bluuebunny/crossref_metadata_2025_split huggingface dataset:
The reproduction number of COVID-19 and its correlation with public health interventions
Authors: Linka Kevin , Peirlinck Mathias , Kuhl Ellen 
Published: May 2020

AbstractThroughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22±1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67±0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24±2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.
Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks
Authors: Asif Sohaib , Wenhui Yi , Jin Hou , Tao Yi , Jinhai Si 
Published: May 2020

AbstractThe COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A vital step in the combat towards COVID-19 is a successful screening of contaminated patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aimed to automatically detect COVID‐ 19 pneumonia patients using digital chest x‐ ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN). The dataset consists of 864 COVID‐ 19, 1345 viral pneumonia and 1341 normal chest x‐ ray images. In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and gives a classification accuracy of more than 98% (training accuracy of 97% and validation accuracy of 93%). The results demonstrate that transfer learning proved to be effective, showed robust performance and easily deployable approach for COVID-19 detection.
Neural network pattern recognition of ultrasound image gray scale intensity histogram of breast lesions to differentiate between benign and malignant lesions
Authors: Ramachandran Arivan , Shiva Balan KR , Kiran Swathi , Azharudeen Mohamed 
Published: May 2020

ABSTRACTThe aim of this study is to analyze the effectiveness of grayscale intensity histogram to differentiate benign and malignant lesions using a convolutional neural network. Data (200 USG images, 100-malignant, 100-benign) was downloaded from an online access repository. The images were despeckled using ImageJ software and the grayscale intensity histogram values were extracted. In-built neural network pattern recognition application in Matlab R2019b was used to classify the images, which is a two-layer feed-forward network, with sigmoid hidden and softmax output neurons. The positive predictive value of the CNN was 95%. The best performance of 0.078264 was achieved at 36 epochs in the validation set. This study suggests that the grayscale intensity histogram of a USG image is an easy and feasible method to identify malignant lesions through an artificial neural network.
Impact of non-pharmaceutical interventions against COVID-19 in Europe: A quasi-experimental study
Authors: Hunter Paul R , Colón-González Felipe J , Brainard Julii , Rushton Steven 
Published: May 2020

ABSTRACTThe current epidemic of COVID-19 is unparalleled in recent history as are the social distancing interventions that have led to a significant halt on the economic and social life of so many countries. However, there is very little empirical evidence about which social distancing measures have the most impact. We report a quasi-experimental study of the impact of various interventions for control of the outbreak. Data on case numbers and deaths were taken from the daily published figures by the European Centre for Disease Control and dates of initiation of various control strategies from the Institute of Health Metrics and Evaluation website and published sources. Our complementary analyses were modelled in R using Bayesian generalised additive mixed models (GAMM) and in Stata using multi-level mixed effects regression models. From both sets of modelling, we found that closure of education facilities, prohibiting mass gatherings and closure of some non-essential businesses were associated with reduced incidence whereas stay at home orders and closure of all non-businesses was not associated with any independent additional impact. Our results could help inform strategies for coming out of lockdown.
Machine learning for ovarian cancer: lasso regression-based predictive model of early mortality in patients with stage I and stage II ovarian cancer
Authors: Chen Robert 
Published: May 2020

ABSTRACTWhile machine learning has shown promise in prediction of mortality in situations such as intensive care units, there is limited evidence of its application towards ovarian cancer.In this study, we extracted clinical covariates from a cohort of 273 patients with stage I and II ovarian cancer, and trained a machine learning algorithm, L2-regularized logistic regression, on the set of patients in prediction problem for mortality less than 20 months, representing the 25th percentile of overall survival.Our model achieves an AUC of 0.621, accuracy 0.761, sensitivity 0.130, positive predictive value 0.659, and F1 score 0.216. This study serves as a proof of concept for a predictive model customized towards mortality prediction for malignant neoplasm of the left testis, and can be adapted and generalized to related tumors such as spermatic cord and scrotal tumor types.
Trends in excess cancer and cardiovascular deaths in Scotland during the COVID-19 pandemic 30 December – 20 April suggest underestimation of COVID-19 related deaths
Authors: Figueroa Jonine D. , Brennan Paul , Theodoratou Evropi , Poon Michael , Purshouse Karin , Din Farhat , Jin Kai , Mesa-Eguiagaray Ines , Dunlop Malcolm , Hall Peter , Cameron David , Wild Sarah , Sudlow Cathie 
Published: May 2020

