bluuebunny/crossref_metadata_2025 huggingface dataset:
Rejection of the name Borreliella and all proposed species comb. nov. placed therein
Authors: Margos Gabriele , Castillo-Ramirez Santiago , Cutler Sally , Dessau Ram B. , Eikeland Randi , Estrada-Peña Agustin , Gofton Alexander , Graña-Miraglia Lucía , Hunfeld Klaus-Peter , Krause Andreas , Lienhard Reto , Lindgren Per-Eric , Oskam Charlotte , Rudolf Ivo , Schwartz Ira , Sing Andreas , Stevenson Brian , Wormser Gary P. , Fingerle Volker
Published: April 2020

Rejection (nomen rejiciendum) of the name

Borreliella

and all new combinations therein is being requested on grounds of risk to human health and patient safety (Principle 1, subprinciple 2 and Rule 56a) and violation to aim for stability of names, to avoid useless creation of names (Principle 1, subprinciple 1 and 3) and that names should not be changed without sufficient reason (Principle 9 of the International Code of Nomenclature of Prokaryotes).
Logic in the time of coronavirus
Authors: Inglis Timothy J. J.
Published: April 2020

Much has happened here since the local news media trumpeted the first Australian COVID-19 fatality, and stirred up a medieval fear of contagion. We now need to take a step back to examine the logic underlying the use of our limited COVID-19 countermeasures. Emerging infectious diseases by their nature, pose new challenges to the diagnostic-treatment-control nexus, and push our concepts of causality beyond the limits of the conventional Koch-Henle approach to aetiology. We need to use contemporary methods of assessing causality to ensure that clinical, laboratory and public health measures draw on a rational, evidence-based approach to argumentation. The purpose of any aetiological hypothesis is to derive actionable insights into this latest emerging infectious disease. This review is an introduction to a conversation with medical microbiologists, which will be supported by a moderated blog.
Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis
Authors: Revilla Lluís , Mayorgas Aida , Corraliza Ana Maria , Masamunt Maria C. , Metwaly Amira , Haller Dirk , Tristán Eva , Carrasco Anna , Esteve Maria , Panés Julian , Ricart Elena , Lozano Juan J. , Salas Azucena
Published: April 2020

Background

Personalized medicine requires finding relationships between variables that influence a patient’s phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of the expected interaction between those variables. Describing these interactions is challenging when the relationship is unknown or when there is no pre-established hypothesis.

Aim

To develop a method to find the relationships between microbiome and transcriptome data and the relevant clinical variables in a complex disease, such as Crohn’s disease.

Results

We present here a method to identify interactions based on canonical correlation analysis. Our main contribution is to show that the model is the most important factor to identify relationships between blocks. Analysis were conducted on three independent datasets: a glioma, Crohn’s disease and a pouchitis data set. We describe how to select the optimum hyperparameters on the glioma dataset. Using such hyperparameters on the Crohn’s disease data set, our analysis revealed the best model for identifying relationships between transcriptome, gut microbiome and clinically relevant variables. With the pouchitis data set our analysis revealed that adding the clinically relevant variables improves the average variance explained by the model.

Conclusions

The methodology described herein provides a framework for identifying interactions between sets of (omic) data and clinically relevant variables. Following this method, we found genes and microorganisms that were related to each other independently of the model, while others were specific to the model used. Thus, model selection proved crucial to finding the existing relationships in multi-omics datasets.
Impact of blood analysis and immune function on the prognosis of patients with COVID-19
Authors: Fu Yue-qiang , Sun Yue-lin , Lu Si-wei , Yang Yang , Wang Yi , Xu Feng
Published: April 2020

Introduction

This retrospective study investigated the implications of changes in blood parameters and cellular immune function in patients with 2019-coronavirus infected disease (COVID-19).

Methods

Records were reviewed of 85 patients with COVID-19 between February 4 and 16, 2020. The primary outcome was in-hospital mortality at 28 days.

Results

Fourteen patients died. The baseline leukocyte count, neutrophil count and hemoglobin was significantly higher in non-survivors compared with survivors, while the reverse was true of lymphocyte count, platelet, PaO2/FiO2, CD3+ count and CD4+ count. The percentage of neutrophil count > 6.3×109/L in death group was significantly higher than that in survival group, and multivariate logistic regression showed neutrophil count was independently associated with mortality. However, there were not significant difference in IgG, IgM, IgA, C3, C4 and the percentage of IgE > 100 IU/ml between the death group and survival group. Areas under the receiver operating characteristic curves of the following at baseline could significantly predict mortality: leukocyte, neutrophil, lymphocyte, CD3+ and CD4+ counts.

