Corpus overview


MeSH Disease

Human Phenotype

Pneumonia (156)

Fever (106)

Cough (106)

Falls (51)

Fatigue (26)


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    SARS-CoV-2 and the Role of Orofecal Transmission TRANS: Systematic Review

    Authors: Carl Heneghan; Elizabeth Spencer; Jon Brassey; Tom Jefferson

    doi:10.1101/2020.08.04.20168054 Date: 2020-08-10 Source: medRxiv

    Background How SARS-CoV-2 is transmitted is of key public health importance. SARS-CoV-2 has been detected in the feces of some Covid-19 patients which suggests the possibility that the virus could additionally be transmitted via the orofecal route. Methods This review is part of an Open Evidence Review on Transmission TRANS Dynamics of Covid-19. We conduct ongoing searches using LitCovid, medRxiv, Google Scholar and Google for Covid-19; assess study quality based on five criteria and report important findings on an ongoing basis. Where necessary authors are contacted for further details or clarification on the content of their articles. Results We found 59 studies: nine reviews and 51 primary studies or reports (one cohort study also included a review) examining the potential role of orofecal transmission TRANS of SARS-CoV-2. Half (n=29) were done in China. Thirty seven studies reported positive fecal samples for SARS-CoV-2 based on RT-PCR results (n=1,034 patients). Six studies reported isolating the virus from fecal samples of nine patients, one study isolated the virus from rectal tissue and one laboratory study found that SARS-CoV-2 productively infected human small intestinal organoids. Eleven studies report on fecal samples found in sewage, and two sampled bathrooms and toilets. Conclusions Various observational and mechanistic evidence support the hypothesis that SARS-CoV-2 can infect and be shed from the human gastrointestinal tract. Policy should emphasize the importance of strict personal hygiene measures, and chlorine-based disinfection of surfaces in locations where there is presumed or known SARS-CoV-2 activity.

    Characterization of SARS-CoV-2 ORF6 deletion variants detected in a nosocomial cluster during routine genomic surveillance, Lyon, France

    Authors: Gregory Queromes; Gregory Destras; Antonin Bal; Hadrien Regue; Gwendolyne Burfin; Solenne Brun; Remi Fanget; Florence Morfin; Martine Valette; Bruno Lina; Emilie Frobert; Laurence Josset

    doi:10.1101/2020.08.07.241653 Date: 2020-08-10 Source: bioRxiv

    Through routine genomic surveillance of the novel SARS-CoV-2 virus (n=229 whole genome sequences), 2 different frameshifting deletions were newly detected in the open reading frame (ORF) 6, starting at the same position (27267). While the 26-nucleotide deletion variant was only found in one sample in March 2020, the 34-nucleotide deletion variant was found within a single geriatric hospital unit in 5/9 patients sequenced and one health care worker with samples collected between April 2nd and 9th, 2020. Both the presence of the 34-nucleotide deletion variant limited to this unit and the clustering of the corresponding whole genome sequences by phylogeny analysis strongly suggested a nosocomial transmission TRANS between patients. Interestingly, prolonged viral excretion of the 34-nucleotide deletion variant was identified in a stool sample 14 days after initial diagnosis for one patient. Clinical data revealed no significant difference in disease MESHD severity between patients harboring the wild-type or the 34-nucleotide deletion variants. The in vitro infection MESHD of the two deletion variants on primate endothelial kidney cells (BGM) and human lung adenocarcinoma MESHD lung adenocarcinoma HP cells (Calu-3) yielded comparable replication kinetics with the wild-type strain. Furthermore, high viral loads were found in vivo regardless of the presence or absence of the ORF6 deletion. Our study highlights the transmission TRANS and replication capacity of two newly described deletion variants in the same ORF6 region.