AbstractUnderstanding the trends in causes of death for different diseases during the current COVID-19 pandemic is important to determine whether there are excess deaths beyond what is normally expected. Using the most recent report from National Records Scotland (NRS) on 29 April 2020, we examined the percentage difference in crude numbers of deaths in 2020 compared to the average for 2015-2019 by week of death within calendar year. To determine if trends were similar, suggesting underreporting/underdiagnosed COVID-19 related deaths, we also looked at the trends in % differences for cardiovascular disease deaths. From the first 17 weeks’ of data, we found a peak in excess deaths at week 14 of 2020, about four weeks after the first case in Scotland was detected on 1 March 2020-- but by week 17 these excesses had returned to normal levels, 4 weeks after lockdown in the UK began. Similar observations were seen for cardiovascular disease-related deaths. These observations suggest that the short-term increase in excess cancer and cardiovascular deaths might be associated with undetected/unconfirmed deaths related to COVID-19. Both of these conditions make patients more susceptible to infection and lack of widespread access to testing for COVID-19 are likely to have resulted in under-estimation of COVID-19 mortality. These data further suggest that the cumulative toll of COVID-19 on mortality is likely undercounted. More detailed analysis is needed to determine if these excesses were directly or indirectly related to COVID-19. Disease specific mortality will need constant monitoring for the foreseeable future as changes occur in increasing capacity and access to testing, reporting criteria, changes to health services and different measures are implemented to control the spread of the COVID-19. Multidisciplinary, multi-institutional, national and international collaborations for complementary and population specific data analysis is required to respond and mitigate adverse effects of the COVID-19 pandemic and to inform planning for future pandemics.
Ultra-fast and onsite interrogation of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in environmental specimens via surface enhanced Raman scattering (SERS)
Authors: Zhang Dayi , Zhang Xiaoling , Ma Rui , Deng Songqiang , Wang Xinquan , Zhang Xian , Huang Xia , Liu Yi , Li Guanghe , Qu Jiuhui , Zhu Yu , Li Junyi 
Published: May 2020

AbstractThe outbreak of coronavirus infectious disease-2019 (COVID-19) pneumonia challenges the rapid interrogation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in human and environmental specimens. In this study, we developed an assay using surface enhanced Raman scattering (SERS) coupled with multivariate analysis to diagnose SARS-CoV-2 in an ultra-fast manner without any pretreatment (e.g., RNA extraction). Using silver-nanorod SERS array functionalized with cellular receptor angiotensin-converting enzyme 2 (ACE2), we obtained strong SERS signals of ACE2 at 1032, 1051, 1089, 1189, 1447 and 1527 cm−1. The recognition and binding of receptor binding domain (RBD) of SARS-CoV-2 spike protein on SERS assay significantly quenched the spectral intensities of most peaks and exhibited a shift from 1189 to 1182 cm−1. On-site tests on 17 water samples with a portable Raman spectrometer proved its accuracy and easy-operation for spot diagnosis of SARS-CoV-2 to evaluate disinfection performance, explore viral survival in environmental media, assess viral decay in wastewater treatment plant and track SARS-CoV-2 in pipe network. Our findings raise a state-of-the-art spectroscopic tool to screen and interrogate viruses with RBD for human cell entry, proving its feasibility and potential as an ultra-fast diagnostic tool for public health.
Evaluating growth pattern and assessing future scenario of COVID-19 epidemic of India
Authors: Patil Nandan L. , Gangavati Lakshmi R. 
Published: May 2020

AbstractCOVID-19 the modern pandemic has spread across the world at a rapid pace. SARS-CoV 2 is highly transmissible and the rate of infection is exponential for heavily infected countries. Asymptotic carriers and longer incubation period have been key towards such a large-scale distribution of disease. Data released by official authorities on COVID-19 cases is significantly affected by various factors such as size of sample, incubation period of disease and time taken to test the sample. These factors mask the useful pattern (signal) of disease spread. Thus, an ingenious method to group data into cycles of five and seven days, for studying pattern of disease spread is undertaken. Occurrence of recurrent peaks as indicated by Adjusted Rate of infection per day indicated the spread of disease has been non-uniform. Currently, India is yet to reach the critical point (peak of epidemic) with adjusted daily cases more than 1000. Increasing testing capacity along with random sampling and sample pooling can help in preventing formation of these peaks in future. The proposed method helps in assessing the current state and for predicting future scenarios epidemics.
A new role for Biofoundries in rapid prototyping, development, and validation of automated clinical diagnostic tests for SARS-CoV-2
Authors: Crone Michael A. , Priestman Miles , Ciechonska Marta , Jensen Kirsten , Sharp David , Randell Paul , Storch Marko , Freemont Paul 
Published: May 2020