Conclusions

For patients with COVID-19, lymphocyte, CD3+ and CD4+ counts that marked decrease suggest a poor outcome. A high neutrophil count is independently associated with mortality. At admission, leukocyte, neutrophil, lymphocyte, CD3+ and CD4+ counts should receive added attention.
Evidence for probable aerosol transmission of SARS-CoV-2 in a poorly ventilated restaurant
Authors: Li Yuguo , Qian Hua , Hang Jian , Chen Xuguang , Hong Ling , Liang Peng , Li Jiansen , Xiao Shenglan , Wei Jianjian , Liu Li , Kang Min
Published: April 2020

Background

The role of aerosols in the transmission of SARS-CoV-2 remains debated. We analysed an outbreak involving three non-associated families in Restaurant X in Guangzhou, China, and assessed the possibility of aerosol transmission of SARS-CoV-2 and characterize the associated environmental conditions.

Methods

We collected epidemiological data, obtained a video record and a patron seating-arrangement from the restaurant, and measured the dispersion of a warm tracer gas as a surrogate for exhaled droplets from the suspected index patient. Computer simulations were performed to simulate the spread of fine exhaled droplets. We compared the in-room location of subsequently infected cases and spread of the simulated virus-laden aerosol tracer. The ventilation rate was measured using the tracer decay method.

Results

Three families (A, B, C), 10 members of which were subsequently found to have been infected with SARS-CoV-2 at this time, or previously, ate lunch at Restaurant X on Chinese New Year’s Eve (January 24, 2020) at three neighboring tables. Subsequently, three members of family B and two members of family C became infected with SARS-CoV-2, whereas none of the waiters or 68 patrons at the remaining 15 tables became infected. During this occasion, the ventilation rate was 0.75–1.04 L/s per person. No close contact or fomite contact was observed, aside from back-to-back sitting by some patrons. Our results show that the infection distribution is consistent with a spread pattern representative of exhaled virus-laden aerosols.

Conclusions

Aerosol transmission of SARS-CoV-2 due to poor ventilation may explain the community spread of COVID-19.
Saliva is more sensitive for SARS-CoV-2 detection in COVID-19 patients than nasopharyngeal swabs
Authors: Wyllie Anne L. , Fournier John , Casanovas-Massana Arnau , Campbell Melissa , Tokuyama Maria , Vijayakumar Pavithra , Geng Bertie , Muenker M. Catherine , Moore Adam J. , Vogels Chantal B.F. , Petrone Mary E. , Ott Isabel M. , Lu Peiwen , Venkataraman Arvind , Lu-Culligan Alice , Klein Jonathan , Earnest Rebecca , Simonov Michael , Datta Rupak , Handoko Ryan , Naushad Nida , Sewanan Lorenzo R. , Valdez Jordan , White Elizabeth B. , Lapidus Sarah , Kalinich Chaney C. , Jiang Xiaodong , Kim Daniel J. , Kudo Eriko , Linehan Melissa , Mao Tianyang , Moriyama Miyu , Oh Ji Eun , Park Annsea , Silva Julio , Song Eric , Takahashi Takehiro , Taura Manabu , Weizman Orr-El , Wong Patrick , Yang Yexin , Bermejo Santos , Odio Camila , Omer Saad B. , Dela Cruz Charles S. , Farhadian Shelli , Martinello Richard A. , Iwasaki Akiko , Grubaugh Nathan D. , Ko Albert I.
Published: April 2020

Rapid and accurate SARS-CoV-2 diagnostic testing is essential for controlling the ongoing COVID-19 pandemic. The current gold standard for COVID-19 diagnosis is real-time RT-PCR detection of SARS-CoV-2 from nasopharyngeal swabs. Low sensitivity, exposure risks to healthcare workers, and global shortages of swabs and personal protective equipment, however, necessitate the validation of new diagnostic approaches. Saliva is a promising candidate for SARS-CoV-2 diagnostics because (1) collection is minimally invasive and can reliably be self-administered and (2) saliva has exhibited comparable sensitivity to nasopharyngeal swabs in detection of other respiratory pathogens, including endemic human coronaviruses, in previous studies. To validate the use of saliva for SARS-CoV-2 detection, we tested nasopharyngeal and saliva samples from confirmed COVID-19 patients and self-collected samples from healthcare workers on COVID-19 wards. When we compared SARS-CoV-2 detection from patient-matched nasopharyngeal and saliva samples, we found that saliva yielded greater detection sensitivity and consistency throughout the course of infection. Furthermore, we report less variability in self-sample collection of saliva. Taken together, our findings demonstrate that saliva is a viable and more sensitive alternative to nasopharyngeal swabs and could enable at-home self-administered sample collection for accurate large-scale SARS-CoV-2 testing.
Psychological impact of infectious disease outbreaks on pregnant women: Rapid evidence review
Authors: Brooks Samantha K. , Weston Dale , Greenberg Neil
Published: April 2020

