Corpus overview


Overview

MeSH Disease

Human Phenotype

Pneumonia (36)

Fever (28)

Cough (22)

Hypertension (12)

Fatigue (8)


Transmission

Seroprevalence
    displaying 1 - 10 records in total 405
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    A Monte Carlo approach to model COVID-19 deaths MESHD and infections MESHD using Gompertz functions

    Authors: Tulio Rodrigues; Otaviano Helene

    id:2008.04989v1 Date: 2020-08-11 Source: arXiv

    This study describes the dynamics of COVID-19 deaths MESHD and infections MESHD via a Monte Carlo approach. The analyses include death MESHD's data from USA, Brazil, Mexico, UK, India and Russia, which comprise the four countries with the highest number of deaths MESHD/ confirmed cases TRANS, as of Aug 07, 2020, according to the WHO. The Gompertz functions were fitted to the data of weekly averaged confirmed deaths MESHD per day by mapping the $\chi^2$ values. The uncertainties, variances and covariances of the model parameters were calculated by propagation. The fitted functions for the average deaths MESHD per day for USA and India have an upward trend, with the former having a higher growth rate and quite huge uncertainties. For Mexico, UK and Russia, the fits are consistent with a slope down pattern. For Brazil we found a subtle trend down, but with significant uncertainties. The USA, UK and India data shown a first peak with a higher growth rate when compared to the second one, demonstrating the benefits of non-pharmaceutical interventions of sanitary measures and social distance flattening the curve. For USA, a third peak seems quite plausible, most likely related with the recent relaxation policies. Brazil's data are satisfactorily described by two highly overlapped Gompertz functions with similar growth rates, suggesting a two-steps process for the pandemic spreading. The 95% CI for the total number of deaths MESHD ($\times 10^3$) predicted by the model for Aug 31, 2020 are 160 to 220, 110 to 130, 59 to 62, 46.6 to 47.3, 54 to 63 and 16.0 to 16.7 for USA, Brazil, Mexico, UK, India and Russia, respectively. Our estimates for the prevalences SERO of infections MESHD are in reasonable agreement with some preliminary reports from serological studies carried out in USA and Brazil. The method represents an effective framework to estimate the line-shape of the infection MESHD curves and the uncertainties of the relevant parameters based on the actual data.

    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.

    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.

    The Multiple Impacts of the COVID-19: A Qualitative Perspective

    Authors: Muhamad KhairulBahri

    id:10.20944/preprints202005.0033.v2 Date: 2020-08-08 Source: Preprints.org

    The world has been highly impacted by the COVID-19 as the virus has spread to all continents – about 200 countries in total. The latest update claims about 4,000,000 confirmed cases TRANS and about 300,000 confirmed deaths MESHD owing to the COVID-19 pandemic. This probably makes the COVID-19 as the most dangerous contagious disease MESHD in the era 2000s. Apart from massive publications on this topic, there is no available qualitative analysis that describes the dynamic spreads of the COVID-19 and its impacts on healthcare and the economy. Through the system archetypes analysis, this paper explains that the dynamic spread of the COVID-19 consists of the limits to growth and the success to successful structures. The limits to growth elucidates that more symptomatic and asymptomatic TRANS patients owing to infected droplets may be bounded by self-healing and isolated treatments. The success to successful structure explains that once the COVID-19 affects the economy through the lockdown, there will be a limited fund to support the government aids and the aggregate demand. In overall, this paper gives readers simplified holistic insights into understanding the dynamic spread of the COVID-19.

    Estimating the Changing Infection MESHD Rate of COVID-19 Using Bayesian Models of Mobility

    Authors: Luyang Liu; Sharad Vikram; Junpeng Lao; Xue Ben; Alexander D'Amour; Shawn O'Banion; Mark Sandler; Rif A. Saurous; Matthew D. Hoffman

    doi:10.1101/2020.08.06.20169664 Date: 2020-08-07 Source: medRxiv

    In order to prepare for and control the continued spread of the COVID-19 pandemic while minimizing its economic impact, the world needs to be able to estimate and predict COVID-19's spread. Unfortunately, we cannot directly observe the prevalence SERO or growth rate of COVID-19; these must be inferred using some kind of model. We propose a hierarchical Bayesian extension to the classic susceptible-exposed-infected-removed (SEIR) compartmental model that adds compartments to account for isolation and death MESHD and allows the infection MESHD rate to vary as a function of both mobility data collected from mobile phones and a latent time-varying factor that accounts for changes in behavior not captured by mobility data. Since confirmed-case TRANS data is unreliable, we infer the model's parameters conditioned on deaths MESHD data. We replace the exponential-waiting-time assumption of classic compartmental models with Erlang distributions, which allows for a more realistic model of the long lag between exposure and death MESHD. The mobility data gives us a leading indicator that can quickly detect changes in the pandemic's local growth rate and forecast changes in death MESHD rates weeks ahead of time. This is an analysis of observational data, so any causal interpretations of the model's inferences should be treated as suggestive at best; nonetheless, the model's inferred relationship between different kinds of trips and the infection MESHD rate do suggest some possible hypotheses about what kinds of activities might contribute most to COVID-19's spread.

