Corpus overview


MeSH Disease

Human Phenotype

Pneumonia (158)

Fever (109)

Cough (109)

Falls (51)

Fatigue (27)


    displaying 11 - 20 records in total 2564
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    A Comprehensive Classification of Coronaviruses and Inferred Cross-Host Transmissions TRANS

    Authors: Yiran Fu; Marco Pistolozzi; Xiaofeng Yang; Zhanglin Lin

    doi:10.1101/2020.08.11.232520 Date: 2020-08-11 Source: bioRxiv

    In this work, we present a unified and robust classification scheme for coronaviruses based on concatenated protein clusters. This subsequently allowed us to infer the apparent 'horizontal gene transfer' events via reconciliation with the corresponding gene trees, which we argue can serve as a marker for cross-host transmissions TRANS. The cases of SARS-CoV, MERS-CoV, and SARS-CoV-2 are discussed. Our study provides a possible technical route to understand how coronaviruses evolve and are transmitted to humans.

    Compound risks of hurricane evacuation amid the COVID-19 pandemic in the United States

    Authors: Sen Pei; Kristina A. Dahl; Teresa K. Yamana; Rachel Licker; Jeffrey Shaman

    doi:10.1101/2020.08.07.20170555 Date: 2020-08-11 Source: medRxiv

    Current projections and unprecedented storm activity to date suggest the 2020 Atlantic hurricane season will be extremely active and that a major hurricane could make landfall during the global COVID-19 pandemic. Such an event would necessitate a large-scale evacuation, with implications for the trajectory of the pandemic. Here we model how a hypothetical hurricane evacuation from four counties in southeast Florida would affect COVID-19 case levels. We find that hurricane evacuation increases the total number of COVID-19 cases in both origin and destination locations; however, if transmission TRANS rates in destination counties can be kept from rising during evacuation, excess evacuation-induced case numbers can be minimized by directing evacuees to counties experiencing lower COVID-19 transmission TRANS rates. Ultimately, the number of excess COVID-19 cases produced by the evacuation depends on the ability of destination counties to meet evacuee needs while minimizing virus exposure through public health directives.

    A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia MESHD pneumonia HP in China

    Authors: Dawei Yang; Tao Xu; Xun Wang; Deng Chen; Ziqiang Zhang; Lichuan Zhang; Jie Liu; Kui Xiao; Li Bai; Yong Zhang; Lin Zhao; Lin Tong; Chaomin Wu; Yaoli Wang; Chunling Dong; Maosong Ye; Yu Xu; Zhenju Song; Hong Chen; Jing Li; Jiwei Wang; Fei Tan; Hai Yu; Jian Zhou; Jinming Yu; Chunhua Du; Hongqing Zhao; Yu Shang; Linian Huang; Jianping Zhao; Yang Jin; Charles A. Powell; Yuanlin Song; Chunxue Bai

