Mathematical modelling on diffusion and control of COVID–19

In this paper, we develop a mathematical model for the spread and control of the coronavirus disease. An outbreak of COVID-19 has led to more than one million confirmed cases as of April 3rd, 2020. Understanding the early spread dynamics of the infection and evaluating the effectiveness of control m...

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Main Author: M. Veera Krishna
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2020-01-01
Series:Infectious Disease Modelling
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2468042720300397
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spelling doaj-85aae0170f944366b55c699230e28f6f2021-04-02T19:57:44ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272020-01-015588597Mathematical modelling on diffusion and control of COVID–19M. Veera Krishna0Department of Mathematics, Rayalaseema University, Kurnool, Andhra Pradesh, 518007, IndiaIn this paper, we develop a mathematical model for the spread and control of the coronavirus disease. An outbreak of COVID-19 has led to more than one million confirmed cases as of April 3rd, 2020. Understanding the early spread dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe COVID-19 spread with four datasets from within and outside of Wuhan, China; it is estimated how spread in Wuhan varied between January and February 2020. It is used these estimates to assess the potential for sustained human-to-human spread to occur in locations outside Wuhan if disease holders were introduced. It is combined SEIR framework model with data on cases of COVID-19 in China and International cases that originated in Wuhan to estimate how spread had varied over time during January and February 2020. Based on these estimates, it is calculated the probability that freshly introduced cases might produce outbreaks in other regions. Also, it is calculated approximately the median day by day basic reproduction number in Wuhan, refused from 2·45 (95% CI: 1·16–4·87) one week before travel restrictions were introduced on Jan 23rd, 2020, to 1.05 (0·42–2·40) one week after. Based on our estimates of, presumptuous SARS approximating disparity, it is computed that in locations with a similar spread potential to Wuhan in near the beginning of January, some time ago there are at least four independently set up cases, there is a more than fifty percent chance the infection will found within those inhabitants. COVID-19 spreading probably refused in Wuhan during delayed January 2020, corresponding with the prologue of voyage control channels. As more cases arrive in international locations with similar spread potential to Wuhan, before these organize measures, it is likely many chains of spread will fail to create initially but might lead to innovative outbreaks ultimately.http://www.sciencedirect.com/science/article/pii/S2468042720300397COVID-19CoronavirusMathematical modellingDiffusionReproduction number
collection DOAJ
language English
format Article
sources DOAJ
author M. Veera Krishna
spellingShingle M. Veera Krishna
Mathematical modelling on diffusion and control of COVID–19
Infectious Disease Modelling
COVID-19
Coronavirus
Mathematical modelling
Diffusion
Reproduction number
author_facet M. Veera Krishna
author_sort M. Veera Krishna
title Mathematical modelling on diffusion and control of COVID–19
title_short Mathematical modelling on diffusion and control of COVID–19
title_full Mathematical modelling on diffusion and control of COVID–19
title_fullStr Mathematical modelling on diffusion and control of COVID–19
title_full_unstemmed Mathematical modelling on diffusion and control of COVID–19
title_sort mathematical modelling on diffusion and control of covid–19
publisher KeAi Communications Co., Ltd.
series Infectious Disease Modelling
issn 2468-0427
publishDate 2020-01-01
description In this paper, we develop a mathematical model for the spread and control of the coronavirus disease. An outbreak of COVID-19 has led to more than one million confirmed cases as of April 3rd, 2020. Understanding the early spread dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe COVID-19 spread with four datasets from within and outside of Wuhan, China; it is estimated how spread in Wuhan varied between January and February 2020. It is used these estimates to assess the potential for sustained human-to-human spread to occur in locations outside Wuhan if disease holders were introduced. It is combined SEIR framework model with data on cases of COVID-19 in China and International cases that originated in Wuhan to estimate how spread had varied over time during January and February 2020. Based on these estimates, it is calculated the probability that freshly introduced cases might produce outbreaks in other regions. Also, it is calculated approximately the median day by day basic reproduction number in Wuhan, refused from 2·45 (95% CI: 1·16–4·87) one week before travel restrictions were introduced on Jan 23rd, 2020, to 1.05 (0·42–2·40) one week after. Based on our estimates of, presumptuous SARS approximating disparity, it is computed that in locations with a similar spread potential to Wuhan in near the beginning of January, some time ago there are at least four independently set up cases, there is a more than fifty percent chance the infection will found within those inhabitants. COVID-19 spreading probably refused in Wuhan during delayed January 2020, corresponding with the prologue of voyage control channels. As more cases arrive in international locations with similar spread potential to Wuhan, before these organize measures, it is likely many chains of spread will fail to create initially but might lead to innovative outbreaks ultimately.
topic COVID-19
Coronavirus
Mathematical modelling
Diffusion
Reproduction number
url http://www.sciencedirect.com/science/article/pii/S2468042720300397
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