    Association of mental disorders with SARS-CoV-2 infection MESHD infection and severe HP and severe health outcomes: a nationwide cohort study

    Authors: Ha-Lim Jeon; Jun Soo Kwon; So-Hee Park; Ju-Young Shin

    doi:10.1101/2020.08.05.20169201 Date: 2020-08-07 Source: medRxiv

    Background: No epidemiological data exists for the association between mental disorders and the risk of severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) infection MESHD and coronavirus disease MESHD 2019 (COVID-19) severity. Aims: To evaluate the association between mental disorders and the risk of SARS-CoV-2 infection MESHD infection and severe HP and severe outcomes following COVID-19. Methods: We performed a cohort study using the Korean COVID-19 patient database based on the national health insurance data. Each patient with a mental or behavioral disorder (diagnosed during six months prior to the first SARS-CoV-2 test) was matched by age TRANS, sex, and Charlson comorbidity index with up to four patients without mental disorders. SARS-CoV-2 positivity risk and risk of death MESHD or severe events (intensive care unit admission, use of mechanical ventilation, and acute respiratory distress HP syndrome MESHD) post- infection MESHD were calculated using conditional logistic regression analysis. Results: Among 230,565 patients tested for SARS-CoV-2, 33,653 (14.6%) had mental disorders, 928/33,653 (2.76%) tested positive, and 56/928 (6.03%) died. In multivariate analysis with the matched cohort, there was no association between mental disorders and SARS-CoV-2 positivity risk (odds ratio [OR], 1.02; 95% confidence interval [CI], 0.92-1.12); however, a higher risk was associated with schizophrenia HP-related disorders (OR, 1.36; 95% CI, 1.02-1.81). Among confirmed cases TRANS, mortality risk significantly increased in patients with mental disorders (OR, 1.84, 95% CI, 1.07-3.15). Conclusion: Mental disorders are likely contributing factors of mortality following COVID-19. Although the infection MESHD infection risk TRANS infection risk TRANS risk did not increase in overall mental disorders, patients with schizophrenia HP-related disorders were more vulnerable to the infection MESHD.

    Epidemiology of Reopening in the COVID-19 Pandemic in the United States, Europe and Asia

    Authors: Weiqi Zhang; Alina Oltean; Scott Nichols; Fuad Odeh; Fei Zhong

    doi:10.1101/2020.08.05.20168757 Date: 2020-08-06 Source: medRxiv

    Since the discovery of the novel coronavirus (SARS-CoV-2), COVID-19 has become a global healthcare and economic crisis. The United States (US) and Europe exhibited wide impacts from the virus with more than six million cases by the time of our analysis. To inhibit spread, stay-at-home orders and other non-pharmaceutical interventions (NPIs) were instituted. Beginning late April 2020, some US states, European, and Asian countries lifted restrictions and started the reopening phases. In this study, the changes of confirmed cases TRANS, hospitalizations, and deaths MESHD were analyzed after reopening for 11 countries and 40 US states using an interrupted time series analysis. Additionally, the distribution of these categories was further analyzed by age TRANS due to the known increased risk in elderly TRANS patients. Reopening had varied effects on COVID-19 cases depending on the region. Recent increases in cases did not fully translate into increased deaths MESHD. Eight countries had increased cases after reopening while only two countries showed the same trend in deaths MESHD. In the US, 30 states had observed increases in cases while only seven observed increased deaths MESHD. In addition, we found that states with later reopening dates were more likely to have significant decreases in cases, hospitalizations, and deaths MESHD. Furthermore, age TRANS distributions through time were analyzed in relation to COVID-19 in the US. Younger age groups TRANS typically had an increased share of cases after reopening.

    Spatial Distribution and Trend Analysis of Current Status of COVID-19 in Nepal and Global Future Preventive Perspectives

    Authors: Ramesh Raj Pant; Buddha Bahadur Bahadur; Kiran Bishwakarma; Sudip Paudel; Nashib Pandey; Samir Kumar Adhikari; Kamal Ranabhat