Infectious disease outbreaks can be distressing for everyone, especially so for those deemed to be particularly vulnerable, such as pregnant women who have been named a high-risk group in the current COVID-19 pandemic. This rapid review aimed to summarise existing literature on the psychological impact of infectious disease outbreaks on women who were pregnant at the time of the outbreak. In April 2020 five databases were searched for relevant literature and main findings were extracted. Thirteen papers were included in the review. The following themes were identified: negative emotional states; living with uncertainty; concerns about infection; concerns about and uptake of prophylaxis or treatment; disrupted routines; non-pharmaceutical protective behaviours; social support; demands from others; financial and occupational concerns; disrupted expectations of birth, prenatal care and postnatal care, and; sources of information. Results showed that pregnant women have unique needs during infectious disease outbreaks and could benefit from: up-to-date, consistent information and guidance; appropriate support and advice from healthcare professionals, particularly with regards to the risks and benefits of prophylaxis and treatment; virtual support groups, and; designating locations or staff specifically for pregnant women.
Diffusion tensor imaging of white matter microstructure in chronic pain: a tract-based spatial statistics study and a systematic review
Authors: Gronemann Dominique Christopher , Koch Katharina , Bantel Carsten , Sörös Peter
Published: April 2020

The pathophysiology of many chronic pain disorders is far from evident. MR imaging studies provided initial data indicating chronic pain might lead to changes in brain structure and function. These changes may contribute to cognitive and emotional impairment and maybe even to the chronification of pain. However, the evidence for pain-related changes in gray and white matter is inconclusive so far. Hence we investigated potential changes of white matter microstructure in 34 adults with chronic noncancer pain (> 1 year) and 34 sex- and age-matched healthy individuals using diffusion tensor imaging (DTI). Whole-brain tract-based spatial statistics (TBSS) analyses of fractional anisotropy, mode of diffusivity, mean diffusivity, axial diffusivity, and radial diffusivity did not show significant differences after correction for multiple comparisons. The volumes of subdivisons of the corpus callosum were not significantly different either. We also performed a systematic review of the existing literature on white matter microstructure in patients with chronic pain following PRISMA guidelines. We identified 53 eligible studies on DTI in chronic pain. These studies demonstrated conflicting results regarding the direction and location of white matter changes across all diagnoses and within major diagnostic subcategories. We propose that two factors primarily contribute to this low reproducibility, small sample size and the use of potentially unreliable DTI parameters derived from the single-tensor model.
Coronavirus Disease (COVID-19) Pandemic: An Overview of Systematic Reviews
Authors: do Nascimento Israel Júnior Borges , O’Mathúna Dónal P. , von Groote Thilo Caspar , Abdulazeem Hebatullah Mohamed , Weerasekara Ishanka , Marusic Ana , Puljak Livia , Civile Vinicius Tassoni , Zakarija-Grkovic Irena , Pericic Tina Poklepovic , Atallah Alvaro Nagib , Filoso Santino , Bragazzi Nicola Luigi , Marcolino Milena Soriano
Published: April 2020

Background

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).

Methods

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.

Results

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%.

Conclusions

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.

Protocol registration

This is an extension of a PROSPERO protocol (CRD42020170623); protocol available on Open Science Framework (https://osf.io/6xtyw).
Impact of Social Distancing Measures on COVID-19 Healthcare Demand in Central Texas
Authors: Wang Xutong , Pasco Remy F , Du Zhanwei , Petty Michaela , Fox Spencer J , Galvani Alison P , Pignone Michael , Johnston S. Claiborne , Meyers Lauren Ancel
Published: April 2020

Background

A novel coronavirus (SARS-CoV-2) emerged in Wuhan, China in late 2019 and rapidly spread worldwide. In the absence of effective antiviral drugs and vaccines, well-targeted social distancing measures are essential for mitigating the COVID-19 pandemic, reducing strain on local health systems, and preventing mortality. Here, we provide a quantitative assessment of the efficacy of social distancing to slow COVID-19 transmission and reduce hospital surge, depending on the timing and extent of the measures imposed for a metropolitan region and its health care systems.

Methods and Findings

We built a granular mathematical model of COVID-19 transmission that incorporated age-specific and risk-stratified heterogeneity, estimates for the transmission, and severity of COVID-19 using current best evidence. We performed thousands of stochastic simulations of COVID-19 transmission in the Austin-Round Rock Metropolitan Area to project the impact of school closures coupled with social distancing measures that were estimated to reduce non-household contacts by 0%, 25%, 50%, 75% or 90%. We compare early versus late implementation and estimate the number of COVID-19 hospitalizations, ICU patients, ventilator needs and deaths through mid-August, 2020. We queried local emergency services and hospital systems to estimate total hospital bed, ICU, and ventilator capacity for the region. We expected COVID-19 hospital beds and ICU requirements would surpass local capacity by mid-May if no intervention was taken.

