bluuebunny/crossref_metadata_2025_split huggingface dataset:
Superspreaders help covid-19 elimination
Authors: Kault David 
Published: April 2020

AbstractThere seems to be widespread pessimism regarding the ability of a nation to eliminate covid. One factor in this pessimism seems to be concern that covid might always be able to re-emerge because of the ongoing presence of unrecognised asymptomatic cases. However, it is shown here that it should be possible to eliminate covid more easily than anticipated, for a reason that at first glance seems paradoxical - the presence of superspreaders. If superspreaders are responsible for most of the spread, then, with the average number of secondary cases fixed at say R0 = 2.5, we have to conclude that superspreaders are relatively rare. When towards the end of an elimination program, there are very few infected people, whether symptomatic or asymptomatic, that small number of people may well not include any superspreaders. As a result, chance effects may make extinction likely. Nevertheless it is clear an attempt at elimination will require a rather onerous “lockdown”. In this paper we use a branching processes model to look at the tradeoff between risk of disease re-emergence and the length of “lockdown” required after a program of elimination has dropped the number of symptomatic cases in a region to just one.
Estimated inequities in COVID-19 infection fatality rates by ethnicity for Aotearoa New Zealand
Authors: Steyn Nicholas , Binny Rachelle N. , Hannah Kate , Hendy Shaun C. , James Alex , Kukutai Tahu , Lustig Audrey , McLeod Melissa , Plank Michael J. , Ridings Kannan , Sporle Andrew 
Published: April 2020

AbstractThere is limited evidence as to how COVID-19 infection fatality rates (IFR) may vary by ethnicity. We combine demographic and health data for ethnic groupings in Aotearoa New Zealand with international data on IFR for different age groups to estimate inequities in IFR by ethnicity. We find that, if age is the dominant factor determining IFR, estimated IFR for Māori is around 50% higher than non-Māori. If underlying health conditions are more important than age per se, then estimated IFR for Māori is more than 2.5 times that of New Zealand European, and estimated IFR for Pasifika is almost double that of New Zealand European. IFRs for Māori and Pasifika are likely to be increased above these estimates by racism within the healthcare system and other inequities not reflected in official data. IFR does not account for differences among ethnicities in COVID-19 incidence, which could be higher in Māori and Pasifika as a result of crowded housing and higher inter- generational contact rates. These factors should be included in future disease incidence modelling. The communities at the highest risk will be those with elderly populations, and Māori and Pasifika communities, where the compounded effects of underlying health conditions, socioeconomic disadvantage, and structural racism result in imbricated risk of contracting COVID-19, becoming unwell, and death.
XGMix: Local-Ancestry Inference with Stacked XGBoost
Authors: Kumar Arvind , Montserrat Daniel Mas , Bustamante Carlos , Ioannidis Alexander 
Published: April 2020

AbstractGenomic medicine promises increased resolution for accurate diagnosis, for personalized treatment, and for identification of population-wide health burdens at rapidly decreasing cost (with a genotype now cheaper than an MRI and dropping). The benefits of this emerging form of affordable, data-driven medicine will accrue predominantly to those populations whose genetic associations have been mapped, so it is of increasing concern that over 80% of such genome-wide association studies (GWAS) have been conducted solely within individuals of European ancestry [1]. The severe under-representation of the majority of the world’s populations in genetic association studies stems in part from an addressable algorithmic weakness: lack of simple, accurate, and easily trained methods for identifying and annotating ancestry along the genome (local ancestry). Here we present such a method (XGMix) based on gradient boosted trees, which, while being accurate, is also simple to use, and fast to train, taking minutes on consumer-level laptops.
Statistical analysis of the impact of environmental temperature on the exponential growth rate of cases infected by COVID-19
Authors: Livadiotis G. 
Published: April 2020

