article provides emergency physicians with an overview of the most current understanding of COVID-19 and recommendations on the evaluation and management of patients with suspected COVID-19.  Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for causing COVID-19, is primarily transmitted from person-to-person through close contact (approximately 6 ft) by respiratory droplets. Symptoms of COVID-19 are similar to other viral upper respiratory illnesses. Three major trajectories include mild disease with upper respiratory symptoms, non-severe pneumonia, and severe pneumonia complicated by acute respiratory distress syndrome (ARDS). Emergency physicians should focus on identifying patients at risk, isolating suspected patients, and informing hospital infection prevention and public health authorities. Patients with suspected COVID-19 should be asked to wear a facemask. Respiratory etiquette, hand washing, and personal protective equipment are recommended for all healthcare personnel caring for suspected cases. Disposition depends on patient symptoms, hemodynamic status, and patient ability to self-quarantine.  This narrative review provides clinicians with an updated approach to the evaluation and management of patients presenting to the emergency department with suspected COVID-19.

title: Emerging Influenza and Psychosis: An Important Story in Psychological Medicine

title: COVID-19: A Survey on Public Medical Imaging Data Resources
abstract: This regularly updated survey provides an overview of public resources that offer medical images and metadata of COVID-19 cases. The purpose of this survey is to simplify the access to open COVID-19 image data resources for all scientists currently working on the coronavirus crisis.

title: Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsTM Analysis
abstract: To assess the association of public interest in coronavirus infections with the actual number of infected cases for selected countries across the globe.  We performed a Google TrendsTM search for “Coronavirus” and compared Relative Search Volumes (RSV) indices to the number of reported COVID-19 cases by the European Center for Disease Control (ECDC) using time-lag correlation analysis.  Worldwide public interest in Coronavirus reached its first peak end of January when numbers of newly infected patients started to increase exponentially in China. The worldwide Google TrendsTM index reached its peak on the 12th of March 2020 at a time when numbers of infected patients started to increase in Europe and COVID-19 was declared a pandemic. At this time the general interest in China but also the Republic of Korea has already been significantly decreased as compared to end of January. Correlations between RSV indices and number of new COVID-19 cases were observed across all investigated countries with highest correlations observed with a time lag of -11.5 days, i.e. highest interest in coronavirus observed 11.5 days before the peak of newly infected cases. This pattern was very consistent across European countries but also holds true for the US. In Brazil and Australia, highest correlations were observed with a time lag of -7 days. In Egypt the highest correlation is given with a time lag of 0, potentially indicating that in this country, numbers of newly infected patients will increase exponentially within the course of April.  Public interest indicated by RSV indices can help to monitor the progression of an outbreak such as the current COVID-19 pandemic. Public interest is on average highest 11.5 days before the peak of newly infected cases.

title: Influenza Vaccination and COVID19 Mortality in the USA
abstract: COVID-19 mortality rate is higher in the elderly and in those with preexisting chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infection, and thus annual influenza vaccination is recommended for them. In this study, we explore a possible area-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data until June 10, 2020 together with population health data for the United States at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the number of deaths from COVID-19 as the outcome variable. We adjusted for a wide array of potential confounding variables using both county-level generalized propensity scores for influenza vaccination rates, as well as direct adjustment. Our results suggest that influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19. This finding is robust to using different analysis periods, different thresholds for inclusion of counties, and a variety of methodologies for confounding adjustment. In conclusion, our results suggest a potential protective effect of the influenza vaccine on COVID-19 mortality in the elderly population. The significant public health implications of this possibility point to an urgent need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level, to investigate both the epidemiology and any underlying biological mechanism.

title: How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic
abstract: SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.

title: Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study
abstract: Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. Our research aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT, relieve working pressure of radiologists and contribute to the control of the epidemic.  For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University (Wuhan, Hubei province, China) were retrospectively collected and processed. Twenty-seven consecutive patients undergoing CT scans in Feb, 5, 2020 in Renmin Hospital of Wuhan University were prospectively collected to evaluate and compare the efficiency of radiologists against 2019-CoV pneumonia with that of the model.  The model achieved a per-patient sensitivity of 100%, specificity of 93.55%, accuracy of 95.24%, PPV of 84.62%, and NPV of 100%; a per-image sensitivity of 94.34%, specificity of 99.16%, accuracy of 98.85%, PPV of 88.37%, and NPV of 99.61% in retrospective dataset. For 27 prospective patients, the model achieved a comparable performance to that of expert radiologist. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%.  The deep learning model showed a comparable performance with expert radiologist, and greatly improve the efficiency of radiologists in clinical practice. It holds great potential to relieve the pressure of frontline radiologists, improve early diagnosis, isolation and treatment, and thus contribute to the control of the epidemic.