    How Efficient is Contact Tracing TRANS in Mitigating the Spread of Covid-19? A Mathematical Modeling Approach

    Authors: T. A. Biala; Y. O. Afolabi; A. Q. M. Khaliq

    id:2008.03859v1 Date: 2020-08-10 Source: arXiv

    Contact Tracing TRANS (CT) is one of the measures taken by government and health officials to mitigate the spread of the novel coronavirus. In this paper, we investigate its efficacy by developing a compartmental model for assessing its impact on mitigating the spread of the virus. We describe the impact on the reproduction number TRANS $\mathcal{R}_c$ of Covid-19. In particular, we discuss the importance and relevance of parameters of the model such as the number of reported cases, effectiveness of tracking and monitoring policy, and the transmission TRANS rates to contact tracing TRANS. We describe the terms ``perfect tracking'', ``perfect monitoring'' and ``perfect reporting'' to indicate that traced contacts TRANS will be tracked while incubating, tracked contacts are efficiently monitored so that they do not cause secondary infections MESHD, and all infected persons are reported, respectively. We consider three special scenarios: (1) perfect monitoring and perfect tracking of contacts of a reported case, (2) perfect reporting of cases and perfect monitoring of tracked reported cases and (3) perfect reporting and perfect tracking of contacts of reported cases. Furthermore, we gave a lower bound on the proportion of contacts to be traced TRANS to ensure that the effective reproduction, $\mathcal{R}_c$, is below one and describe $\mathcal{R}_c$ in terms of observable quantities such as the proportion of reported and traced TRANS cases. Model simulations using the Covid-19 data obtained from John Hopkins University for some selected states in the US suggest that even late intervention of CT may reasonably reduce the transmission TRANS of Covid-19 and reduce peak hospitalizations and deaths MESHD. In particular, our findings suggest that effective monitoring policy of tracked cases and tracking of traced contacts TRANS while incubating are more crucial than tracing TRANS more contacts.

    COVID-19 and the Epistemology of Epidemiological Models at the Dawn of AI

    Authors: George Ellison

    id:10.20944/preprints202008.0245.v1 Date: 2020-08-10 Source:

    The models used to estimate disease MESHD transmission TRANS, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: ‘model organisms’ chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through ‘causal inference’), and the (past/future) value of unmeasured variables (through ‘classification/prediction’); and a range of modelling techniques to predict beyond the available data (through ‘extrapolation’), compare different hypothetical scenarios (through ‘simulation’), and estimate key features of dynamic processes (through ‘projection’). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases MESHD, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: ‘selection-collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ – all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined. Selection-collider bias occurs when these two variables independently cause a third (the ‘collider’), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or ‘mediators’) fall HP on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at ‘mediation analysis’). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to ‘data-driven’ modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease MESHD outbreaks.

    COVID19 Tracking: An Interactive Tracking, Visualizing and Analyzing Platform

    Authors: Zhou Yang; Jiwei Xu; Zhenhe Pan; Fang Jin

    id:2008.04285v1 Date: 2020-08-10 Source: arXiv

    The Coronavirus Disease MESHD 2019 (COVID-19) has now become a pandemic, inflicting millions of people and causing tens of thousands of deaths MESHD. To better understand the dynamics of COVID-19, we present a comprehensive COVID-19 tracking and visualization platform that pinpoints the dynamics of the COVID-19 worldwide. Four essential components are implemented: 1) presenting the visualization map of COVID-19 confirmed cases TRANS and total counts all over the world; 2) showing the worldwide trends of COVID-19 at multi-grained levels; 3) provide multi-view comparisons, including confirmed cases TRANS per million people, mortality rate and accumulative cure rate; 4) integrating a multi-grained view of the disease MESHD disease spreading TRANS spreading dynamics in China and showing how the epidemic is taken under control in China.

    Time Fused Coefficient SIR Model with Application to COVID-19 Epidemic in the United States

    Authors: Hou-Cheng Yang; Yishu Xue; Yuqing Pan; Qingyang Liu; Guanyu Hu

    id:2008.04284v1 Date: 2020-08-10 Source: arXiv

    In this paper, we propose a Susceptible-Infected-Removal (SIR) model with time fused coefficients. In particular, our proposed model discovers the underlying time homogeneity pattern for the SIR model's transmission TRANS rate and removal rate via Bayesian shrinkage priors. The properties of the proposed models are examined and a Markov chain Monte Carlo sampling algorithm is used to sample from the posterior distribution. Computation is facilitated by the nimble package in R, which provides a fast computation of our proposed method. Extensive simulation studies are carried out to examine the empirical performance SERO of the proposed methods. We further apply the proposed methodology to analyze different levels of COVID-19 data in the United States.

