Corpus overview


Overview

MeSH Disease

Human Phenotype

Pneumonia (39)

Fever (27)

Cough (26)

Falls (10)

Respiratory distress (10)


Transmission

Seroprevalence
    displaying 1 - 10 records in total 486
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    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.

    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.

    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.

    Reconciling epidemiological models with misclassified case-counts for SARS-CoV-2 with seroprevalence SERO surveys: A case study in Delhi, India

    Authors: Rupam Bhattacharyya; Ritwik Bhaduri; Ritoban Kundu; Maxwell Salvatore; Bhramar Mukherjee

    doi:10.1101/2020.07.31.20166249 Date: 2020-08-04 Source: medRxiv

    Underreporting of COVID-19 cases and deaths MESHD is a hindrance to correctly modeling and monitoring the pandemic. This is primarily due to limited testing, lack of reporting infrastructure and a large number of asymptomatic infections MESHD asymptomatic TRANS. In addition, diagnostic tests (RT-PCR tests for detecting current infection MESHD) and serological antibody tests SERO for IgG (to assess past infections MESHD) are imperfect. In particular, the diagnostic tests have a high false negative rate. Epidemiologic models with a latent compartment for unascertained infections MESHD like the Susceptible-Exposed-Infected-Removed (SEIR) models can provide predictions for unreported cases and deaths MESHD under certain assumptions. Typically, the number of unascertained cases is unobserved and thus we cannot validate these estimates for a real study except for simulation studies. Population-based seroprevalence SERO studies can provide a rough estimate of the total number of infections MESHD and help us check epidemiologic model projections. In this paper, we develop a method to account for high false negative rates in RT-PCR in an extension to the classic SEIR model. We apply this method to Delhi, the national capital region of India, with a population of 19.8 million and a COVID-19 hotspot of the country, obtaining estimates of underreporting factor for cases at 34-53 times and that for deaths MESHD at 8-13 times. Based on a recently released serological survey for Delhi with an estimated 22.86% seroprevalence SERO, we compute adjusted estimates of the true number of infections MESHD reported by the survey (after accounting for misclassification of the antibody test SERO results) which is largely consistent with the model outputs, yielding an underreporting factor for cases from 30-42. Together with the model and the serosurvey, this implies approximately 96-98% cases in Delhi remained unreported and whereas only 109,140 cases were reported on July 10, the true number of infections MESHD varied somewhere between 4.4-4.6 million across different estimates. While repeated serological monitoring is resource intensive, model-based adjustments, run with the most up to date data, can provide a viable option to keep track of the unreported cases and deaths MESHD and gauge the true extent of transmission TRANS of this insidious virus.

    Comparing the impact on COVID-19 mortality of self-imposed behavior change and of government regulations across 13 countries

    Authors: Julian Jamison; Donald Bundy; Dean Jamison; Jacob Spitz; Stephane Verguet

    doi:10.1101/2020.08.02.20166793 Date: 2020-08-04 Source: medRxiv

    Background: Countries have adopted different approaches, at different times, to reduce the transmission TRANS of coronavirus disease MESHD 2019 (COVID-19). Cross-country comparison could indicate the relative efficacy of these approaches. We assess various non-pharmaceutical interventions (NPIs) over time, comparing the effects of self-imposed (i.e. voluntary) behavior change and of changes enforced via official regulations, by statistically examining their impacts on subsequent death MESHD rates in 13 European countries. Methods and findings: We examine two types of NPI: the introduction of government-enforced closure policies over time; and self-imposed alteration of individual behaviors in response to awareness of the epidemic, in the period prior to regulations. Our proxy for the latter is Google mobility data, which captures voluntary behavior change when disease MESHD salience is sufficiently high. The primary outcome variable is the rate of change in COVID-19 fatalities per day, 16-20 days after interventions take place. Linear multivariate regression analysis is used to evaluate impacts. Voluntarily reduced mobility, occurring prior to government policies, decreases the percent change in deaths MESHD per day by 9.2 percentage points (95% CI 4.5-14.0 pp). Government closure policies decrease the percent change in deaths MESHD per day by 14.0 percentage points (95% CI 10.8-17.2 pp). Disaggregating government policies, the most beneficial are intercity travel TRANS restrictions, cancelling public events, and closing non-essential workplaces. Other sub-components, such as closing schools and imposing stay-at-home rules, show smaller and statistically insignificant impacts. Conclusions: This study shows that NPIs have substantially reduced fatalities arising from COVID-19. Importantly, the effect of voluntary behavior change is of the same order of magnitude as government-mandated regulations. These findings, including the substantial variation across dimensions of closure, have implications for the phased withdrawal of government policies as the epidemic recedes, and for the possible reimposition of regulations if a second wave occurs, especially given the substantial economic and human welfare consequences of maintaining lockdowns.

