Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan

We propose a compartmental mathematical model for the spread of the COVID-19 disease with special focus on the transmissibility of super-spreaders individuals. We compute the basic reproduction number threshold, we study the local stability of the disease free equilibrium in terms of the basic repro...

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Detalles Bibliográficos
Autores principales: Ndaïrou, Faïçal, Area, Iván, Nieto, Juan J., Torres, Delfim F.M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7184012/
https://www.ncbi.nlm.nih.gov/pubmed/32341628
http://dx.doi.org/10.1016/j.chaos.2020.109846
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author Ndaïrou, Faïçal
Area, Iván
Nieto, Juan J.
Torres, Delfim F.M.
author_facet Ndaïrou, Faïçal
Area, Iván
Nieto, Juan J.
Torres, Delfim F.M.
author_sort Ndaïrou, Faïçal
collection PubMed
description We propose a compartmental mathematical model for the spread of the COVID-19 disease with special focus on the transmissibility of super-spreaders individuals. We compute the basic reproduction number threshold, we study the local stability of the disease free equilibrium in terms of the basic reproduction number, and we investigate the sensitivity of the model with respect to the variation of each one of its parameters. Numerical simulations show the suitability of the proposed COVID-19 model for the outbreak that occurred in Wuhan, China.
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spelling pubmed-71840122020-04-27 Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan Ndaïrou, Faïçal Area, Iván Nieto, Juan J. Torres, Delfim F.M. Chaos Solitons Fractals Article We propose a compartmental mathematical model for the spread of the COVID-19 disease with special focus on the transmissibility of super-spreaders individuals. We compute the basic reproduction number threshold, we study the local stability of the disease free equilibrium in terms of the basic reproduction number, and we investigate the sensitivity of the model with respect to the variation of each one of its parameters. Numerical simulations show the suitability of the proposed COVID-19 model for the outbreak that occurred in Wuhan, China. Elsevier Ltd. 2020-06 2020-04-27 /pmc/articles/PMC7184012/ /pubmed/32341628 http://dx.doi.org/10.1016/j.chaos.2020.109846 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Ndaïrou, Faïçal
Area, Iván
Nieto, Juan J.
Torres, Delfim F.M.
Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan
title Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan
title_full Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan
title_fullStr Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan
title_full_unstemmed Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan
title_short Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan
title_sort mathematical modeling of covid-19 transmission dynamics with a case study of wuhan
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7184012/
https://www.ncbi.nlm.nih.gov/pubmed/32341628
http://dx.doi.org/10.1016/j.chaos.2020.109846
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