AbstractWe perform a statistical analysis for understanding the effect of the environmental temperature on the exponential growth rate of the cases infected by COVID-19 for US and Italian regions. In particular, we analyze the datasets of regional infected cases, derive the growth rates for regions characterized by a readable exponential growth phase in their evolution spread curve and plot them against the environmental temperatures averaged within the same regions, derive the relationship between temperature and growth rate, and evaluate its statistical confidence. The results clearly support the first reported statistically significant relationship of negative correlation between the average environmental temperature and exponential growth rates of the infected cases. The critical temperature, which eliminates the exponential growth, and thus the COVID-19 spread in US regions, is estimated to be TC = 86.1 ± 4.3 F0.
Excess deaths during the Covid-19 pandemic: An international comparison
Authors: Docherty Kieran F. , Butt Jawad H. , de Boer Rudolf A. , Dewan Pooja , Køber Lars , Maggioni Aldo P. , McMurray John J.V. , Solomon Scott D. , Jhund Pardeep S. 
Published: April 2020

AbstractBackgroundWith the global pandemic of coronavirus disease 2019 (Covid-19) there has been disruption to normal clinical activity in response to the increased demand on health services. There are reports of a reduction in non-Covid-19 emergency presentations. Consequentially, there are concerns that deaths from non-Covid-19 causes could increase. We examined recent reported population-based mortality rates, compared with expected rates, and compared any excess in deaths with the number of deaths attributed to Covid-19.MethodsNational agency and death registration reports were searched for numbers of deaths attributed to Covid-19 and overall mortality that had been publicly reported by 06 May 2020. Data on the number of deaths attributed to Covid-19, the total number of deaths registered in the population and the historical average over at least 3 years were collected. Data were available for 4 European countries (England & Wales, Scotland, Netherlands and Italy) and New York State, United States of America.ResultsThere was an increase in observed, compared with expected, mortality in Scotland (+68%), England and Wales (+74%), the Netherlands (+58%), Italy (+39%) and New York state (+49%). Of these deaths, only 73% in Scotland, 71 % in England and Wales, 53% in the Netherlands, 54% in Italy and 79% in New York state were attributed to Covid-19 leaving a number of excess deaths not attributed to Covid-19. In the 5-week period of study, Scotland, 10% of the excess of deaths were attributed to dementia/Alzheimer’s disease and 7% to cardiovascular causes.ConclusionA substantial proportion of excess deaths observed during the current COVID-19 pandemic are not attributed to COVID-19 and may represent unrecognised deaths due to Covid-19, an excess of deaths due to other causes, or both. The impact of Covid-19 on mortality and morbidity from other causes needs to be quantified and addressed in public health planning.
Military participation in health security: analysis of Joint External Evaluation mission reports and National Action Plans for Health Security
Authors: Forshey Brett M. , Woodward Alexandra K. , Sanchez Jose L. , Petzing Stephanie R. 
Published: April 2020

AbstractMilitaries across the world play an important but at times poorly defined and underappreciated role in global health security. For example, they are often called upon to support civilian authorities in humanitarian crises and to provide routine healthcare for civilians. Furthermore, military personnel are a unique population in a health security context, as they are highly mobile and often deploy to austere settings domestically and internationally, which may increase exposure to infectious diseases. Despite the role of militaries, few studies have systematically evaluated the involvement of militaries in global health security activities, including the Global Health Security Agenda (GHSA). To address this shortcoming, we analyzed Joint External Evaluation (JEE) mission reports (n=91) and National Action Plans for Health Security (n=11) that had been completed as of October 2019 (n=91) to determine the extent to which military organizations have been involved in the evaluation process, country military contributions to health security are accounted for, and specific recommendations are provided for the country’s military. For JEE reports, military involvement was highest for the “Respond” core area (73%) but much lower for the Prevent (36%) and Detect (30%) core areas. Similarly, 73% of NAPHS documents mentioned military involvement in the Respond core area, compared to 27% and 36% for Prevent and Detect, respectively. Additionally, only 26% of JEE reports provide recommendations for the military in any of the core areas. Our results indicate the need to more fully incorporate military roles and contributions into the GHSA framework and other health security activities in order to improve national capabilities to prevent, detect, and respond to infectious disease threats.
Multi-level modeling of early COVID-19 epidemic dynamics in French regions and estimation of the lockdown impact on infection rate
Authors: Prague Mélanie , Wittkop Linda , Collin Annabelle , Dutartre Dan , Clairon Quentin , Moireau Philippe , Thiébaut Rodolphe , Hejblum Boris P. 
Published: April 2020