title: The ocular surface, coronaviruses and COVID-19
abstract: The ocular surface has been suggested as a site of infection with Coronavirus-2 (SARS-CoV-2) responsible for the coronavirus disease-19 (COVID-19). This review examines the evidence for this hypothesis, and its implications for clinical practice. Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces. SARS-CoV-2 binds to angiotensin converting enzyme-2 (ACE2) to facilitate infection in humans. This review sets out to evaluate evidence for the ocular surface as a route of infection. A literature search in this area was conducted on 15 April 2020 using the Scopus database. In total, 287 results were returned and reviewed. There is preliminary evidence for ACE2 expression on corneal and conjunctival cells, but most of the other receptors to which coronaviruses bind appear to be found under epithelia of the ocular surface. Evidence from animal studies is limited, with a single study suggesting viral particles on the eye can travel to the lung, resulting in very mild infection. Coronavirus infection is rarely associated with conjunctivitis, with occasional cases reported in patients with confirmed COVID-19, along with isolated cases of conjunctivitis as a presenting sign. Coronaviruses have been rarely isolated from tears or conjunctival swabs. The evidence suggests coronaviruses are unlikely to bind to ocular surface cells to initiate infection. Additionally, hypotheses that the virus could travel from the nasopharynx or through the conjunctival capillaries to the ocular surface during infection are probably incorrect. Conjunctivitis and isolation of the virus from the ocular surface occur only rarely, and overwhelmingly in patients with confirmed COVID-19. Necessary precautions to prevent person-to-person transmission should be employed in clinical practice throughout the pandemic, and patients should be reminded to maintain good hygiene practices.

title: Identifying Radiological Findings Related to COVID-19 from Medical Literature
abstract: Coronavirus disease 2019 (COVID-19) has infected more than one million individuals all over the world and caused more than 55,000 deaths, as of April 3 in 2020. Radiological findings are important sources of information in guiding the diagnosis and treatment of COVID-19. However, the existing studies on how radiological findings are correlated with COVID-19 are conducted separately by different hospitals, which may be inconsistent or even conflicting due to population bias. To address this problem, we develop natural language processing methods to analyze a large collection of COVID-19 literature containing study reports from hospitals all over the world, reconcile these results, and draw unbiased and universally-sensible conclusions about the correlation between radiological findings and COVID-19. We apply our method to the CORD-19 dataset and successfully extract a set of radiological findings that are closely tied to COVID-19.

title: Spatial transmission of COVID-19 via public and private transportation in China

title: Mechanisms and pathways to impact in public health research: a preliminary analysis of research funded by the National Institute for Health Research (NIHR)
abstract: The mechanisms and pathways to impacts from public health research in the UK have not been widely studied. Through the lens of one funder (NIHR), our aims are to map the diversity of public health research, in terms of funding mechanisms, disciplinary contributions, and public health impacts, identify examples of impacts, and pathways to impact that existing reporting mechanisms may not otherwise have captured, and provide illustrations of how public health researchers perceive the generation of non-academic impact from their work.  A total of 1386 projects were identified as ‘public health research’ by the NIHR and listed in the NIHR Public Health Overview database (2000–2016). From these, a subset of 857 projects were matched as potentially having begun reporting impacts via an external data-gathering platform (Researchfish). Data on the 857 projects were analyzed quantitatively, and nine projects were selected to investigate further through semi-structured interviews with principal investigators. Two workshops took place to validate emerging and final findings and facilitate analysis.  In addition to the NIHR School for Public Health Research and the NIHR Public Health Research Programme, 89% of projects contained in the NIHR Public Health Overview portfolio as ‘public health research’ are funded via other NIHR research programmes, suggesting significant diversity in disciplines contributing to public health research and outcomes. The pathways to impact observed in our in-depth case studies include contributing to debates on what constitutes appropriate evidence for national policy change, acknowledging local ‘unintended’ impacts, building trusted relationships with stakeholders across health and non-health sectors and actors, collaborating with local authorities, and using non-academic dissemination channels.  Public health as a discipline contributes substantially to impact beyond academia. To support the diversity of these impacts, we need to recognise localized smaller-scale impacts, and the difference in types of evidence required for community and local authority-based impacts. This will also require building capacity and resources to enable impact to take place from public health research. Finally, support is required for engagement with local authorities and working with non-health sectors that contribute to health outcomes.

title: The misleading illusion of COVID-19 confirmed case data: alternative estimates and a monitoring tool
abstract: Confirmed Case Data have been widely cited during the current COVID-19 pandemic as an estimate of the spread of the SARS-CoV-2 virus. However, their central role in media, official reports and decision-making may be undeserved and misleading. Previously published Infection Fatality Rates were weighted by age structure in the 50 countries with more reported deaths to obtain country-specific rates. For each country, the number of infections up to the Infection Date (23 days ago = Incubation Period + Onset to Death period) and the present percentage of immune population were estimated using Infection Fatality Rate, the number of reported deaths (which is less prone to undersampling), and projecting back to Infection Date. We then estimated a Detection Index for each country as the percentage of estimated infections that confirmed cases represent. Assuming that detection remains constant after Infection Date, we estimated the number of deaths and the estimated percentage of the population of each country expected to be immune up to 23 days into the future. Estimated Infection Fatality Rates are higher in Europe. In most countries, confirmed cases currently represent less than 30% of estimated infections on Infection Date, and this value decreases with time. Countries with flat curves throughout the pandemic show the lowest immunity percentages and these values seem unlikely to change in the near future, suggesting that they remain vulnerable to new outbreaks. Estimates for some countries with low Infection Fatality Rates suggest a still steep increase in the number of casualties in the next three weeks. Countries that did not control initial outbreaks seem to have reached higher immunity percentages, although mostly still under 5%. We provide the code to monitor the trajectories of these estimates in 178 countries throughout the COVID-19 pandemic.