Assuming a four-day epidemic doubling time, school closures alone would be expected to reduce peak hospitalizations by only 18% and cumulative deaths by less than 3%. Immediate social distancing measures that reduced non-household contacts by over 75%, such as stay-at-home orders and closing of non-essential businesses, would be required to ensure that COVID-19 cases do not overwhelm local hospital surge capacity. Peak ICU bed demand prior to mid August 2020 would be expected to be reduced from 2,121 (95% CI: 2,018-2,208) with no intervention to 698 (95% CI: 204-1,100) with 75% social distancing and 136 (95% CI: 38-308) with 90% social distancing; current ICU bed capacity was estimated at 680. A two-week delay in implementation of such measures is projected to accelerate a local ICU bed shortage by four weeks.

Conclusions

School closures alone hardly impact the epidemic curve. Immediate social distancing measures that reduce non-household contacts by over 75% were required to ensure that COVID-19 cases do not overwhelm local hospital surge capacity. These findings helped inform the Stay Home-Work Safe order enacted by the city of Austin, Texas on March 24, 2020 as a means of mitigating the emerging COVID-19 epidemic.
Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19
Authors: McRae Michael P. , Simmons Glennon W. , Christodoulides Nicolaos J. , Lu Zhibing , Kang Stella K. , Fenyo David , Alcorn Timothy , Dapkins Isaac P. , Sharif Iman , Vurmaz Deniz , Modak Sayli S. , Srinivasan Kritika , Warhadpande Shruti , Shrivastav Ravi , McDevitt John T.
Published: April 2020

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase–myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40–83) and 9 (6–17), respectively, and area under the curve of 0.94 (95% CI 0.89– 0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.
Cholesterol Metabolism—Impacts on SARS-CoV-2 Infection Prognosis
Authors: Peng Yumeng , Wan Luming , Fan Chen , Zhang Pingping , Wang Xiaolin , Sun Jin , Zhang Yanhong , Yan Qiulin , Gong Jing , Yang Huan , Yang Xiaopan , Li Huilong , Wang Yufei , Zong Yulong , Yin Feng , Yang Xiaoli , Zhong Hui , Cao Yuan , Wei Congwen
Published: April 2020

In this study, we specifically addressed the connection between the SARS-CoV-2 virus with host cholesterol metabolism. Plasma lipid profile was measured in 861 COVID-19 patients classified as mild (n=215), moderate (n=364), severe (n=217) or critical (n=65) and 1108 age- and sex-matched healthy individuals. We showed that the levels of both TG and HDL-C were significantly lower in patients with severe disease than in patients with moderate or mild disease. After successful treatment, cholesterol metabolism was reestablished in patients with SARS-CoV-2 infection. The serum concentrations of TC and HDL-C can be used as indicators of disease severity and prognosis in COVID-19 patients.
Influenza and other respiratory viral infections associated with absence from school among schoolchildren in Pittsburgh, Pennsylvania, USA: a cohort study
Authors: Read Jonathan M. , Zimmer Shanta , Vukotich Charles , Schweizer Mary Lou , Galloway David , Lingle Carrie , Yearwood Gaby , Calderone Patti , Noble Eva , Quadelacy Talia , Grantz Kyra , Rinaldo Charles , Gao Hongjiang , Rainey Jeanette , Uzicanin Amra , Cummings Derek A.T.
Published: April 2020

Background

Information on the etiology and age-specific burden of respiratory viral infections among school-aged children remains limited.

Methods

We conducted a cohort study to determine the etiology of ILI (influenza like illness) among 2,519 K–12 students during the 2012–13 influenza season. We obtained nasal swabs from students with ILI-related absences. Generalized linear mixed-effect regressions determined associations of outcomes, including ILI and laboratory-confirmed respiratory virus infection, with school grade and other covariates.

Results

Overall, 459 swabs were obtained from 552 ILI–related absences. Respiratory viruses were found in 292 (63.6%) samples. Influenza was found in 189 (41.2%) samples. with influenza B found in 134 (70.9%). Rates of influenza B were significantly higher in grades 1 (10.1%, 95% CI 6.8%–14.4%), 2 (9.7%, 6.6%–13.6%), 3 (9.3%, 6.3%–13.2%), and 4 (9.9%, 6.8%–13.8%) than in kindergarteners (3.2%, 1.5%–6.0%). After accounting for grade, sex and self-reported vaccination status, influenza B infection risk was lower among kindergarteners in half-day programs compared to kindergarteners in full-day programs (OR = 0.19; 95% CI 0.08–0.45).