    doi:10.1101/2020.08.07.20163402 Date: 2020-08-11 Source: medRxiv

    Background The outbreak of coronavirus disease MESHD 2019 (COVID-19) has become a global pandemic acute infectious disease MESHD, especially with the features of possible asymptomatic TRANS carriers TRANS and high contagiousness. It causes acute respiratory distress HP syndrome MESHD and results in a high mortality rate if pneumonia MESHD pneumonia HP is involved. Currently, it is difficult to quickly identify asymptomatic TRANS cases or COVID-19 patients with pneumonia MESHD pneumonia HP due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans, which facilitates the spread of the disease TRANS disease MESHD at the community level, and contributes to the overwhelming of medical resources in intensive care units. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist global frontline doctors to efficiently and precisely diagnose asymptomatic TRANS COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan Unversity (approval number B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. These patients had SARS-CoV-2 RT-PCR test results and chest CT scans, both of which were used as the gold standard for the diagnosis of COVID-19 and COVID-19 pneumonia MESHD pneumonia HP. In particular, the dataset included 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, and 122 asymptomatic TRANS cases who had positive RT-PCR test results, amongst whom 31 cases were diagnosed. We also integrated the function of a survey in nCapp to collect user feedback from frontline doctors. Findings We applied the statistical method of a multi-factor regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are fast and accessible: 'Residing or visiting history in epidemic regions', 'Exposure history to COVID-19 patient', 'Dry cough MESHD cough HP', ' Fatigue MESHD Fatigue HP', 'Breathlessness', 'No body temperature decrease after antibiotic treatment', 'Fingertip blood SERO oxygen saturation<=93%', ' Lymphopenia MESHD Lymphopenia HP', and 'C-reactive protein (CRP) increased'. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). To ensure the sensitivity SERO of the model, we used a cutoff value of 0.09. The sensitivity SERO and specificity of the model were 98.0% (95% CI: 96.9%, 99.1%) and 17.3% (95% CI: 15.0%, 19.6%), respectively, in the training dataset, and 96.5% (95% CI: 95.1%, 98.0%) and 18.8% (95% CI: 16.4%, 21.2%), respectively, in the validation dataset. In the subset of the 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, the model predicted 132 cases, accounting for 96.4% (95% CI: 91.7%, 98.8%) of the cases. In the subset of the 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, the model predicted 59 cases, accounting for 95.2% (95% CI: 86.5%, 99.0%) of the cases. Considering the specificity of the model, we used a cutoff value of 0.32. The sensitivity SERO and specificity of the model were 83.5% (95% CI: 80.5%, 86.4%) and 83.2% (95% CI: 80.9%, 85.5%), respectively, in the training dataset, and 79.6% (95% CI: 76.4%, 82.8%) and 81.3% (95% CI: 78.9%, 83.7%), respectively, in the validation dataset, which is very close to the published AI model. The results of the online survey 'Questionnaire Star' showed that 90.9% of nCapp users in WeChat mini programs were 'satisfied' or 'very satisfied' with the tool. The WeChat mini program received a significantly higher satisfaction rate than other platforms, especially for 'availability and sharing convenience of the App' and 'fast speed of log-in and data entry'. Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic TRANS patients and avoid misdiagnoses of cases with false-negative RT-PCR results. These patients require timely isolation or close medical supervision. By applying the model, medical resources can be allocated more reasonably, and missed diagnoses can be reduced. In addition, further education and interaction among medical professionals can improve the diagnostic efficiency for COVID-19, thus avoiding the transmission TRANS of the disease from asymptomatic MESHD asymptomatic TRANS patients at the community level.

    Clustering of age TRANS standardised COVID-19 infection MESHD fatality ratios and death MESHD trajectories

    Authors: Thu-Lan Kelly; Greer Humphrey; Caroline Miller; Jacqueline A Bowden; Joanne Dono; Paddy A Phillips

    doi:10.1101/2020.08.11.20172478 Date: 2020-08-11 Source: medRxiv

    Background An accurate measure of the impact of COVID-19 is the infection MESHD fatality ratio, or the proportion of deaths MESHD among those infected, which does not depend on variable testing rates between nations. The risk of mortality from COVID-19 depends strongly on age TRANS and current estimates of the infection MESHD fatality ratio do not account for differences in national age TRANS profiles. Comparisons of cumulative death MESHD trajectories allow the effect and timing of public health interventions to be assessed. Our purpose is to (1) determine whether countries are clustered according to infection MESHD fatality ratios and (2) compare interventions to slow the spread of the disease TRANS disease MESHD by clustering death MESHD trajectories. Methods National age TRANS standardised infection MESHD fatality ratios were derived from age TRANS stratified estimates from China and population estimates from the World Health Organisation. The IFRs were clustered into groups using Gaussian mixture models. Trajectory analysis clustered cumulative death MESHD rates in two time windows, 50 and 100 days after the first reported death MESHD. Findings Infection MESHD fatality ratios from 201 nations were clustered into three groups: young, medium and older, with corresponding means (SD) of 0.20% (0.03%), 0.38% (0.11%) and 0.93% (0.21%). At 50 and 100 days after the first reported death MESHD, there were two clusters of cumulative death MESHD trajectories from 113 nations with at least 25 deaths MESHD reported at 100 days. The first group had slowly increasing or stable cumulative death MESHD rates, while the second group had accelerating rates at the end of the time window. Fifty-two nations changed group membership between the time windows. Conclusion A cluster of younger nations have a lower estimated infection MESHD fatality ratio than older nations. The effect and timing of public health interventions in preventing the spread of the disease TRANS disease MESHD can be tracked by clustering death MESHD rate trajectories into stable or accelerating and comparing changes over time.