title: Consideration of the Aerosol Transmission for COVID-19 and Public Health
abstract: This article analyzes the available evidence to address airborne, aerosol transmission of the SARS-CoV-2. We review and present three lines of evidence: case reports of transmission for asymptomatic individuals in association with studies that show that normal breathing and talking produce predominantly small droplets of the size that are subject to aerosol transport; limited empirical data that have recorded aerosolized SARS-CoV-2 particles that remain suspended in the air for hours and are subject to transport over distances including outside of rooms and intrabuilding, and the broader literature that further supports the importance of aerosol transmission of infectious diseases. The weight of the available evidence warrants immediate attention to address the significance of aerosols and implications for public health protection.

title: Common Pitfalls in the Interpretation of COVID-19 Data and Statistics
abstract: Policymakers, experts and the general public heavily rely on the data that are being reported in the context of the coronavirus pandemic. Daily data releases on confirmed COVID-19 cases and deaths provide information on the course of the pandemic.

title: A review of initial data on pregnancy during the COVID-19 outbreak: implications for assisted reproductive treatments
abstract: The current outbreak of the novel 2019 coronavirus disease (COVID-19) started in China in December 2019 and has since spread to several other countries. On March 25, 2020, a total of 375,498 cases had been confirmed globally with 2,201 cases in Brazil, showing the urgency of reacting to this international public health emergency. While in most cases, mild symptoms are observed, in some cases the infection leads to serious pulmonary disease. As a result, the possible consequences of the COVID-19 outbreak for pregnant women and its potential effects on the management of assisted reproductive treatments, demand attention. In this review, we summarize the latest research progress related to COVID-19 epidemiology and the reported data of pregnant women, and discuss the current evidence of COVID-19 infections during pregnancy and its potential consequences for assisted reproductive treatments. Reported data suggest that symptoms in pregnant women are similar to those in other people, and that there is no evidence for higher maternal or fetal risks. However, considering the initial data and lack of comprehensive knowledge on the pathogenesis of SARS-CoV-2 during pregnancy, human reproduction societies have recommended postponing the embryo transfers and do not initiate new treatment cycles. New evidence must be considered carefully in order to adjust these recommendations accordingly at any time and to guide assisted reproductive treatments.

title: Genomic variance of the 2019‐nCoV coronavirus
abstract: There is a rising global concern for the recently emerged novel coronavirus (2019‐nCoV). Full genomic sequences have been released by the worldwide scientific community in the last few weeks to understand the evolutionary origin and molecular characteristics of this virus. Taking advantage of all the genomic information currently available, we constructed a phylogenetic tree including also representatives of other coronaviridae, such as Bat coronavirus (BCoV) and severe acute respiratory syndrome. We confirm high sequence similarity (>99%) between all sequenced 2019‐nCoVs genomes available, with the closest BCoV sequence sharing 96.2% sequence identity, confirming the notion of a zoonotic origin of 2019‐nCoV. Despite the low heterogeneity of the 2019‐nCoV genomes, we could identify at least two hypervariable genomic hotspots, one of which is responsible for a Serine/Leucine variation in the viral ORF8‐encoded protein. Finally, we perform a full proteomic comparison with other coronaviridae, identifying key aminoacidic differences to be considered for antiviral strategies deriving from previous anti‐coronavirus approaches.

title: Political Polarization Drives Online Conversations About COVID‐19 in the United States
abstract: Since the outbreak in China in late 2019, the novel coronavirus (COVID‐19) has spread around the world and has come to dominate online conversations. By linking 2.3 million Twitter users to locations within the United States, we study in aggregate how political characteristics of the locations affect the evolution of online discussions about COVID‐19. We show that COVID‐19 chatter in the US is largely shaped by political polarization. Partisanship correlates with sentiment toward government measures and the tendency to share health and prevention messaging. Cross‐ideological interactions are modulated by user segregation and polarized network structure. We also observe a correlation between user engagement with topics related to public health and the varying impact of the disease outbreak in different US states. These findings may help inform policies both online and offline. Decision‐makers may calibrate their use of online platforms to measure the effectiveness of public health campaigns, and to monitor the reception of national and state‐level policies, by tracking in real‐time discussions in a highly polarized social media ecosystem.