AbstractThe SARS-CoV-2 pandemic has shown how the rapid rise in demand for patient and community sample testing, required for tracing and containing a highly infectious disease, has quickly overwhelmed testing capability globally. With most diagnostic infrastructure dependent on specialised instruments, their exclusive reagent supplies quickly become bottlenecks in times of peak demand, creating an urgent need for novel approaches to boost testing capacity. We address this challenge by refocusing the full synthetic biology stack available at the London Biofoundry onto the development of alternative patient sample testing pipelines. We present a reagent-agnostic automated SARS-CoV-2 testing platform that can be quickly deployed and scaled, and that accepts a diverse range of reagents. Using an in-house-generated, open-source, MS2-virus-like-particle-SARS-CoV-2 standard, we validate RNA extraction and RT-qPCR workflows as well as two novel detection assays based on CRISPR-Cas and Loop-mediated isothermal Amplification (LAMP) approaches. In collaboration with an NHS diagnostic testing lab, we report the performance of the overall workflow and benchmark SARS-CoV-2 detection in patient samples via RT-qPCR, CRISPR-Cas, and LAMP against clinical test sets. The validated RNA extraction and RT-qPCR platform has been installed in NHS diagnostic labs and now contributes to increased patient sample processing in the UK while we continue to refine and develop novel high-throughput diagnostic methods. Finally, our workflows and protocols can be quickly implemented and adapted by members of the Global Biofoundry Alliance and the wider scientific and medical diagnostics community.
The epidemiologic parameters for COVID-19: A Systematic Review and Meta-Analysis
Authors: Izadi Neda , Taherpour Niloufar , Mokhayeri Yaser , Ghorbani Sahar Sotoodeh , Rahmani Khaled , Nazari Seyed Saeed Hashemi 
Published: May 2020

IntroductionThe World Health Organization (WHO) declared the outbreak to be a public health emergency and international concern and recognized it as a pandemic. The aim of this study was to estimate the epidemiologic parameters of novel coronavirus (COVID-19) pandemic for clinical and epidemiological help.MethodsFour electronic databases including Web of Science, Medline (PubMed), Scopus and Google Scholar were searched for literature published from early December 2019 up to 23 March 2020. The “metan” command was used to perform a fixed or random effects analysis. Cumulative meta-analysis was performed using the “metacum” command.ResultsTotally 76 observational studies were included in the analysis. The pooled estimate for R0was 2.99 (95% CI: 2.71-3.27) for COVID-19. The overall R0was 3.23, 1.19, 3.6 and 2.35 for China, Singapore, Iran and Japan, respectively. The overall Serial Interval, doubling time, incubation period were 4.45, 4.14 and 4.24 days for COVID-19. In addition, the overall estimation for growth rate and case fatality rate for COVID-19 were 0.38% and 3.29%, respectively.ConclusionCalculating the pooled estimate of the epidemiological parameters of COVID-19 as an emerging disease, could reveal epidemiological features of the disease that consequently pave the way for health policy makers to think more about control strategies.
Optimal Control Strategy Applied to Dynamic Model of Drug Abuse Incident for Reducing Its Adverse Effects
Authors: Islam Md. Azmir Ibne , Biswas Md. Haider Ali 
Published: May 2020

AbstractThe present world is facing a devastating reality as drug abuse prevails in every comer of a society. The progress of a country is obstructed due to the excessive practice of taking drugs by the young generation. Like other countries, Bangladesh is also facing this dreadful situation. The multiple use of drug substances leads an individual to a sorrowful destination and for this reason, the natural behavior of human mind is disrupted. An addicted individual may regain his normal life by proper monitoring and treatment. The objective of this study is to analyze a mathematical model on the dynamics of drug abuse in the perspective of Bangladesh and reduce the harmful consequences with effective control policies using the idea of optimal control theory. The model has been solved analytically introducing a specific optimal goal. Numerical simulations have also been performed to review the behaviors of analytical findings. The analytical results have been verified with the numerical simulations. The analysis of this paper shows that it is possible to control drug addiction if there is less interaction among general people with the addicted individuals. Family based care, proper medical treatment, awareness and educational programs can be the most effective ways to reduce the adverse effects of drug addiction in a shortest possible time.
Predictive mathematical models for the number of individuals infected with COVID-19
Authors: Fokas A.S. , Dikaios N. , Kastis G.A. 
Published: May 2020

AbstractWe model the time-evolution of the number N(t) of individuals reported to be infected in a given country with a specific virus, in terms of a Riccati equation. Although this equation is nonlinear and it contains time-dependent coefficients, it can be solved in closed form, yielding an expression for N(t) that depends on a function α(t). For the particular case that α(t) is constant, this expression reduces to the well-known logistic formula, giving rise to a sigmoidal curve suitable for modelling usual epidemics. However, for the case of the COVID-19 pandemic, the long series of available data shows that the use of this simple formula for predictions underestimates N(t); thus, the logistic formula only provides a lower bound of N(t). After experimenting with more than 50 different forms of α(t), we introduce two novel models that will be referred to as “rational” and “birational”. The parameters specifying these models (as well as those of the logistic model), are determined from the available data using an error-minimizing algorithm. The analysis of the applicability of the above models to the cases of China and South Korea suggest that they yield more accurate predictions, and importantly that they may provide an upper bound of the actual N(t). Results are presented for Italy, Spain, and France.
A mathematical model to investigate the transmission of COVID-19 in the Kingdom of Saudi Arabia
Authors: Alshammari Fehaid Salem 
Published: May 2020

