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Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study
In this paper, a mathematical model is formulated to study the transmission dynamics of the novel coronavirus infection under the effect of treatment. The compartmental model is firstly formulated using a system of nonlinear ordinary differential equations. Then, with the help of Caputo operator, th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8566449/ https://www.ncbi.nlm.nih.gov/pubmed/34751202 http://dx.doi.org/10.1016/j.chaos.2021.111534 |
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author | Liu, Xuan Ullah, Saif Alshehri, Ahmed Altanji, Mohamed |
author_facet | Liu, Xuan Ullah, Saif Alshehri, Ahmed Altanji, Mohamed |
author_sort | Liu, Xuan |
collection | PubMed |
description | In this paper, a mathematical model is formulated to study the transmission dynamics of the novel coronavirus infection under the effect of treatment. The compartmental model is firstly formulated using a system of nonlinear ordinary differential equations. Then, with the help of Caputo operator, the model is reformulated in order to obtain deeper insights into disease dynamics. The basic mathematical features of the time fractional model are rigorously presented. The nonlinear least square procedure is implemented in order to parameterize the model using COVID-19 cumulative cases in Saudi Arabia for the selected time period. The important threshold parameter called the basic reproduction number is evaluated based on the estimated parameters and is found [Formula: see text]. The fractional Lyapunov approach is used to prove the global stability of the model around the disease free equilibrium point. Moreover, the model in Caputo sense is solved numerically via an efficient numerical scheme known as the fractional Adamas-Bashforth-Molten approach. Finally, the model is simulated to present the graphical impact of memory index and various intervention strategies such as social-distancing, disinfection of the virus from environment and treatment rate on the pandemic peaks. This study emphasizes the important role of various scenarios in these intervention strategies in curtailing the burden of COVID-19. |
format | Online Article Text |
id | pubmed-8566449 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85664492021-11-04 Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study Liu, Xuan Ullah, Saif Alshehri, Ahmed Altanji, Mohamed Chaos Solitons Fractals Article In this paper, a mathematical model is formulated to study the transmission dynamics of the novel coronavirus infection under the effect of treatment. The compartmental model is firstly formulated using a system of nonlinear ordinary differential equations. Then, with the help of Caputo operator, the model is reformulated in order to obtain deeper insights into disease dynamics. The basic mathematical features of the time fractional model are rigorously presented. The nonlinear least square procedure is implemented in order to parameterize the model using COVID-19 cumulative cases in Saudi Arabia for the selected time period. The important threshold parameter called the basic reproduction number is evaluated based on the estimated parameters and is found [Formula: see text]. The fractional Lyapunov approach is used to prove the global stability of the model around the disease free equilibrium point. Moreover, the model in Caputo sense is solved numerically via an efficient numerical scheme known as the fractional Adamas-Bashforth-Molten approach. Finally, the model is simulated to present the graphical impact of memory index and various intervention strategies such as social-distancing, disinfection of the virus from environment and treatment rate on the pandemic peaks. This study emphasizes the important role of various scenarios in these intervention strategies in curtailing the burden of COVID-19. Elsevier Ltd. 2021-12 2021-11-04 /pmc/articles/PMC8566449/ /pubmed/34751202 http://dx.doi.org/10.1016/j.chaos.2021.111534 Text en © 2021 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 Liu, Xuan Ullah, Saif Alshehri, Ahmed Altanji, Mohamed Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study |
title | Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study |
title_full | Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study |
title_fullStr | Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study |
title_full_unstemmed | Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study |
title_short | Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study |
title_sort | mathematical assessment of the dynamics of novel coronavirus infection with treatment: a fractional study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8566449/ https://www.ncbi.nlm.nih.gov/pubmed/34751202 http://dx.doi.org/10.1016/j.chaos.2021.111534 |
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