title: COVID-19, SARS and MERS: are they closely related?
abstract: The 2019 novel coronavirus (SARS-CoV-2) is a new human coronavirus which is spreading with epidemic features in China and other Asian countries; cases have also been reported worldwide. This novel coronavirus disease (COVID-19) is associated with a respiratory illness that may lead to severe pneumonia and acute respiratory distress syndrome (ARDS). Although related to the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS), COVID-19 shows some peculiar pathogenetic, epidemiological and clinical features which to date are not completely understood.  To provide a review of the differences in pathogenesis, epidemiology and clinical features of COVID-19, SARS and MERS.  The most recent literature in the English language regarding COVID-19 has been reviewed, and extracted data have been compared with the current scientific evidence about SARS and MERS epidemics. Content COVID-19 seems not to be very different from SARS regarding its clinical features. However, it has a fatality rate of 2.3%, lower than that of SARS (9.5%) and much lower than that of MERS (34.4%). The possibility cannot be excluded that because of the less severe clinical picture of COVID-19 it can spread in the community more easily than MERS and SARS. The actual basic reproductive number (R0) of COVID-19 (2.0–2.5) is still controversial. It is probably slightly higher than the R0 of SARS (1.7–1.9) and higher than that of MERS (<1). A gastrointestinal route of transmission for SARS-CoV-2, which has been assumed for SARS-CoV and MERS-CoV, cannot be ruled out and needs further investigation.  There is still much more to know about COVID-19, especially as concerns mortality and its capacity to spread on a pandemic level. Nonetheless, all of the lessons we learned in the past from the SARS and MERS epidemics are the best cultural weapons with which to face this new global threat.

title: Investigating the cases of novel coronavirus disease (COVID-19) in China using dynamic statistical techniques
abstract: The initial investigation by local hospital attributed the outbreak of the novel coronavirus disease (COVID-19) to pneumonia with unknown cause that appeared like the 2003 severe acute respiratory syndrome (SARS). The World Health Organization declared COVID-19 as public health emergency after it spread outside China to several countries. Thus, an assessment of the novel coronavirus disease (COVID-19) with novel estimation approaches is essential to the global debate. This study is the first to develop both time series and panel data models to construct conceptual tools that examine the nexus between death from COVID-19 and confirmed cases. We collected daily data on four health indicators namely deaths, confirmed cases, suspected cases, and recovered cases across 31 Provinces/States in China. Due to the complexities of the COVID-19, we investigated the unobserved factors including environmental exposures accounting for the spread of the disease through human-to-human transmission. We used estimation methods capable of controlling for cross-sectional dependence, endogeneity, and unobserved heterogeneity. We predicted the impulse-response between confirmed cases of COVID-19 and COVID-19-attributable deaths. Our study revealed that the effect of confirmed cases on the novel coronavirus attributable deaths is heterogeneous across Provinces/States in China. We found a linear relationship between COVID-19 attributable deaths and confirmed cases whereas a nonlinear relationship was confirmed for the nexus between recovery cases and confirmed cases. The empirical evidence revealed that an increase in confirmed cases by 1% increases coronavirus attributable deaths by ~0.10%–~1.71% (95% CI). Our empirical results confirmed the presence of unobserved heterogeneity and common factors that facilitates the novel coronavirus attributable deaths caused by increased levels of confirmed cases. Yet, the role of such a medium that facilitates the transmission of COVID-19 remains unclear. We highlight safety precaution and preventive measures to circumvent the human-to-human transmission.

title: Using informatics to guide public health policy during the COVID-19 pandemic in the USA
abstract: Current and future pandemics will require informatics solutions to assess the risks, resources and policies to guide better public health decision-making.  Cross-sectional study of all COVID-19 cases and deaths in the USA on a population- and resource-adjusted basis (as of 24 April 2020) by applying biomedical informatics and data visualization tools to several public and federal government datasets, including analysis of the impact of statewide stay-at-home orders.  There were 2753.2 cases and 158.0 deaths per million residents, respectively, in the USA with variable distributions throughout divisions, regions and states. Forty-two states and Washington, DC, (84.3%) had statewide stay-at-home orders, with the remaining states having population-adjusted characteristics in the highest risk quartile.  Effective national preparedness requires clearly understanding states' ability to predict, manage and balance public health needs through all stages of a pandemic. This will require leveraging data quickly, correctly and responsibly into sound public health policies.

title: Measuring Human and Economic Activity from Satellite Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic
abstract: The COVID-19 outbreak forced governments worldwide to impose lockdowns and quarantines over their population to prevent virus transmission. As a consequence, there are disruptions in human and economic activities all over the globe. The recovery process is also expected to be rough. Economic activities impact social behaviors, which leave signatures in satellite images that can be automatically detected and classified. Satellite imagery can support the decision-making of analysts and policymakers by providing a different kind of visibility into the unfolding economic changes. Such information can be useful both during the crisis and also as we recover from it. In this work, we use a deep learning approach that combines strategic location sampling and an ensemble of lightweight convolutional neural networks (CNNs) to recognize specific elements in satellite images and compute economic indicators based on it, automatically. This CNN ensemble framework ranked third place in the US Department of Defense xView challenge, the most advanced benchmark for object detection in satellite images. We show the potential of our framework for temporal analysis using the US IARPA Function Map of the World (fMoW) dataset. We also show results on real examples of different sites before and after the COVID-19 outbreak to demonstrate possibilities. Among the future work is the possibility that with a satellite image dataset that samples a region at a weekly (or biweekly) frequency, we can generate more informative temporal signatures that can predict future economic states. Our code is being made available at https://github.com/maups/covid19-satellite-analysis

