Corpus overview


MeSH Disease

Human Phenotype

There are no HP terms in the subcorpus



There are no seroprevalence terms in the subcorpus

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    COVID-19 and India: What Next?

    Authors: Ramesh Behl; Manit Mishra

    id:2007.13523v1 Date: 2020-07-24 Source: arXiv

    The study carries out predictive modeling based on publicly available COVID-19 data for the duration 01 April to 20 June 2020 pertaining to India and five of its most infected states: Maharashtra, Tamil Nadu, Delhi, Gujarat, and Rajasthan using susceptible, infected, recovered, and dead ( SIRD MESHD) model. The basic reproduction number TRANS R0 TRANS is derived by exponential growth method using RStudio package R0 TRANS. The differential equations reflecting SIRD MESHD model have been solved using Python 3.7.4 on Jupyter Notebook platform. For visualization, Python Matplotlib 3.2.1 package is used. The study offers insights on peak-date, peak number of COVID-19 infections, and end-date pertaining to India and five of its states. The results could be leveraged by political leadership, health authorities, and industry doyens for policy planning and execution.

    Predictive Analysis for COVID-19 Spread in India by Adaptive Compartmental Model

    Authors: Sudhansu Sekhar Singh; Dinakrushna Mohapatra

    doi:10.1101/2020.07.08.20148619 Date: 2020-07-09 Source: medRxiv

    The role of mathematical modelling in predicting spread of an epidemic is of vital importance. The purpose of present study is to develop and apply a computational tool for predicting evolution of different epidemiological variables for COVID-19 in India. We propose a dynamic SIRD MESHD (Susceptible-Infected-Recovered-Dead) and SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) model for this purpose. In the dynamic model, time dependent infection MESHD rate is assumed for estimating evolution of different variables of the model. Parameter estimation of the model is the first step of the analysis which is performed by least square optimization of priori data. In the second step of the analysis, simulation is carried out by using evaluated parameters for prediction of the outbreak. The computational model has been validated against real data for COVID-19 outbreak in Italy. Time to reach peak, peak infected cases and total reported cases were compared with actual data and found to be in very good agreement. Next the model is applied for the case of India and various Indian states to predict different epidemiological parameters. Priori data was taken from the beginning of nation-wide lockdown on 24 March to 6 July. It was found that peak of the outbreak may reach in the month of August-September with maximum 4-5 lakhs active cases at peak. Total number of reported cases all over India would be in between three to five millions. State wise, Maharashtra, Tamilnadu and Delhi would be worst affected.

    Modeling the COVID-19 dissemination in the South Region of Brazil and testing gradual mitigation strategies

    Authors: Rafael Marques Da Silva

    doi:10.1101/2020.07.02.20145136 Date: 2020-07-04 Source: medRxiv

    This study aims to understand the features of the COVID-19 spread in the South Region of Brazil by estimating the Effective Reproduction Number TRANS (ERN) for the states of Parana (PR), Rio Grande do Sul ( RS MESHD), and Santa Catarina (SC). We used the SIRD (Susceptibles-Infectious-Recovered-Dead) model to describe the past data and to simulate strategies for the gradual mitigation of the epidemic curve by applying non-pharmacological measures. Besides the SIRD MESHD model does not include some aspects of COVID-19, as the symptomatic and asymptomatic TRANS subgroups of individuals and the incubation period TRANS, for example, in this work we intend to use a classical and easy to handle model to introduce a thorough method of adjustment that allows us to achieve reliable fitting for the real data and to obtain insights about the current trends for the pandemic in each locality. Our results demonstrate that for localities for which the ERN is about 2, only rigid measures are efficient to avoid overwhelming the health care system. These findings corroborate the relevance of keeping the value of the ERN below 1 and applying containment measures early.

    Forecasting the transmission TRANS of Covid-19 in India using a data driven SEIRD MESHD model

    Authors: Vishwajeet Jha

    id:2006.04464v1 Date: 2020-06-08 Source: arXiv

    The infections MESHD and fatalities due to SARS-CoV-2 virus for cases specific to India have been studied using a deterministic susceptible-exposed-infected-recovered-dead (SEIRD) compartmental model. One of the most significant epidemiological parameter, namely the effective reproduction number TRANS of the infection is extracted from the daily growth rate data of reported infections MESHD and it is included in the model with a time variation. We evaluate the effect of control interventions implemented till now and estimate the case numbers for infections MESHD and deaths averted by these restrictive measures. We further provide a forecast on the extent of the future Covid-19 transmission TRANS in India and predict the probable numbers of infections MESHD and fatalities under various potential scenarios.