Conclusions

ILI and influenza infection is concentrated in younger schoolchildren. Reduced infection by respiratory viruses is associated with a truncated school day for kindergarteners, but requires further investigation in other grades and populations.
Cohort profile: The Chikwawa lung health cohort; a population-based observational non-communicable respiratory disease study in Malawi
Authors: Njoroge Martin , Rylance Sarah , Nightingale Rebecca , Gordon Stephen , Mortimer Kevin , Burney Peter , Rylance Jamie , Obasi Angela , Niessen Louis , Devereux Graham
Published: April 2020

Purpose

The Chikwawa lung health cohort was established in rural Malawi in 2014 to prospectively determine the prevalence and causes of lung disease amongst the general population of adults living in a low-income rural setting in Sub-Saharan Africa.

Participants

A total of 1481 participants were randomly identified and recruited in 2014 for the baseline study. We collected data on demographic, socio-economic status, respiratory symptoms and potentially relevant exposures such as smoking, household fuels, environmental exposures, occupational history/exposures, dietary intake, healthcare utilization, cost (medication, outpatient visits and inpatient admissions) and productivity losses. Spirometry was performed to assess lung function. At baseline, 56.9% of the participants were female, a mean age of 43.8 (SD:17.8) and mean body mass index (BMI) of 21.6 Kg/m2 (SD: 3.46)

Findings to date

Currently, two studies have been published. The first reported the prevalence of chronic respiratory symptoms (13.6%, 95% confidence interval [CI], 11.9 – 15.4), spirometric obstruction (8.7%, 95% CI, 7.0 – 10.7), and spirometric restriction (34.8%, 95% CI, 31.7 – 38.0). The second reported annual decline in forced expiratory volume in one second [FEV1] of 30.9mL/year (95% CI: 21.6 to 40.1) and forced vital capacity [FVC] by 38.3 mL/year (95% CI: 28.5 to 48.1).

Future plans

The ongoing current phase of follow-up will determine the annual rate of decline in lung function as measured through spirometry, and relate this to morbidity, mortality and economic cost of airflow obstruction and restriction. Population-based mathematical models will be developed driven by the empirical data from the cohort and national population data for Malawi to assess the effects of interventions and programmes to address the lung burden in Malawi. The present follow-up study started in 2019.

Strengths and limitations of this study

This is an original cohort study comprising adults randomly identified in a low-income Sub-Saharan African Setting.

The repeated follow up of the cohort has included objective measures of lung function.

The cohort has had high rates of case ascertainment that include verbal autopsies.

The study will include an analysis of the health economic consequences of rate of change of lung function and health economic modelling of impact of lung diseases and potential interventions that could be adopted.

A main limitation of our study is the systematic bias may be introduced through the self-selection of the participants who agreed to take part in the study to date and the migration of individuals from Chikwawa.
Importance of Social Distancing: Modeling the spread of 2019-nCoV using Susceptible-Infected-Quarantined-Recovered-t model
Authors: Aggarwal Nipun
Published: April 2020

Background

The Novel Coronavirus that originated in Wuhan, Hubei, China, has raised global concerns and has been declared a pandemic. The infection shows the primary symptoms of pneumonia and has an incubation period, with the majority of people showing symptoms within 14 days. Online Social Networks are the closest simulations of real-world networks and have similar topology characteristics. This article simulates the spread and control of the nCoV-19 using the SIQR-t model to highlight the importance of self-quarantine and exercise of proper health care as a method to prevent the spread of the virus.

Method

The article uses the Susceptible-Infected-Quarantined-Recovered model with modification, introducing 14 different Infected states depending on the number of days the host has been carrying the infection. We simulate the spread of 2019-nCoV on human interaction similar graph taken from Online Social Network Epinions, of about 75000 nodes, similar to a small town or settlement. The infection rates depend on the sanitation and cleanliness these people exercise.

Results

When people practice self-quarantine and hygiene, aided by the governmental efforts of testing and quarantine, the cumulative number of affected people fall drastically. The decrease is apparent in time-based simulations of the spread received from the study.

Conclusion

The 2019-nCoV is a highly infectious zoonotic virus. It has spread like a pandemic, and governments across the world have launched quarantines. The results of the SIQR-t model indicate that hygiene and social-distancing can reduce its impact and sharply decrease the infection scale. Individual efforts are key to the control.
Individual adherence to mass drug administration in neglected tropical disease control: a probability model conditional on past behaviour
Authors: Hardwick Robert J. , Truscott James E. , Oswald William E. , Werkman Marleen , Halliday Katherine E. , Pullan Rachel L. , Anderson Roy M.
Published: April 2020