title: Discovering associations in COVID-19 related research papers
abstract: A COVID-19 pandemic has already proven itself to be a global challenge. It proves how vulnerable humanity can be. It has also mobilized researchers from different sciences and different countries in the search for a way to fight this potentially fatal disease. In line with this, our study analyses the abstracts of papers related to COVID-19 and coronavirus-related-research using association rule text mining in order to find the most interestingness words, on the one hand, and relationships between them on the other. Then, a method, called information cartography, was applied for extracting structured knowledge from a huge amount of association rules. On the basis of these methods, the purpose of our study was to show how researchers have responded in similar epidemic/pandemic situations throughout history.

title: Contextualizing covid-19 spread: a county level analysis, urban versus rural, and implications for preparing for the next wave
abstract: Paraphrasing [Morano and Holt, 2017], contextual determinants of health including social, environmental, healthcare and others, are a so-called deck of cards one is dealt. The ability to modify health outcomes varies then based upon how one's hand is played. It is thus of great interest to understand how these determinants associate with the emerging pandemic covid-19. To this end, we conducted a deep-dive analysis into this problem using a recently curated public dataset on covid-19 that connects infection spread over time to a rich collection of contextual determinants for all counties of the U.S and Washington, D.C. Using random forest machine learning methodology, we identified a relevant constellation of contextual factors of disease spread which manifest differently for urban and rural counties. The findings also have clear implications for better preparing for the next wave of disease.

title: Open access epidemiological data from the COVID-19 outbreak

title: CAN-NPI: A Curated Open Dataset of Canadian Non-Pharmaceutical Interventions in Response to the Global COVID-19 Pandemic
abstract: Non-pharmaceutical interventions (NPIs) have been the primary tool used by governments and organizations to mitigate the spread of the ongoing pandemic of COVID-19. Natural experiments are currently being conducted on the impact of these interventions, but most of these occur at the subnational level - data not available in early global datasets. We describe the rapid development of the first comprehensive, labelled dataset of 1640 NPIs implemented at federal, provincial/territorial and municipal levels in Canada to guide COVID-19 research. For each intervention, we provide: a) information on timing to aid in longitudinal evaluation, b) location to allow for robust spatial analyses, and c) classification based on intervention type and target population, including classification aligned with a previously developed measure of government response stringency. This initial dataset release (v1.0) spans January 1st, and March 31st, 2020; bi-weekly data updates to continue for the duration of the pandemic. This novel dataset enables robust, inter-jurisdictional comparisons of pandemic response, can serve as a model for other jurisdictions and can be linked with other information about case counts, transmission dynamics, health care utilization, mobility data and economic indicators to derive important insights regarding NPI impact.

title: CORONAVIRUSES (CORONAVIRIDAE)

title: Anosmia and ageusia are emerging as symptoms in patients with COVID-19: What does the current evidence say?
abstract: There have been several reports noting anosmia and ageusia as possible symptoms of COVID-19. This is of particular interest in oncology since patients receiving some cancer treatments such as chemotherapy or immune therapy often experience similar symptoms as side-effects. The purpose of this report was to summarise the evidence on the existence of anosmia and ageusia an emerging COVID-19 symptoms in order to better inform both oncology patients and clinicians. Currently, there is no published evidence or case reports noting anosmia or ageusia as symptoms of COVID-19. Nevertheless, experts in rhinology have suggested that the onset of such symptoms could either act as a trigger for testing for the disease where possible, or could be a new criterion to self-isolate. Whilst more data is currently needed to strengthen our knowledge of the symptoms of COVID-19, oncology patients who are concerned about anosmia or ageusia in the context of their systemic anti-cancer therapy should contact their acute oncology support line for advice.