    Effective reproduction number TRANS for COVID-19 in Aotearoa New Zealand

    Authors: Rachelle N Binny; Audrey Lustig; Ann Brower; Shaun C Hendy; Alex James; Matthew Parry; Michael J Plank; Nicholas Steyn

    doi:10.1101/2020.08.10.20172320 Date: 2020-08-11 Source: medRxiv

    The effective reproduction number TRANS, Reff, is the average number of secondary cases TRANS infected by a primary case TRANS, a key measure of the transmission TRANS potential for a disease MESHD. Compared to many countries, New Zealand has had relatively few COVID-19 cases, many of which were caused by infections MESHD acquired overseas. This makes it difficult to use standard methods to estimate Reff. In this work, we use a stochastic model to simulate COVID-19 spread in New Zealand and report the values of Reff from simulations that gave best fit to case data. We estimate that New Zealand had an effective reproduction number TRANS Reff = 1.8 for COVID-19 transmission TRANS prior to moving into Alert Level 4 on March 25 2020 and that after moving into Alert level 4 this was reduced to Reff = 0.35. Our estimate Reff = 1.8 for reproduction number TRANS before Alert Level 4, is relatively low compared to other countries. This could be due, in part, to measures put in place in early- to mid-March, including: the cancellation of mass gatherings, the isolation of international arrivals, and employees being encouraged to work from home.

    The COVID-19 Early Detection in Doctors and Healthcare Workers (CEDiD) Study: study protocol for a prospective observational trial

    Authors: Alexander Zargaran; Dina Radenkovic; Chelsea Trengrove; Gill Arbane; Kariem El-Boghdadly; Rocio Teresa Martinez-Nunez; Anne Greenough

    doi:10.1101/2020.08.11.20172502 Date: 2020-08-11 Source: medRxiv

    Background: The global COVID-19 pandemic has caused worldwide disruption with its exponential spread mandating national and international lockdown measures. Hospital-associated transmission TRANS has been identified as a major factor in the perpetuation of COVID-19, with healthcare workers at high-risk of becoming infected with SARS-CoV-2 and representing important vectors for spread, but not routinely having their clinical observations monitored or being tested for COVID-19. Methods: A single-center, prospective observational study of 60 healthcare workers will explore how many healthcare workers in high-risk areas develop COVID-19 infection MESHD over a thirty day period. High-risk areas are defined as COVID positive wards, the intensive care unit or the accident and emergency MESHD department. Healthcare workers (HCWs) will be recruited and have daily self-administered nasopharyngeal SARS-CoV-2 PCR tests. They will also be provided with a wearable medical device to measure their clinical observations during non-working hours, and be asked to complete a daily self-reported symptom questionnaire over the study period. Statistical analysis will assess the proportion of healthcare workers who develop COVID-19 infection MESHD as a primary objective, with secondary objectives exploring what symptoms are developed, time-to-event, and deviations in clinical observations. Discussion: At present clinical observations, symptoms and COVID-19 PCR swabs are not routinely undertaken for healthcare workers. If the CEDiD (COVID-19 Early Detection in Doctors and Healthcare Workers) study is successful, it will provide useful information for workforce decisions in reducing hospital-associated transmission TRANS of COVID-19. The data will help in determining whether there are early warning signs for development of COVID-19 infections MESHD amongst healthcare workers and may contribute to the evidence base advocating for more regular testing of healthcare workers observations, symptoms and COVID-19 status. Trial registration, NCT04363489. Registered on 27th July 2020

    Application of Optimal Control to Long Term Dynamics of COVID-19 Disease MESHD in South Africa