    A Study on Survival Scenario of COVID-19 patients in India: An Application of Survival Analysis on patient demographics

    Authors: Sampurna Kundu; Kirti; Debarghya Mandal

    doi:10.1101/2020.08.01.20162115 Date: 2020-08-04 Source: medRxiv

    The study of transmission TRANS dynamics of COVID-19, have depicted the rate, patterns and predictions of the pandemic cases. In order to combat the disease MESHD transmission TRANS in India, the Government had declared lockdown on the 25th of March. Even after a strict lockdown nationwide, the cases are increasing and have crossed 4.5 lakh positive cases. A positive point to be noted amongst all that the recovered cases are slowly exceeding the active cases. The survival of the patients, taking death MESHD as the event that varies over age groups TRANS and gender TRANS wise is noteworthy. This study aims in carrying out a survival analysis to establish the variability in survivorship among age groups TRANS and sex, at different levels, that is, national, state and district level. The open database of COVID-19 tracker (covid19india.org) of India has been utilized to fulfill the objectives of the study. The study period has been taken from the beginning of the first case which was on 30th Jan 2020 till 30th June. Due to the amount of under-reporting of data and dropping missing columns a total of 26,815 sample patients were considered. The entry point of each patient is different and event of interest is death MESHD in the study. Kaplan Meier survival estimation, Cox proportional hazard model and multilevel survival model has been used to perform survival analysis. Kaplan Meier survival function, shows that the probability of survival has been declining during the study period of five months. A significant variability has been observed in the age groups TRANS, as evident from all the survival estimates, with increasing age TRANS the risk of dying from COVID-19 increases. When Western and Central India show ever decreasing survival rate in the framed time period then Eastern , North Eastern and Southern India shows a slightly better picture in terms of survival. Maharashtra, Gujarat, Delhi, Rajasthan and West bengal showed alrmingly poor survival as well. This study has depicted a grave scenario of gradation of ever decreasing survival rates in various regions and shows the variability by age TRANS and gender TRANS.

    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.

    Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdown

    Authors: Abhinav Gola; Ravi Kumar Arya; Animesh; Ravi Dugh

    doi:10.1101/2020.08.03.20167254 Date: 2020-08-04 Source: medRxiv

    COVID-19 is spreading widely across the globe right now. Majority of the countries are relying on models and studies such as stochastic simulations, AceMod model, neural networks-based models, exponential growth model,Weibull distribution model, and so on to forecast the number of COVID-19 cases in the coming months. The objective on utilizing these models is to ensure that strict measures can be enacted to contain the virus spread and also predict the resources required to deal with the pandemic as the disease MESHD disease spreads TRANS spreads. In the past few months, several models were used to predict the infection MESHD rate for COVID-19. These models predicted the infection MESHD rates, recovery rate or death MESHD rates for the COVID-19 patients. All these different models took different approaches and different scenarios to predict the future rates. Now, that we know the real cases, we can check how accurate these models were. Some of these models were able to predict the near future quite close to the reality but most of them went astray. In this study, we review major forecasting models that were used in the context of India during country-wise lockdown and compare them. From these comparisons, we can see that while the advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic, effects can be catastrophic if poorly fitting models are used for predictions.

    Estimating the reproductive number TRANS R0 TRANS of SARS-CoV-2 in the United States and eight European countries and implications for vaccination

    Authors: Ruian Ke; Ethan Obie Romero-Severson; Steven Sanche; Nick Hengartner

    doi:10.1101/2020.07.31.20166298 Date: 2020-08-04 Source: medRxiv

    SARS-CoV-2 rapidly spread from a regional outbreak to a global pandemic in just a few months. Global research efforts have focused on developing effective vaccines against SARS-CoV-2 and the disease MESHD it causes, COVID-19. However, some of the basic epidemiological parameters, such as the exponential epidemic growth rate and the basic reproductive number TRANS, R0 TRANS, across geographic areas are still not well quantified. Here, we developed and fit a mathematical model to case and death MESHD count data collected from the United States and eight European countries during the early epidemic period before broad control measures were implemented. Results show that the early epidemic grew exponentially at rates between 0.19-0.29/day (epidemic doubling times between 2.4-3.6 days). We discuss the current estimates of the mean serial interval TRANS, and argue that existing evidence suggests that the interval is between 6-8 days in the absence of active isolation efforts. Using parameters consistent with this range, we estimated the median R0 TRANS value to be 5.8 (confidence interval: 4.7-7.3) in the United States and between 3.6 and 6.1 in the eight European countries. This translates to herd immunity thresholds needed to stop transmission TRANS to be between 73% and 84%. We further analyze how vaccination schedules depends on R0 TRANS, the duration of vaccine-induced immunity to SARS-CoV-2, and show that individual-level heterogeneity in vaccine induced immunity can significantly affect vaccination schedules.

    Implementation of Stacking Based ARIMA Model for Prediction of Covid-19 Cases in India

    Authors: Aman Swaraj; Arshpreet Kaur; Karan Verma; Ghanshyam Singh; Ashok Kumar; Leandro Melo de Sales

    doi:10.21203/rs.3.rs-52063/v1 Date: 2020-08-01 Source: ResearchSquare

    Background: Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease TRANS disease MESHD. The novel coronavirus disease MESHD, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely.Methods: We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data.Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance SERO evaluation parameters.Result:The hybrid combination displayed significant reduction in RMSE(16.23%), MAE(37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths MESHD and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries.Conclusion: Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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