title: Novel coronavirus 2019-nCoV: prevalence, biological and clinical characteristics comparison with SARS-CoV and MERS-CoV.
abstract: Human infections with zoonotic coronavirus contain emerging and reemerging pathogenic characteristics which have raised great public health concern. This study aimed at investigating the global prevalence, biological and clinical characteristics of novel coronavirus, Wuhan China (2019-nCoV), Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection outbreaks.  The data on the global outbreak of "2019-nCoV, SARS-CoV, and MERS-CoV" were obtained from World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), concerned ministries and research institutes. We also recorded the information from research documents published in global scientific journals indexed in ISI Web of Science and research centers on the prevalence, biological and clinical characteristics of 2019-nCoV, SARS-CoV, and MERS-CoV.  Worldwide, SARS-CoV involved 32 countries, with 8422 confirmed cases and 916 (10.87%) casualties from November 2002 to August 2003. MERS-CoV spread over 27 states, causing 2496 cases and 868 (34.77%) fatalities during the period April 2012 to December 2019. However, the novel coronavirus 2019-nCoV spread swiftly the global borders of 27 countries. It infected 34799 people and resulted in 724 (2.08%) casualties during the period December 29, 2019 to February 7, 2020. The fatality rate of coronavirus MERS-CoV was (34.77%) higher than SARS-CoV (10.87%) and 2019-nCoV (2.08%); however, the 2019-nCoV transmitted rapidly in comparison to SARS-CoV and MERS-CoV.  The novel coronavirus 2019-nCoV has diverse epidemiological and biological characteristics, making it more contagious than SARS-CoV and MERS-CoV. It has affected more people in a short time period compared to SARS-CoV and MERS-CoV, although the fatality rate of MERS-CoV was higher than SARS-CoV and 2019-nCoV. The major clinical manifestations in coronavirus infections 2019-nCoV, MERS-CoV, and SARS CoV are fever, chills, cough, shortness of breath, generalized myalgia, malaise, drowsy, diarrhea, confusion, dyspnea, and pneumonia. Global health authorities should take immediate measures to prevent the outbreaks of such emerging and reemerging pathogens across the globe to minimize the disease burden locally and globally.

title: A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow
abstract: The Covid-19 outbreak, beyond its tragic effects, has changed to an unprecedented extent almost every aspect of human activity throughout the world. At the same time, the pandemic has stimulated enormous amount of research by scientists across various disciplines, seeking to study the phenomenon itself, its epidemiological characteristics and ways to confront its consequences. Information Technology, and particularly Data Science, drive innovation in all related to Covid-19 biomedical fields. Acknowledging that software developers routinely resort to open question and answer communities like Stack Overflow to seek advice on solving technical issues, we have performed an empirical study to investigate the extent, evolution and characteristics of Covid-19 related posts. In particular, through the study of 464 Stack Overflow questions posted mainly in February and March 2020 and leveraging the power of text mining, we attempt to shed light into the interest of developers in Covid-19 related topics and the most popular technological problems for which the users seek information. The findings reveal that indeed this global crisis sparked off an intense and increasing activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data, primarily using Python technologies.

title: Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images
abstract: A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images.  We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19.  The experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019.  The established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

title: Reimagining the Administrative State in Times of Global Health Crisis: An Anatomy of Taiwan’s Regulatory Actions in Response to the COVID-19 Pandemic

title: Major Infection Events Over 5 Years: How Is Media Coverage Influencing Online Information Needs of Health Care Professionals and the Public?
abstract: The last decade witnessed turbulent events in public health. Emerging infections, increase of antimicrobial resistance, deliberately released threats and ongoing battles with common illnesses were amplified by the spread of disease through increased international travel. The Internet has dramatically changed the availability of information about outbreaks; however, little research has been done in comparing the online behavior of public and professionals around the same events and the effect of media coverage of outbreaks on information needs.  To investigate professional and public online information needs around major infection outbreaks and correlate these with media coverage. Questions include (1) How do health care professionals’ online needs for public health and infection control information differ from those of the public?, (2) Does dramatic media coverage of outbreaks contribute to the information needs among the public?, and (3) How do incidents of diseases and major policy events relate to the information needs of professionals?  We used three longitudinal time-based datasets from mid-2006 until end of 2010: (1) a unique record of professional online behavior on UK infection portals: National electronic Library of Infection and National Resource of Infection Control (NeLI/NRIC), (2) equivalent public online information needs (Google Trends), and (3) relevant media coverage (LexisNexis). Analysis of NeLI/NRIC logs identified the highest interest around six major infectious diseases: Clostridium difficile (C difficile)/Methicillin-resistant Staphylococcus aureus (MRSA), tuberculosis, meningitis, norovirus, and influenza. After pre-processing, the datasets were analyzed and triangulated with each other.  Public information needs were more static, following the actual disease occurrence less than those of professionals, whose needs increase with public health events (eg, MRSA/C difficile) and the release of major national policies or important documents. Media coverage of events resulted in major public interest (eg, the 2007/2008 UK outbreak of C difficile/MRSA). An exception was norovirus, showing a seasonal pattern for both public and professionals, which matched the periodic disease occurrence. Meningitis was a clear example of a disease with heightened media coverage tending to focus on individual and celebrity cases. Influenza was a major concern during the 2009 H1N1 outbreak creating massive public interest in line with the spring and autumn peaks in cases; although in autumn 2009, there was no corresponding increase in media coverage. Online resources play an increasing role in fulfilling professionals’ and public information needs.  Significant factors related to a surge of professional interest around a disease were typically key publications and major policy changes. Public interests seem more static and correlate with media influence but to a lesser extent than expected. The only exception was norovirus, exhibiting online public and professional interest correlating with seasonal occurrences of the disease. Public health agencies with responsibility for risk communication of public health events, in particular during outbreaks and emergencies, need to collaborate with media in order to ensure the coverage is high quality and evidence-based, while professionals’ information needs remain mainly fulfilled by online open access to key resources.