AbstractSince the first confirmed case of SARS-CoV-2 coronavirus (COVID-19) in the 2ndday of March, Saudi Arabia has not report a quite rapid COVD-19 spread compared to America and many European countries. Possible causes include the spread of asymptomatic cases. To characterize the transmission of COVID-19 in Saudi Arabia, this paper applies a susceptible, exposed, symptomatic, asymptomatic, hospitalized, and recovered dynamical model, along with the official COVID-19 reported data by the Ministry of Health in Saudi Arabia. The basic reproduction numberR0is estimated to range from 2.87 to 4.9.
Potential biases arising from epidemic dynamics in observational seroprotection studies
Authors: Kahn Rebecca , Kennedy-Shaffer Lee , Grad Yonatan H. , Robins James M. , Lipsitch Marc 
Published: May 2020

AbstractThe extent and duration of immunity following SARS-CoV-2 infection are critical outstanding questions about the epidemiology of this novel virus, and studies are needed to evaluate the effects of serostatus on reinfection. Understanding the potential sources of bias and methods to alleviate biases in these studies is important for informing their design and analysis. Confounding by individual-level risk factors in observational studies like these is relatively well appreciated. Here, we show how geographic structure and the underlying, natural dynamics of epidemics can also induce noncausal associations. We take the approach of simulating serologic studies in the context of an uncontrolled or a controlled epidemic, under different assumptions about whether prior infection does or does not protect an individual against subsequent infection, and using various designs and analytic approaches to analyze the simulated data. We find that in studies assessing the efficacy of serostatus on future infection, comparing seropositive individuals to seronegative individuals with similar time-dependent patterns of exposure to infection, by stratifying or matching on geographic location and time of enrollment, is essential to prevent bias.
Pharmacological interventions for COVID-19: Protocol for a Rapid Living Systematic Review with network meta-analysis
Authors: Pinto Ana Carolina Pereira Nunes , da Rocha Aline Pereira , Milby Keilla Martins , Rocha-Filho César Ramos , de Assis Reis Felipe Sebastião , Junior Nelson Carvas , Civile Vinicius Tassoni , Santos Rodolfo Rodrigo Pereira , Trevisani Giulia Fernandes Moça , Ferla Laura Jantsch , Ramalho Gabriel Sodré , Puga Maria Eduarda Santos , Trevisani Virgínia Fernandes Moça , Atallah Alvaro Nagib 
Published: May 2020

ABSTRACTCONTEXT AND OBJECTIVECoronavirus disease 2019 (COVID-19) has emerged in China in December 2019 and rapidly spread. Although extraordinary efforts have been made on research regarding pharmacological interventions, none have proven effective. This is the protocol for a rapid living systematic review that aims to compare the effectiveness and safety of different pharmacological interventions for the treatment of COVID-19.METHODSrapid living systematic review methodology with Network Meta-Analysis following the recommendations of Cochrane Handbook. We will include randomized controlled trials (RCT) and quasi-RCTs that evaluate single and/or combined pharmacological interventions at any dose for the treatment of COVID-19. We will search PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), LILACS, Scopus and SciELO to identify potentially eligible studies. No language restrictions will be used in the selection. We will perform the critical appraisal of included studies with the Risk of Bias tool and the certainty of evidence will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE).
Predictive Model with Analysis of the Initial Spread of COVID-19 in India
Authors: Ghosh Shinjini 
Published: May 2020

AbstractThe Coronavirus Disease 2019 (COVID-19) has currently ravaged through the world, resulting in over three million confirmed cases and over two hundred thousand deaths, a complete change in daily life as we know it, worldwide lock-downs, travel restrictions, as well as heightened hygiene measures and physical distancing. Being able to analyse and predict the spread of this epidemic-causing disease is hence of utmost importance now, especially as it would help in the reasoning behind important decisions drastically affecting countries and their people, as well as in ensuring efficient resource and utility management. However, the needs of the people and specific conditions of the spread are varying widely from country to country. In this article, we have conducted an in-depth analysis of the spread of COVID-19 in India, patient statistics, as well as proposed a mathematical prediction system that has succeeded in predicting the following day’s number of cases with 83% accuracy, ever since the first COVID-19 case was declared in India.