We present a general framework which describes the systematic (binary) scenario of individuals either taking treatment or not for any reason, over the course of mass drug administration (MDA) — which we refer to as ‘adherence’ and ‘non-adherence’. The probability models developed can be informed by observed adherence behaviour as well as employed to explore how different patterns influence the impact of MDA programmes, by the use of mathematical models of transmission and control. We demonstrate the interpretative value of the developed probability model employing a dataset collected in the TUMIKIA project, a randomised trial of deworming strategies to control soil-transmitted helminths (STH) by MDA conducted in coastal Kenya. We stratify our analysis by age and sex, although the framework which we introduce here may be readily adapted to accommodate other stratifications. Our findings include the detection of specific patterns of non-adherence in all age groups to varying extents. This is particularly apparent in men of ages 30+. We then demonstrate the use of the probability model in stochastic individual-based simulations by running two example forecasts for the elimination of STH transmission employing MDA within the TUMIKIA trial setting with different adherence patterns. This suggested a substantial reduction in the probability of elimination (between 23-43%) when comparing observed adherence patterns with an assumption of independence, with important implications for programmes. The results here demonstrate the considerable impact and utility of considering non-adherence on the success of MDA programmes to control neglected tropical diseases (NTDs).

Author summary

Mass drug administration (MDA) is an important tool in the prevention of morbidity caused by various NTDs and in the reduction of their transmission. Due to a variety of social and behavioural reasons, many people will either not be offered or refuse such treatment, and if this behaviour is recurring at an individual level, then control measures may face a challenge in achieving their stated goals. Accurately describing the patterns of individual adherence or non-adherence to MDA control measures for NTDs from data, followed by their use in simulated scenarios is a relatively recent development in the study of NTDs. Past analyses assessing individual adherence have informed the approach we take in this work. However, we have sought to provide a framework which encapsulates as many types of adherence behaviour as possible to facilitate the assessment of impact in mathematical models of parasite transmission and control. Our example application to the TUMIKIA data highlights the importance of such a general framework as we find a dependence on past behaviour that may have been missed in standard statistical analyses.
Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence
Authors: Kolozsvári László Róbert , Bérczes Tamás , Hajdu András , Gesztelyi Rudolf , Tiba Attila , Varga Imre , Al-Tammemi Ala’a B. , Szőllősi Gergő József , Harsányi Szilvia , Garbóczy Szabolcs , Zsuga Judit
Published: April 2020

The aim of our study was to predict the epidemic curves (daily new cases) of COVID-19 pandemic using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. We used the publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we used RNNs with gated recurring units (Long Short-Term Memory) to create two 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 also used Root Mean Squared Logarithmic Errors (RMSLE) to compare the predicted models with the observed data. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seems to be low. The influence of this pandemic is significant worldwide and has already impacted our daily life. Decision makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible.
Years of life lost due to the psychosocial consequences of COVID19 mitigation strategies based on Swiss data
Authors: Moser Dominik A. , Glaus Jennifer , Frangou Sophia , Schechter Daniel S.
Published: April 2020

Background

The pandemic caused by COVID-19 has forced governments to implement strict social mitigation strategies to reduce the morbidity and mortality from acute infections. These strategies however carry a significant risk for mental health which can lead to increased short-term and long-term mortality and is currently not included in modelling the impact of the pandemic.

Methods

We used years of life lost (YLL) as the main outcome measure as applied to Switzerland as an exemplar. We focused on suicide, depression, alcohol use disorder, childhood trauma due to domestic violence, changes in marital status and social isolation as these are known to increase YLL in the context of imposed restriction in social contact and freedom of movement. We stipulated a minimum duration of mitigation of 3 months based on current public health plans.

Results

The study projects that the average person would suffer 0.205 YLL due to psychosocial consequence of COVID-19 mitigation measures. However, this loss would be entirely borne by 2.1% of the population, who will suffer an average 9.79 YLL.

Conclusions

The results presented here are likely to underestimate the true impact of the mitigation strategies on YLL. However, they highlight the need for public health models to expand their scope in order to provide better estimates of the risks and benefits of mitigation.
An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City
Authors: Whittle Richard S. , Diaz-Artiles Ana
Published: April 2020

Background

New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the number of detected COVID-19 cases.

Methods

Data were collected from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population). We fit multiple Bayesian Besag-York-Mollié (BYM) mixed models using positive COVID-19 tests as the outcome and a set of 10 representative economic, demographic, and health-care associated ZCTA-level parameters as potential predictors. The BYM model includes both spatial and nonspatial random effects to account for clustering and overdispersion.

Results

Multiple different regression approaches indicated a consistent, statistically significant association between detected COVID-19 cases and dependent (under 18 or 65+ years old) population, male to female ratio, and median household income. In the final model, we found that an increase of only 1% in dependent population is associated with a 2.5% increase in detected COVID-19 cases (95% confidence interval (CI): 1.6% to 3.4%, p < 0.0005). An increase of 1 male per 100 females is associated with a 1.0% (95% CI: 0.6% to 1.5%, p < 0.0005) increases in detected cases. A decrease of $10,000 median household income is associated with a 2.5% (95% CI: 1.0% to 4.1% p = 0.002) increase in detected COVID-19 cases.