title: COVID-19 in Italy: momentous decisions and many uncertainties

title: Empowering Virus Sequences Research through Conceptual Modeling
abstract: The pandemic outbreak of the coronavirus disease has attracted attention towards the genetic mechanisms of viruses. We hereby present the Viral Conceptual Model (VCM), centered on the virus sequence and described from four perspectives: biological (virus type and hosts/sample), analytical (annotations and variants), organizational (sequencing project) and technical (experimental technology). VCM is inspired by GCM, our previously developed Genomic Conceptual Model, but it introduces many novel concepts, as viral sequences significantly differ from human genomes. When applied to SARS-CoV2 virus, complex conceptual queries upon VCM are able to replicate the search results of recent articles, hence demonstrating huge potential in supporting virology research. In addition to VCM, we also illustrate the data dictionary for patient’s phenotype used by the COVID-19 Host Genetic Initiative. Our effort is part of a broad vision: availability of conceptual models for both human genomics and viruses will provide important opportunities for research, especially if interconnected by the same human being, playing the role of virus host as well as provider of genomic and phenotype information.

title: CovidNet: To Bring Data Transparency in the Era of COVID-19
abstract: Timely, creditable, and fine-granular case information is vital for local communities and individual citizens to make rational and data-driven responses to the COVID-19 pandemic. This paper presents CovidNet, a COVID-19 tracking project associated with a large scale epidemic dataset, which was initiated by 1Point3Acres. To the best of our knowledge, the project is the only platform providing real-time global case information of more than 4,124 sub-divisions from over 27 countries worldwide with multi-language supports. The platform also offers interactive visualization tools to analyze the full historical case curves in each region. Initially launched as a voluntary project to bridge the data transparency gap in North America in January 2020, this project by far has become one of the major independent sources worldwide and has been consumed by many other tracking platforms. The accuracy and freshness of the dataset is a result of the painstaking efforts from our voluntary teamwork, crowd-sourcing channels, and automated data pipelines. As of May 18, 2020, the project website has been visited more than 200 million times and the CovidNet dataset has empowered over 522 institutions and organizations worldwide in policy-making and academic researches. All datasets are openly accessible for non-commercial purposes at https://coronavirus.1point3acres.com via a formal request through our APIs.

title: A Meta-Analysis of Multiple Whole Blood Gene Expression Data Unveils a Diagnostic Host-Response Transcript Signature for Respiratory Syncytial Virus
abstract: Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infection worldwide. The absence of a commercial vaccine and the limited success of current therapeutic strategies against RSV make further research necessary. We used a multi-cohort analysis approach to investigate host transcriptomic biomarkers and shed further light on the molecular mechanism underlying RSV-host interactions. We meta-analyzed seven transcriptome microarray studies from the public Gene Expression Omnibus (GEO) repository containing a total of 922 samples, including RSV, healthy controls, coronaviruses, enteroviruses, influenzas, rhinoviruses, and coinfections, from both adult and pediatric patients. We identified > 1500 genes differentially expressed when comparing the transcriptomes of RSV-infected patients against healthy controls. Functional enrichment analysis showed several pathways significantly altered, including immunologic response mediated by RSV infection, pattern recognition receptors, cell cycle, and olfactory signaling. In addition, we identified a minimal 17-transcript host signature specific for RSV infection by comparing transcriptomic profiles against other respiratory viruses. These multi-genic signatures might help to investigate future drug targets against RSV infection.

title: Ideas for how informaticians can get involved with COVID-19 research
abstract: The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on population health and wellbeing. Biomedical informatics is central to COVID-19 research efforts and for the delivery of healthcare for COVID-19 patients. Critical to this effort is the participation of informaticians who typically work on other basic science or clinical problems. The goal of this editorial is to highlight some examples of COVID-19 research areas that could benefit from informatics expertise. Each research idea summarizes the COVID-19 application area, followed by an informatics methodology, approach, or technology that could make a contribution. It is our hope that this piece will motivate and make it easy for some informaticians to adopt COVID-19 research projects.