AbstractWe developed a multi-level model of the French COVID-19 epidemic at the regional level. We rely on a global extended Susceptible-Exposed-Infectious-Recovered (SEIR) mechanistic model as a simplified representation of the average epidemic process, with the addition of region specific random effects. Combining several French public datasets on the early dynamics of the epidemic, we estimate region-specific key parameters conditionally on this mechanistic model through Stochastic Approximation Expectation Maximization (SAEM) optimization usingMonolixsoftware. We thus estimate the basic reproductive numbers by region before lockdown (with a national average at 2.81 with 95% Confidence Interval [2.58; 3.07]), attack rates (i.e. percentages of infected people) over time per region which range between 1.9% and 9.9% as of May 11th, 2020, and the impact of nationwide lockdown on the infection rate which decreased the transmission rate by 76% towards reproductive numbers ranging from 0.63 to 0.73 at the end of lockdown across regions. These results confirm the low population immunity, the strong effect of the lockdown on the dynamics of the epidemics and the need for further intervention when lifting the lockdown to avoid an epidemic rebound.
Risk of COVID-19 and long-term exposure to air pollution: evidence from the first wave in China
Authors: Zheng Pai , Liu Yonghong , Song Hongbin , Wu Chieh-Hsi , Li Bingying , Kraemer Moritz U.G. , Tian Huaiyu , Yan Xing , Zheng Yuxin , Stenseth Nils Chr. , Dye Christopher , Jia Guang 
Published: April 2020

AbstractPeople with chronic obstructive pulmonary disease, cardiovascular disease or hypertension have a high risk of severe coronavirus disease 2019 (COVID-19). Long-term exposure to air pollution, especially PM2.5, has also been associated with COVID-19 mortality. We collated individual-level data of confirmed COVID-19 cases during the first wave of the epidemic in mainland China. We fitted a generalized linear model using city-level COVID-19 cases and severe cases as the outcome, and long-term average levels of air pollutants as the exposure. Our analysis was adjusted using several variables, including a mobile phone dataset, covering human movement from Wuhan before the travel ban and movements within each city during the time of emergency response. Other variables included census, smoking prevalence, climate, and socio-economic data from 324 cities in China. We adjusted for human mobility and socio-economic factors, and found that an increase in long-term NO2 or PM2.5 may correspond to an increase in the number of COVID-19 cases and severe infections. However, the linkage might also be affected by the confounding factor of population size because of the predefined correlation between population size and air pollution. The results are derived from a large, newly compiled and geocoded repository of population and epidemiological data relevant to COVID-19. The findings of this paper (and other previous studies that have given ambiguous results) indicate that a more definitive analysis is needed of the link between COVID-19 and air pollution.
Prediction of the COVID-19 Epidemic Trends Based on SEIR and AI Models
Authors: Feng Shuo , Feng Zebang , Ling Chen , Chang Chen , Feng Zhongke 
Published: April 2020

AbstractThe outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. To contain its spread, China adopted unprecedented nationwide interventions on January 23. We sought to show how these control measures impacted the containment of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23, 2020, was indispensable in reducing the eventual COVID-19 epidemic size.
Comparison of videolaryngoscope & direct laryngoscope for intubation in critically ill patients: A Bayesian meta-analysis
Authors: Maitra Souvik , Som Anirban , Bhattacharjee Sulagna 
Published: April 2020

AbstractPurposeTo identify the benefit of video laryngoscope (VL) over direct laryngoscope (DL) for intubation in the intensive care unit (ICU)Material & MethodsRandomized controlled trials (RCTs) comparing VL with DL for intubation in ICU by was conducted in conventional frequentist methodology and also incorporated of the previous evidences from observational studies in Bayesian methodology.ResultsData of 1464 patients from six RCTs have been included in this meta-analysis. In conventional meta-analysis of RCTs, first attempt intubation success rate was similar between VL and DL group [p=0.39]. Rate of esophageal intubation was significantly less with VL [p=0.03] and glottic visualization was significantly improved with VL in comparison to DL [p=0.009]. Time to intubation was similar in both the group [p=0.48]. When evidences from a meta-analysis of observational studies incorporated in Bayesian model, first attempt intubation success is significantly higher with VL [posterior median log OR (95% credible interval) 0.50 (0.06, 1.00)].ConclusionEvidences from both observational studies and RCTs synthesized in Bayesian methodology suggest that use of VL for endotracheal intubation in critically patients may be associated with higher first intubation success when compared to DL.
Methylphenidate use and restorative treatment in 13,000 young adults with ADHD
Authors: Schermann Haggai , Schiffmann Nathan , Ankory Ran , Shlaifer Amir , Yavnai Nirit , Yoffe Victoria , Natapov Lena 
Published: April 2020