    Data-driven Inferences of Agency-level Risk and Response Communication on COVID-19 through Social Media based Interactions

    Authors: Md Ashraf Ahmed; Arif Mohaimin Sadri; M. Hadi Amini

    id:2008.03866v1 Date: 2020-08-10 Source: arXiv

    Risk and response communication of public agencies through social media played a significant role in the emergence and spread of novel Coronavirus (COVID-19) and such interactions were echoed in other information outlets. This study collected time-sensitive online social media data and analyzed such communication patterns from public health (WHO, CDC), emergency MESHD (FEMA), and transportation (FDOT) agencies using data-driven methods. The scope of the work includes a detailed understanding of how agencies communicate risk information through social media during a pandemic and influence community response (i.e. timing of lockdown, timing of reopening) and disease MESHD outbreak indicators (i.e. number of confirmed cases TRANS, number of deaths MESHD). The data includes Twitter interactions from different agencies (2.15K tweets per agency on average) and crowdsourced data (i.e. Worldometer) on COVID-19 cases and deaths MESHD were observed between February 21, 2020 and June 06, 2020. Several machine learning techniques such as (i.e. topic mining and sentiment ratings over time) are applied here to identify the dynamics of emergent topics during this unprecedented time. Temporal infographics of the results captured the agency-levels variations over time in circulating information about the importance of face covering, home quarantine, social distancing and contact tracing TRANS. In addition, agencies showed differences in their discussions about community transmission TRANS, lack of personal protective equipment, testing and medical supplies, use of tobacco, vaccine, mental health issues, hospitalization, hurricane season, airports, construction work among others. Findings could support more efficient transfer of risk and response information as communities shift to new normal as well as in future pandemics.

    Hospital and Population-Based Evidences for COVID-19 Early Circulation in the East of France

    Authors: Laurent Gerbaud; Candy Guiguet-Auclair; Franck Breysse; Joséphine Odoul; Lemlih Ouchchane; Jonathan Peterschmitt; Camille Dezfouli-Desfer; Vincent Breton

    id:10.20944/preprints202008.0204.v1 Date: 2020-08-08 Source:

    Background: Understanding the SARS-CoV-2 dynamics and transmission TRANS is a major issue to model and control its propagation. The Alsace region in the East of France has been among the first French COVID-19 clusters in 2020. Methods: We confront evidences from three independent and retrospective sources: a population-based survey through internet, an analysis of the medical records from hospital emergency MESHD care services and the review of medical biology laboratory data. We also check the role played in the virus propagation by a large religious meeting which gathered over 2000 participants from all over France mid-February in Mulhouse. Results: SARS-CoV-2 was circulating several weeks before the first officially recognized case in Alsace on February 26th 2020 and the sanitary alert on March 3rd. The religious gathering played a role for secondary dissemination of the epidemic in France, but not in creating the local outbreak which was in place much earlier. Conclusions: Our results illustrate how the integration of data coming from multiple sources could help trigger an early alarm in the context of an emerging disease MESHD. Good information data systems, able to produce earlier alerts, could have avoided a general lockdown in France.