title: Trends and prediction in daily incidence and deaths of COVID-19 in the United States: a search-interest based model
abstract: The coronavirus disease 2019 (COVID-19) infected more than 586,000 patients in the U.S. However, its daily incidence and deaths in the U.S. are poorly understood. Internet search interest was found correlated with COVID-19 daily incidence in China, but not yet applied to the U.S. Therefore, we examined the association of internet search-interest with COVID-19 daily incidence and deaths in the U.S.  We extracted the COVDI-19 daily incidence and death data in the U.S. from two population-based datasets. The search interest of COVID-19 related terms was obtained using Google Trends. Pearson correlation test and general linear model were used to examine correlations and predict future trends, respectively.  There were 555,245 new cases and 22,019 deaths of COVID-19 reported in the U.S. from March 1 to April 12, 2020. The search interest of COVID, “COVID pneumonia,” and “COVID heart” were correlated with COVDI-19 daily incidence with ~12-day of delay (Pearson’s r=0.978, 0.978 and 0.979, respectively) and deaths with 19-day of delay (Pearson’s r=0.963, 0.958 and 0.970, respectively). The COVID-19 daily incidence and deaths appeared to both peak on April 10. The 4-day follow-up with prospectively collected data showed moderate to good accuracies for predicting new cases (Pearson’s r=−0.641 to −0.833) and poor to good accuracies for daily new deaths (Pearson’s r=0.365 to 0.935).  Search terms related to COVID-19 are highly correlated with the trends in COVID-19 daily incidence and deaths in the U.S. The prediction-models based on the search interest trend reached moderate to good accuracies.

title: Discussing the ABCs of Health Security-Antibiotic Resistance, Biothreats, and Coronavirus.
abstract: According to the World Health Organization (WHO), viral diseases continue to emerge and represent a serious issue to public health In the last twenty years, several viral epidemics such as the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 to 2003, and H1N1 influenza in 2009, have been recorded Most recently, the Middle East respiratory syndrome coronavirus (MERS-CoV) was first identified in Saudi Arabia in 2012 In a timeline that reaches the present day, an epidemic of cases with unexplained low respiratory infections detected in Wuhan, the largest metropolitan area in China's Hubei province, was first reported to the WHO Country Office in China, on December 31, 2019 Published literature can trace the beginning of symptomatic individuals back to the beginning of December 2019 As they were unable to identify the causative agent, these first cases were classified as "pneumonia of unknown etiology " The Chinese Center for Disease Control and Prevention (CDC) and local CDCs organized an intensive outbreak investigation program The etiology of this illness is now attributed to a novel virus belonging to the coronavirus (CoV) family, COVID-19 On February 11, 2020, the WHO Director-General, Dr Tedros Adhanom Ghebreyesus, announced that the disease caused by this new CoV was a "COVID-19," which is the acronym of "coronavirus disease 2019" In the past twenty years, two additional coronavirus epidemics have occurred SARS-CoV provoked a large-scale epidemic beginning in China and involving two dozen countries with approximately 8000 cases and 800 deaths, and the MERS-CoV that began in Saudi Arabia and has approximately 2,500 cases and 800 deaths and still causes as sporadic cases This new virus seems to be very contagious and has quickly spread globally In a meeting on January 30, 2020, per the International Health Regulations (IHR, 2005), the outbreak was declared by the WHO a Public Health Emergency of International Concern (PHEIC) as it had spread to 18 countries with four countries reporting human-to-human transmission An additional landmark occurred on February 26, 2020, as the first case of the disease, not imported from China, was recorded in the United States Initially, the new virus was called 2019-nCoV Subsequently, the task of experts of the International Committee on Taxonomy of Viruses (ICTV) termed it the SARS-CoV-2 virus as it is very similar to the one that caused the SARS outbreak (SARS-CoVs) The CoVs have become the major pathogens of emerging respiratory disease outbreaks They are a large family of single-stranded RNA viruses (+ssRNA) that can be isolated in different animal species For reasons yet to be explained, these viruses can cross species barriers and can cause, in humans, illness ranging from the common cold to more severe diseases such as MERS and SARS Interestingly, these latter viruses have probably originated from bats and then moving into other mammalian hosts — the Himalayan palm civet for SARS-CoV, and the dromedary camel for MERS-CoV — before jumping to humans The dynamics of SARS-Cov-2 are currently unknown, but there is speculation that it also has an animal origin The potential for these viruses to grow to become a pandemic worldwide seems to be a serious public health risk Concerning COVID-19, the WHO raised the threat to the CoV epidemic to the "very high" level, on February 28, 2020 Probably, the effects of the epidemic caused by the new CoV has yet to emerge as the situation is quickly evolving World governments are at work to establish countermeasures to stem possible devastating effects Health organizations coordinate information flows and issues directives and guidelines to best mitigate the impact of the threat At the same time, scientists around the world work tirelessly, and information about the transmission mechanisms, the clinical spectrum of disease, new diagnostics, and prevention and therapeutic strategies are rapidly developing Many uncertainties remain with regard to both the virus-host interaction and the evolution of the epidemic, with specific reference to the times when the epidemic will reach its peak At the moment, the therapeutic strategies to deal with the infection are only supportive, and prevention aimed at reducing transmission in the community is our best weapon Aggressive isolation measures in China have led to a progressive reduction of cases in the last few days In Italy, in geographic regions of the north of the peninsula, political and health authorities are making incredible efforts to contain a shock wave that is severely testing the health system In the midst of the crisis, the authors have chosen to use the "Statpearls" platform because, within the PubMed scenario, it represents a unique tool that may allow them to make updates in real-time The aim, therefore, is to collect information and scientific evidence and to provide an overview of the topic that will be continuously updated