AbstractObjectiveTo assess a dose-response effect of methylphenidate use on restorative treatment needs, which served as an indicator of caries.Subjects and MethodsThis study is a retrospective cohort of military recruits aged 18-25 who served for 12 to 48 months between 2005 and 2017. The cohort included 6,875 subjects with ADHD who received treatment with methylphenidate, 6,729 subjects with ADHD who had no prescriptions for methylphenidate, and 200,000 healthy controls. The outcome was normative treatment needs: having at least one recommendation for restorative treatment during the study period.ResultsFrequency of recommendation for restorative treatment among the treated, the untreated and the control groups was 24%, 22% and 17%, respectively (p<0.0001). On multivariate analysis, the dose-response association between methylphenidate use and the odds of having at least one restorative treatment was confirmed (OR=1.006 for each additional 1 gr of methylphenidate; 95% CI [1.004:1.009]).ConclusionsSubjects with ADHD who receive chronic treatment with methylphenidate have higher restorative treatment needs than subjects with untreated ADHD and healthy controls. Our results show that chronic methylphenidate medication among young adults leads to an elevated need for restorative treatment and implies a significant impact on oral health.
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
Authors: Jelodar Hamed , Wang Yongli , Orji Rita , Huang Hucheng 
Published: April 2020

AbstractInternet 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.
DeepViral: infectious disease phenotypes improve prediction of novel virus–host interactions
Authors: Liu-Wei Wang , Kafkas Şenay , Chen Jun , Dimonaco Nicholas , Tegnér Jesper , Hoehndorf Robert 
Published: April 2020

AbstractMotivationInfectious diseases from novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts.ResultsWe developed DeepViral, a deep learning based method that predicts protein–protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses.Availabilityhttps://github.com/bio-ontology-research-group/DeepViralContactrobert.hoehndorf@kaust.edu.sa
Genomic, geographic and temporal distributions of SARS-CoV-2 mutations
Authors: Yang Hsin-Chou , Chen Chun-houh , Wang Jen-Hung , Liao Hsiao-Chi , Yang Chih-Ting , Chen Chia-Wei , Lin Yin-Chun , Kao Chiun-How , Liao James C. 
Published: April 2020

AbstractThe COVID-19 pandemic is the most significant public health issue in recent history. Its causal agent, SARS-CoV-2, has evolved rapidly since its first emergence in December 2019. Mutations in the viral genome have critical impacts on the adaptation of viral strains to the local environment, and may alter the characteristics of viral transmission, disease manifestation, and the efficacy of treatment and vaccination. Using the complete sequences of 1,932 SARS-CoV-2 genomes, we examined the genomic, geographic and temporal distributions of aged, new, and frequent mutations of SARS-CoV-2, and identified six phylogenetic clusters of the strains, which also exhibit a geographic preference in different continents. Mutations in the form of single nucleotide variations (SNVs) provide a direct interpretation for the six phylogenetic clusters. Linkage disequilibrium, haplotype structure, evolutionary process, global distribution of mutations unveiled a sketch of the mutational history. Additionally, we found a positive correlation between the average mutation count and case fatality, and this correlation had strengthened with time, suggesting an important role of SNVs on disease outcomes. This study suggests that SNVs may become an important consideration in virus detection, clinical treatment, drug design, and vaccine development to avoid target shifting, and that continued isolation and sequencing is a crucial component in the fight against this pandemic.Significance StatementMutation is the driving force of evolution for viruses like SARS-CoV-2, the causal agent of COVID-19. In this study, we discovered that the genome of SARS-CoV-2 is changing rapidly from the originally isolated form. These mutations have been spreading around the world and caused more than 2.5 million of infected cases and 170 thousands of deaths. We found that fourteen frequent mutations identified in this study can characterize the six main clusters of SARS-CoV-2 strains. In addition, we found the mutation burden is positively correlated with the fatality of COVID-19 patients. Understanding mutations in the SARS-CoV-2 genome will provide useful insight for the design of treatment and vaccination.