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Analysis of a Fractional-Order COVID-19 Epidemic Model with Lockdown

The outbreak of the coronavirus disease (COVID-19) has caused a lot of disruptions around the world. In an attempt to control the spread of the disease among the population, several measures such as lockdown, and mask mandates, amongst others, were implemented by many governments in their countries....

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Detalles Bibliográficos
Autores principales: Denu, Dawit, Kermausuor, Seth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9693277/
https://www.ncbi.nlm.nih.gov/pubmed/36366284
http://dx.doi.org/10.3390/vaccines10111773
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author Denu, Dawit
Kermausuor, Seth
author_facet Denu, Dawit
Kermausuor, Seth
author_sort Denu, Dawit
collection PubMed
description The outbreak of the coronavirus disease (COVID-19) has caused a lot of disruptions around the world. In an attempt to control the spread of the disease among the population, several measures such as lockdown, and mask mandates, amongst others, were implemented by many governments in their countries. To understand the effectiveness of these measures in controlling the disease, several mathematical models have been proposed in the literature. In this paper, we study a mathematical model of the coronavirus disease with lockdown by employing the Caputo fractional-order derivative. We establish the existence and uniqueness of the solution to the model. We also study the local and global stability of the disease-free equilibrium and endemic equilibrium solutions. By using the residual power series method, we obtain a fractional power series approximation of the analytic solution. Finally, to show the accuracy of the theoretical results, we provide some numerical and graphical results.
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spelling pubmed-96932772022-11-26 Analysis of a Fractional-Order COVID-19 Epidemic Model with Lockdown Denu, Dawit Kermausuor, Seth Vaccines (Basel) Article The outbreak of the coronavirus disease (COVID-19) has caused a lot of disruptions around the world. In an attempt to control the spread of the disease among the population, several measures such as lockdown, and mask mandates, amongst others, were implemented by many governments in their countries. To understand the effectiveness of these measures in controlling the disease, several mathematical models have been proposed in the literature. In this paper, we study a mathematical model of the coronavirus disease with lockdown by employing the Caputo fractional-order derivative. We establish the existence and uniqueness of the solution to the model. We also study the local and global stability of the disease-free equilibrium and endemic equilibrium solutions. By using the residual power series method, we obtain a fractional power series approximation of the analytic solution. Finally, to show the accuracy of the theoretical results, we provide some numerical and graphical results. MDPI 2022-10-22 /pmc/articles/PMC9693277/ /pubmed/36366284 http://dx.doi.org/10.3390/vaccines10111773 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Denu, Dawit
Kermausuor, Seth
Analysis of a Fractional-Order COVID-19 Epidemic Model with Lockdown
title Analysis of a Fractional-Order COVID-19 Epidemic Model with Lockdown
title_full Analysis of a Fractional-Order COVID-19 Epidemic Model with Lockdown
title_fullStr Analysis of a Fractional-Order COVID-19 Epidemic Model with Lockdown
title_full_unstemmed Analysis of a Fractional-Order COVID-19 Epidemic Model with Lockdown
title_short Analysis of a Fractional-Order COVID-19 Epidemic Model with Lockdown
title_sort analysis of a fractional-order covid-19 epidemic model with lockdown
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9693277/
https://www.ncbi.nlm.nih.gov/pubmed/36366284
http://dx.doi.org/10.3390/vaccines10111773
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