Conclusions

Our findings indicate associations between neighborhoods with a large dependent population, those with a high proportion of males, and low-income neighborhoods and detected COVID-19 cases. Given the elevated mortality in aging populations, the study highlights the importance of public health management during and after the current COVID-19 pandemic. Further work is warranted to fully understand the mechanisms by which these factors may have affected the number of detected cases, either in terms of the true number of cases or access to testing.
A multivariate spatiotemporal spread model of COVID-19 using ensemble of ConvLSTM networks
Authors: Paul Swarna kamal , Jana Saikat , Bhaumik Parama
Published: April 2020

The high R-naught factor of SARS-CoV-2 has created a race against time for mankind and it necessitates rapid containment actions to control the spread. In such scenario short term accurate spatiotemporal predictions can help understanding the dynamics of the spread in a geographic region and identify hotspots. However due to the novelty of the disease there is very little disease specific data generated yet. This poses a difficult problem for machine learning methods to learn a model of the epidemic spread from data. A proposed ensemble of convolutional LSTM based spatiotemporal model can forecast the spread of the epidemic with high resolution and accuracy in a large geographic region. A data preparation method is proposed to convert available spatial causal features into set of 2D images with or without temporal component. The model has been trained with available data for USA and Italy. It achieved 5.57% and 0.3% mean absolute percent error for total number of predicted infection cases in a 5day prediction period for USA and Italy respectively.
Saliva Sample as a Non-Invasive Specimen for the Diagnosis of Coronavirus Disease-2019 (COVID-19): a Cross-Sectional Study
Authors: Pasomsub Ekawat , Watcharananan Siriorn P. , Boonyawat Kochawan , Janchompoo Pareena , Wongtabtim Garanyuta , Suksuwan Worramin , Sungkanuparph Somnuek , Phuphuakrat Angsana
Published: April 2020

Objectives

Amid the increasing number of global pandemic coronavirus disease 2019 (COVID-19) cases, there is a need for a quick and easy method to obtain a non-invasive sample for the detection of this novel coronavirus 2019 (SARS-CoV-2). We aimed to investigate the potential use of saliva samples as a non-invasive tool for the diagnosis of COVID-19.

Methods

From 27 March to 4 April, 2020, we prospectively collected saliva samples and a standard nasopharyngeal and throat swab in persons seeking care at an acute respiratory infection clinic in a university hospital during the outbreak of COVID-19. Real-time polymerase chain reaction (RT-PCR) was performed, and the results of the two specimens were compared.

Results

Two-hundred pairs of the samples were collected. Sixty-nine (34.5%) patients were male, and the median (interquartile) age was 36 (28-48) years. Using nasopharyngeal and throat swab RT-PCR as the reference standard, the prevalence of COVID-19 diagnosed by nasopharyngeal and throat swab RT-PCR was 9.5%. The sensitivity and specificity of the saliva sample RT-PCR were 84.2% [95% confidence interval (CI) 79.2%-89.3%], and 98.9% (95% CI 97.5-100.3%), respectively. An analysis of the agreement between the two specimens demonstrated 97.5% observed agreement (kappa coefficient 0.851, 95% CI 0.723-0.979; p <0.001).

Conclusions

Saliva specimens can be used for the diagnosis of COVID-19. The collection method is non-invasive, and non-aerosol generating. Using a saliva sample as a specimen for the detection of SARS-CoV-2 could facilitate the diagnosis of the disease, which is one of the strategies that helps in controlling the epidemic.
COVID-19: An Update on the Epidemiological, Genomic Origin, Phylogenetic study, India centric to Worldwide current status
Authors: Nandagopal Murugan , Jansi R. Sagaya
Published: April 2020

The pandemic spread of novel coronavirus, (SARS-CoV-2) causing CoronaVirus Infectious Diseases (COVID-19) emerged into a global threat for human life causing serious death rates and economic crunch all over the globe. As on April 17, 2020 at 2:00am CEST, there include a total of 2,034,802 confirmed cases for Corona and 1,35,163 deaths worldwide have been reported which includes 212 countries, areas or territories reported by World Health Organization (WHO), in which USA tops 6,32,781 confirmed cases (28,221 deaths) followed by Italy 1,65,155 (21,647 deaths), Spain 1,77,633 (18,579 deaths) and China 84,149 (4,642 deaths). This study aims to compare the genomic nature of SARS-CoV-2 genome reported from Wuhan, China with two Indian isolate genome reported by ICMR-NIV, India. Further Phylogenetic studies performed with coronavirus infecting non-human species like Bats, Duck, and sparrow were compared with Indian and other country whole genome sequences of SARS-CoV2 using MegaX and traced out the association between the human coronavirus with the other species viral genome. In addition, epidemiological reports on COVID-19 among Worldwide and India centric data were compared between April 7, 2020 to April 17, 2020 global data and the number of active cases were increased dramatically in this 10 days period studied, highlighted in the current study.
Revealing COVID-19 Transmission by SARS-CoV-2 Genome Sequencing and Agent Based Modelling
Authors: Rockett Rebecca J , Arnott Alicia , Lam Connie , Sadsad Rosemarie , Timms Verlaine , Gray Karen-Ann , Eden John-Sebastian , Chang Sheryl , Gall Mailie , Draper Jenny , Sim Eby , Bachmann Nathan L , Carter Ian , Basile Kerri , Byun Roy , O’Sullivan Matthew V , Chen Sharon C-A , Maddocks Susan , Sorrell Tania C. , Dwyer Dominic E , Holmes Edward C , Kok Jen , Prokopenko Mikhail , Sintchenko Vitali
Published: April 2020

