Corpus overview


MeSH Disease

Human Phenotype

Pneumonia (39)

Fever (27)

Cough (26)

Falls (10)

Respiratory distress (10)


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    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.

    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.

    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.

    COVID-19 in South Asia: Real-time monitoring of reproduction and case fatality rate

    Authors: Fakhar Mustafa; Rehan Ahmed Khan Sherwani; Syed Salman Saqlain; Muhammad Asad Meraj; Haseeb ur Rehman; Rida Ayyaz

    id:2008.04347v1 Date: 2020-08-10 Source: arXiv

    As the ravages caused by COVID-19 pandemic are becoming inevitable with every moment, monitoring and understanding of transmission TRANS and fatality rate has become even more paramount for containing its spread. The key purpose of this analysis is to report the real-time effective reproduction rate ($R_t$ ) and case fatality rates (CFR) of COVID-19 in South Asia region. Data for this study are extracted from JHU CSSE COVID-19 Data source up to July 31, 2020. $R_t$ is estimated using exponential growth and time-dependent methods. R0 TRANS package in R-language is employed to estimate $R_t$ by fitting the existing epidemic curve. Case fatality rate is estimated by using Naive and Kaplan-Meier methods. Owing to exponential increase in cases of COVID-19, the pandemic will ensue in India, Maldives and in Nepal as $R_t$ was estimated greater than 1 for these countries. Although case fatality rates are found lesser as compared to other highly affected regions in the world, strict monitoring of deaths MESHD for better health facilities and care of patients is emphasized. More regional level cooperation and efforts are the need of time to minimize the detrimental effects of the virus.

    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/ 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.

    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 ( 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.

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MeSH Disease
Human Phenotype

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