    Authors: Farai Nyabadza; Williams Chukwu; Faraimunashe Chirove; fatmawati fatmawati; Princess Gatyeni

    doi:10.1101/2020.08.10.20172049 Date: 2020-08-11 Source: medRxiv

    SARS-CoV-2 (COVID-19) belongs to the beta-coronavirus family, these include; the severe acute respiratory syndrome MESHD coronavirus (SARS-CoV) and the Middle East respiratory syndrome MESHD coronavirus (MERS-CoV). Since its resurgence in South Africa in March 2020, it has lead to high mortality and thousands of people contracting the virus. In this study, we use a set of five differential equations to analyse the effects on long term dynamics of COVID-19 pandemic with optimal control measures. Mathematical analyses of the model without control were done and the basic reproduction number TRANS ( R0 TRANS) of the COVID-19 for the South African epidemic determined. The model steady states were also determined, and their analyses presented based on R0 TRANS: We introduced permissible control measures and formulated an optimal control problem using the Pontraygain Maximum Principle. Our numerical findings suggest that joint implementation of effective mask usage, physical distancing and active screening and testing are effective measures to curtail the spread of the disease TRANS disease on undiagnosed MESHD humans. The results obtained in this paper are of public health importance in the control and management of the spread for the novel coronavirus, SARS-CoV-2, in South Africa.

    Probability of elimination for COVID-19 in Aotearoa New Zealand

    Authors: Rachelle N Binny; Shaun C Hendy; Alex James; Audrey Lustig; Michael J Plank; Nicholas Steyn

    doi:10.1101/2020.08.10.20172361 Date: 2020-08-11 Source: medRxiv

    On 25th March 2020, New Zealand implemented stringent lockdown measures (Alert Level 4, in a four-level alert system) with the goal of eliminating community transmission TRANS of COVID-19. Once new cases are no longer detected over consecutive days, the probability of elimination is an important measure for informing decisions on when certain COVID-19 restrictions should be relaxed. Our model of COVID-19 spread in New Zealand estimates that after 2-3 weeks of no new reported cases, there is a 95% probability that COVID-19 has been eliminated. We assessed the sensitivity SERO of this estimate to varying model parameters, in particular to different likelihoods of detection of clinical cases and different levels of control effectiveness. Under an optimistic scenario with high detection of clinical cases, a 95% probability of elimination is achieved after 10 consecutive days with no new reported cases, while under a more pessimistic scenario with low case detection it is achieved after 22 days.

    Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis

    Authors: Garyfallos Konstantinoudis; Tullia Padellini; James E Bennett; Bethan Davies; Majid Ezzati; Marta Blangiardo

    doi:10.1101/2020.08.10.20171421 Date: 2020-08-11 Source: medRxiv

    Background: Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design, based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 deaths MESHD up to June 30, 2020 in England using high geographical resolution. Methods: We included 38 573 COVID-19 deaths MESHD up to June 30, 2020 at the Lower Layer Super Output Area level in England (n=32 844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014-2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. Findings: We find a 0.5% (95% credible interval: -0.2%-1.2%) and 1.4% (-2.1%-5.1%) increase in COVID-19 mortality rate for every 1g/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect of 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease TRANS disease MESHD during the first wave of the epidemic. Interpretation: Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain. Funding: Medical Research Council, Wellcome Trust, Environmental Protection Agency and National Institutes of Health.

    Efficacy of face coverings in reducing transmission TRANS of COVID-19: calculations based on models of droplet capture

    Authors: Joshua F. Robinson; Ioatzin Rios de Anda; Fergus Moore; Jonathan P. Reid; Richard P. Sear; C. Patrick Royall

    id:2008.04995v1 Date: 2020-08-11 Source: arXiv

    In the COVID--19 pandemic, among the more controversial issues is the use of masks and face coverings. Much of the concern boils down to the question -- just how effective are face coverings? One means to address this question is to review our understanding of the physical mechanisms by which masks and coverings operate -- steric interception, inertial impaction, diffusion and electrostatic capture. We enquire as to what extent these can be used to predict the efficacy of coverings. We combine the predictions of the models of these mechanisms which exist in the filtration literature and compare the predictions with recent experiments and lattice Boltzmann simulations, and find reasonable agreement with the former and good agreement with the latter. We build on these results to predict the utility of various materials from which masks are comprised, and predict their efficiency for removing particles of varying size. We make assumptions about the relative viral load of the respirable droplet size distribution to show that even simple cloth-based face coverings have the potential to significantly reduce the number of secondary infections TRANS infections MESHD per infected individual.

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MeSH Disease
Human Phenotype

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