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.
Pulmonary post-mortem findings in a large series of COVID-19 cases from Northern Italy
Authors: Carsana Luca , Sonzogni Aurelio , Nasr Ahmed , Rossi Roberta Simona , Pellegrinelli Alessandro , Zerbi Pietro , Rech Roberto , Colombo Riccardo , Antinori Spinello , Corbellino Mario , Galli Massimo , Catena Emanuele , Tosoni Antonella , Gianatti Andrea , Nebuloni Manuela
Published: April 2020

Importance

The analysis of lung tissues of patients with COVID-19 may help understand pathogenesis and clinical outcomes in this life-threatening respiratory illness.

Objective

To determine the histological patterns in lung tissue of patients with severe COVID-19.

Design and participants

Lungs tissues of 38 cases who died for COVID-19 in two hospital of Northern Italy were systematically analysed. Hematoxylin-eosin staining, immunohistochemistry for the inflammatory infiltrate and cellular components, electron microscopy were performed.

Results

The features of the exudative and proliferative phases of Diffuse Alveolar Disease (DAD) were found: capillary congestion, necrosis of pneumocytes, hyaline membrane, interstitial oedema, pneumocyte hyperplasia and reactive atypia, platelet-fibrin thrombi. The inflammatory infiltrate was composed by macrophages in alveolar lumens and lymphocytes mainly in the interstice. Electron microscopy revealed viral particles in the cytoplasm of pneumocytes.

Conclusions and relevance

The predominant pattern of lung lesions in COVID-19 patients is DAD, as described for the other two coronavirus that infect humans, SARS-CoV and MERS-CoV. Hyaline membrane formation and pneumocyte atypical hyperplasia are frequently found. The main relevant finding is the presence of platelet-fibrin thrombi in small arterial vessels; this important observation fits into the clinical context of coagulopathy which dominates in these patients and which is one of the main targets of therapy.
Osteocyte Transcriptome Mapping Identifies a Molecular Landscape Controlling Skeletal Homeostasis and Susceptibility to Skeletal Disease
Authors: Youlten Scott E. , Kemp John P. , Logan John G. , Ghirardello Elena J. , Sergio Claudio M. , Dack Michael R. G. , Guilfoyle Siobhan E. , Leitch Victoria D. , Butterfield Natalie C. , Komla-Ebri Davide , Chai Ryan C. , Corr Alexander P. , Smith James T. , Mohanty Sindhu , Morris John A. , McDonald Michelle M. , Quinn Julian M. W. , McGlade Amelia R. , Bartonicek Nenad , Jansson Matt , Hatzikotoulas Konstantinos , Irving Melita D. , Beleza-Meireles Ana , Rivadeneira Fernando , Duncan Emma , Richards J. Brent , Adams David J. , Lelliott Christopher J. , Brink Robert , Phan Tri Giang , Eisman John A. , Evans David M. , Zeggini Eleftheria , Baldock Paul A. , Bassett J. H. Duncan , Williams Graham R. , Croucher Peter I.
Published: April 2020

Osteocytes are master regulators of the skeleton. We mapped the transcriptome of osteocytes from different skeletal sites, across age and sexes in mice to reveal genes and molecular programs that control this complex cellular-network. We define anosteocyte transcriptome signatureof 1239 genes that distinguishes osteocytes from other cells. 77% have no previously known role in the skeleton and are enriched for genes regulating neuronal network formation, suggesting this program is important in osteocyte communication. We evaluated 19 skeletal parameters in 733 knockout mouse lines and reveal 26osteocyte transcriptome signaturegenes that control bone structure and function. We showedosteocyte transcriptome signaturegenes are enriched for human orthologs that cause monogenic skeletal disorders (P=2.4×10-22) and are associated with the polygenic diseases osteoporosis (P=1.8×10-13) and osteoarthritis (P=1.6×10-7). Thus, we reveal the molecular landscape that regulates osteocyte network formation and function and establish the importance ofosteocytesin human skeletal disease.