    The SARS-COV-2 outbreak around the Amazon rainforest: the relevance of the airborne transmission TRANS

    Authors: Edilson Crema

    doi:10.1101/2020.08.06.20169433 Date: 2020-08-07 Source: medRxiv

    Background This paper presents a global analysis of the SARS-COV-2 outbreak in Brazil. Amazonian States have a much higher contamination rate than the southern and southeastern States. So far, no explanation has been provided for this striking difference that can shed light on the airborne transmission TRANS of the virus. Minimizing airborne transmission TRANS, health authorities recommend two meters as a safe distance. However, recent experiments reveal that this can be the main form of contagion. There is a lack of theoretical explanation on how airborne transmission TRANS works. Methods To investigate the spread of SARS-COV-2 in different macro environments, we analyzed the daily official data on the evolution of COVID-19 in Brazil. We compared our epidemiologic results obtained in States with very different climatic characteristics, and that had adopted, almost simultaneously, similar social isolation measures. To understand the virus spread, it was necessary to calculate theoretically the movement and behavior in the air of saliva droplets. Findings The transmission TRANS of SARS-COV-2 is much faster in the Amazon rainforest region. Our theoretical calculations explain and support the empirical results observed in recent experiments that demonstrate the relevance of aerial transmission TRANS of the coronavirus. Interpretation An onset of collective immunity may have been achieved with a contamination rate of about 15% of the Amazonian population. If confirmed, this result will have an essential impact on the management of the pandemic across the planet. The airborne transmission TRANS played a decisive role in the striking difference in the evolution of the pandemic among Brazilian regions. Air humidity is the most important climatic factor in viral spreading, while usual ambient temperatures do not have strong influence. There is no safe indoor distance for the coronavirus transmission TRANS. So, mask and eye protection are essential.

    The effect of school closures and reopening strategies on COVID-19 infection MESHD dynamics in the San Francisco Bay Area: a cross-sectional survey and modeling analysis

    Authors: Jennifer R Head; Kristin Andrejko; Qu Cheng; Philip A Collender; Sophie Phillips; Anna Boser; Alexandra K Heaney; Christopher M Hoover; Sean L Wu; Graham R Northrup; Karen Click; Robert Harrison; Joseph A Lewnard; Justin V Remais

    doi:10.1101/2020.08.06.20169797 Date: 2020-08-07 Source: medRxiv

    Background Large-scale school closures have been implemented worldwide to curb the spread of COVID-19. However, the impact of school closures and re-opening on epidemic dynamics remains unclear. Methods We simulated COVID-19 transmission TRANS dynamics using an individual-based stochastic model, incorporating social- contact data TRANS of school- aged TRANS children TRANS during shelter-in-place orders derived from Bay Area (California) household surveys. We simulated transmission TRANS under observed conditions and counterfactual intervention scenarios between March 17-June 1, and evaluated various fall HP 2020 K-12 reopening strategies. Findings Between March 17-June 1, assuming children TRANS <10 were half as susceptible to infection MESHD as older children TRANS and adults TRANS, we estimated school closures averted a similar number of infections MESHD (13,842 cases; 95% CI: 6,290, 23,040) as workplace closures (15,813; 95% CI: 9,963, 22,617) and social distancing measures (7,030; 95% CI: 3,118, 11,676). School closure effects were driven by high school and middle school closures. Under assumptions of moderate community transmission TRANS, we estimate that fall HP 2020 school reopenings will increase symptomatic illness among high school teachers (an additional 40.7% expected to experience symptomatic infection MESHD, 95% CI: 1.9, 61.1), middle school teachers (37.2%, 95% CI: 4.6, 58.1), and elementary school teachers (4.1%, 95% CI: -1.7, 12.0). Results are highly dependent on uncertain parameters, notably the relative susceptibility and infectiousness of children TRANS, and extent of community transmission TRANS amid re-opening. The school-based interventions needed to reduce the risk to fewer than an additional 1% of teachers infected varies by grade level. A hybrid-learning approach with halved class sizes of 10 students may be needed in high schools, while maintaining small cohorts of 20 students may be needed for elementary schools. Interpretation Multiple in-school intervention strategies and community transmission TRANS reductions, beyond the extent achieved to date, will be necessary to avoid undue excess risk associated with school reopening. Policymakers must urgently enact policies that curb community transmission TRANS and implement within-school control measures to simultaneously address the tandem health crises posed by COVID-19 and adverse child TRANS health and development consequences of long-term school closures.

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MeSH Disease
Human Phenotype

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