    Analyzing Covid-19 Data using SIRD Models

    Authors: Abhijit Chakraborty; Jiaying Chen; Amelie Desvars-Larrive; Peter Klimek; Erwin Flores Tames; David Garcia; Leonhard Horstmeyer; Michaela Kaleta; Jana Lasser; Jenny Reddish; Beate Pinior; Johannes Wachs; Peter Turchin

    doi:10.1101/2020.05.28.20115527 Date: 2020-05-30 Source: medRxiv

    The goal of this analysis is to estimate the effects of the diverse government intervention measures implemented to mitigate the spread of the Covid-19 epidemic. We use a process model based on a compartmental epidemiological framework Susceptible-Infected-Recovered-Dead ( SIRD MESHD). Analysis of case data with such a mechanism-based model has advantages over purely phenomenological approaches because the parameters of the SIRD MESHD model can be calibrated using prior knowledge. This approach can be used to investigate how governmental interventions have affected the Covid-19-related transmission TRANS and mortality rate during the epidemic.

    A panel analysis on preventing and controlling efficiency of COVID-19 pandemic

    Authors: Ming Guan

    doi:10.21203/ Date: 2020-04-29 Source: ResearchSquare

    BACKGROUND: Currently, the globe is making efforts to curb the spreading of coronavirus disease MESHD 2019 (COVID-19). However, only a few studies have examined the associations among spreading growth, treatment efficiency, and death increase MESHD. This study aimed to examine the associations of COVID-2019 dead cases, healed cases, and recovered cases.METHODS: Data from China Data Lab in Harvard Dataverse with the China (January 22, 2020 to March 18, 2020), United States of America (USA, March 17, 2020 to April 12, 2020), and the world (January 22, 2020 to April 4, 2020) were analyzed. The main variables included in the analysis were the numbers of COVID-2019 total confirmed cases TRANS (TC), total dead cases (TD), newly dead cases (ND), newly confirmed cases TRANS (NC), newly healed cases (NH), total healed cases (TH), and newly recovered cases (NR). Pooled, dynamic, and event study analyses were conducted to reflect the associations among them.RESULTS: Descriptive analyses showed that NH/NC ratio in China are bigger than NR/NC in USA and the world. Pooled analysis showed that various roles of regions in NH and NR in a specific sample. Dynamic analysis showed significant roles of lags and NC in NH and NR. Panel event study showed that key events influence ND MESHD and NH in China significantly and NR in the world rather than NR in USA.CONCLUSION: The findings in this study indirectly confirmed the relationship between spreading growth, treatment efficiency, and death increase MESHD. China’s control strategies of COVID-19 pandemic are worth of learning by the globe.  

    Monitoring Italian COVID-19 spread by an adaptive SEIRD model

    Authors: Elena Loli Piccolomiini; Fabiana Zama

    doi:10.1101/2020.04.03.20049734 Date: 2020-04-06 Source: medRxiv

    Due to the recent diffusion of COVID-19 outbreak, the scientific community is making efforts in analysing models for understanding the present situation and predicting future scenarios. In this paper, we propose a Susceptible-Infected-Exposed-Recovered-Dead (SEIRD) differential model [Weitz J. S. and Dushoff J., Scientific reports, 2015] for the analysis and forecast of the COVID-19 spread in Italian regions, using the data from the Italian Protezione Civile from February 24th 2020. In this study, we investigate an adaptation of SEIRD that takes into account the actual policies of the Italian government, consisting of modelling the infection rate as a time-dependent function (SEIRD(rm)). Preliminary results on Lombardia and Emilia-Romagna regions confirm that SEIRD(rm) fits the data more accurately than the original SEIRD MESHD model with constant rate infection MESHD parameter. Moreover, the increased flexibility in the choice of the infection MESHD rate function makes it possible to better control the predictions due to the lockdown policy.

    Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD MESHD model

    Authors: Elena Loli Piccolomini; Fabiana Zama

    id:2003.09909v1 Date: 2020-03-22 Source: arXiv

    In this paper we propose a Susceptible-Infected-Exposed-Recovered-Dead (SEIRD) differential model for the analysis and forecast of the COVID-19 spread in some regions of Italy, using the data from the Italian Protezione Civile from February 24th 2020. In this study investigate an adaptation of the model. Since several restricting measures have been imposed by the Italian government at different times, starting from March 8th 2020, we propose a modification of SEIRD by introducing a time dependent transmitting rate. In the numerical results we report the maximum infection spread for the three Italian regions firstly affected by the COVID-19 outbreak(Lombardia, Veneto and Emilia Romagna). This approach will be successively extended to other Italian regions, as soon as more data will be available.

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MeSH Disease
Human Phenotype

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