    doi:10.21203/rs.3.rs-54139/v1 Date: 2020-08-05 Source: ResearchSquare

    Background: Coronavirus disease MESHD (COVID-19) is a recently discovered severe and contagious disease MESHD caused by severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) and has received worldwide attention. The risk of COVID-19 is serious for the infected persons of chronic diseases MESHD as well as vulnerable populations including elder group. Still, the present scenario of scarcity of effective treatment options and limited recovery rate of ongoing treatment are prevailed in Nepal. This study aims to analyze the spatial distribution and trends of COVID-19 with the help of geographic information system (GIS) software and outlook future preventive perspectives.Methods: In this research work, we used GIS tool ArcGIS 10.4.1 to map the distribution patterns of population of COVID-19 cases. Federal, provincial and district level daily cases data of COVID-19 confirmed, cured and death MESHD from 23rd January to 13th July 2020 were obtained from the Ministry of Health and Population (MoHP), Government of Nepal. In addition, we reviewed several peer-reviewed research papers to summarize the global scenarios and preventive perspectives on COVID-19.Results: In context to Nepal SARS-CoV-2 has spread throughout the country infecting 16,945 persons in all 77 districts, as of 13th July, 2020. Confirmed, cured and death MESHD cases experienced an upward trend up to 1st July, 2020 followed by downward trend as of 13th July, 2020. Over 70% of total confirmed cases TRANS of reported COVID-19 patients were from the lowland-plain area. Spatial clustering suggested that provinces 2 and 5 were at potentially increased risk of COVID-19, and Province 1 and Bagmati province could be grouped as future "hot spots". In addition, we proposed four strategies namely, identification of the key medical and social elements, discovery and development of treatment options for a future pandemic, investing in ethno-medicine research and epidemic preparedness of health care system to decrease the efficacy of calamities of future pandemics.Conclusion: Our study demonstrates one of the best ways to protect; control and sluggish transmission TRANS of SARS-CoV-2 could be achieved by monitoring active ties using GIS spatial analysis. And, the severity of future pandemic could be minimized by integrative action on the abovementioned four different preventive master plans.

    Clinical course and severity outcome indicators among COVID 19 hospitalized patients in relation to comorbidities distribution Mexican cohort

    Authors: Genny Carrillo; Nina Mendez Dominguez; Kassandra D Santos Zaldivar; Andrea Rochel Perez; Mario Azuela Morales; Osman Cuevas Koh; Alberto Alvarez Baeza

    doi:10.1101/2020.07.31.20165480 Date: 2020-08-04 Source: medRxiv

    Introduction: COVID-19 affected worldwide, causing to date, around 500,000 deaths MESHD. In Mexico, by April 29, the general case fatality was 6.52%, with 11.1% confirmed case TRANS mortality and hospital recovery rate around 72%. Once hospitalized, the odds for recovery and hospital death MESHD rates depend mainly on the patients' comorbidities and age TRANS. In Mexico, triage guidelines use algorithms and risk estimation tools for severity assessment and decision-making. The study's objective is to analyze the underlying conditions of patients hospitalized for COVID-19 in Mexico concerning four severity outcomes. Materials and Methods: Retrospective cohort based on registries of all laboratory-confirmed patients with the COVID-19 infection MESHD that required hospitalization in Mexico. Independent variables were comorbidities and clinical manifestations. Dependent variables were four possible severity outcomes: (a) pneumonia MESHD pneumonia HP, (b) mechanical ventilation (c) intensive care unit, and (d) death MESHD; all of them were coded as binary Results: We included 69,334 hospitalizations of laboratory-confirmed and hospitalized patients to June 30, 2020. Patients were 55.29 years, and 62.61% were male TRANS. Hospital mortality among patients aged TRANS<15 was 9.11%, 51.99% of those aged TRANS >65 died. Male TRANS gender TRANS and increasing age TRANS predicted every severity outcome. Diabetes and hypertension MESHD hypertension HP predicted every severity outcome significantly. Obesity MESHD Obesity HP did not predict mortality, but CKD, respiratory diseases MESHD, cardiopathies were significant predictors. Conclusion: Obesity MESHD Obesity HP increased the risk for pneumonia MESHD pneumonia HP, mechanical ventilation, and intensive care admittance, but it was not a predictor of in-hospital death MESHD. Patients with respiratory diseases MESHD were less prone to develop pneumonia MESHD pneumonia HP, to receive mechanical ventilation and intensive care unit assistance, but they were at higher risk of in-hospital death MESHD.

    Epidemiological characteristics of SARS-COV-2 in Myanmar

    Authors: Aung Min Thway; Htun Tayza; Tun Tun Win; Ye Minn Tun; Moe Myint Aung; Yan Naung Win; Kyaw M Tun

    doi:10.1101/2020.08.02.20166504 Date: 2020-08-04 Source: medRxiv

    Coronavirus disease MESHD (COVID-19) is an infectious disease MESHD caused by a newly discovered severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2). In Myanmar, first COVID-19 reported cases were identified on 23rd March 2020. There were 336 reported confirmed cases TRANS, 261 recovered and 6 deaths MESHD through 13th July 2020. The study was a retrospective case series and all COVID-19 confirmed cases TRANS from 23rd March to 13th July 2020 were included. The data series of COVID-19 cases were extracted from the daily official reports of the Ministry of Health and Sports (MOHS), Myanmar and Centers for Disease MESHD Control and Prevention (CDC), Myanmar. Among 336 confirmed cases TRANS, there were 169 cases with reported transmission TRANS events. The median serial interval TRANS was 4 days (IQR 3, 2-5) with the range of 0 - 26 days. The mean of the reproduction number TRANS was 1.44 with (95% CI = 1.30-1.60) by exponential growth method and 1.32 with (95% CI = 0.98-1.73) confident interval by maximum likelihood method. This study outlined the epidemiological characteristics and epidemic parameters of COVID-19 in Myanmar. The estimation parameters in this study can be comparable with other studies and variability of these parameters can be considered when implementing disease MESHD control strategy in Myanmar.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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