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Stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model

In this paper, we develop a new mathematical model for an in-depth understanding of COVID-19 (Omicron variant). The mathematical study of an omicron variant of the corona virus is discussed. In this new Omicron model, we used idea of dividing infected compartment further into more classes i.e asympt...

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Autores principales: Alrabaiah, Hussam, Din, Rahim Ud, Ansari, Khursheed J., ur Rehman Irshad, Ateeq, Ozdemir, Burhanettin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184875/
https://www.ncbi.nlm.nih.gov/pubmed/37214757
http://dx.doi.org/10.1016/j.rinp.2023.106536
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author Alrabaiah, Hussam
Din, Rahim Ud
Ansari, Khursheed J.
ur Rehman Irshad, Ateeq
Ozdemir, Burhanettin
author_facet Alrabaiah, Hussam
Din, Rahim Ud
Ansari, Khursheed J.
ur Rehman Irshad, Ateeq
Ozdemir, Burhanettin
author_sort Alrabaiah, Hussam
collection PubMed
description In this paper, we develop a new mathematical model for an in-depth understanding of COVID-19 (Omicron variant). The mathematical study of an omicron variant of the corona virus is discussed. In this new Omicron model, we used idea of dividing infected compartment further into more classes i.e asymptomatic, symptomatic and Omicron infected compartment. Model is asymptotically locally stable whenever [Formula: see text] and when [Formula: see text] at disease free equilibrium the system is globally asymptotically stable. Local stability is investigated with Jacobian matrix and with Lyapunov function global stability is analyzed. Moreover basic reduction number is calculated through next generation matrix and numerical analysis will be used to verify the model with real data. We consider also the this model under fractional order derivative. We use Grunwald–Letnikov concept to establish a numerical scheme. We use nonstandard finite difference (NSFD) scheme to simulate the results. Graphical presentations are given corresponding to classical and fractional order derivative. According to our graphical results for the model with numerical parameters, the population’s risk of infection can be reduced by adhering to the WHO’s suggestions, which include keeping social distances, wearing facemasks, washing one’s hands, avoiding crowds, etc.
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spelling pubmed-101848752023-05-16 Stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model Alrabaiah, Hussam Din, Rahim Ud Ansari, Khursheed J. ur Rehman Irshad, Ateeq Ozdemir, Burhanettin Results Phys Article In this paper, we develop a new mathematical model for an in-depth understanding of COVID-19 (Omicron variant). The mathematical study of an omicron variant of the corona virus is discussed. In this new Omicron model, we used idea of dividing infected compartment further into more classes i.e asymptomatic, symptomatic and Omicron infected compartment. Model is asymptotically locally stable whenever [Formula: see text] and when [Formula: see text] at disease free equilibrium the system is globally asymptotically stable. Local stability is investigated with Jacobian matrix and with Lyapunov function global stability is analyzed. Moreover basic reduction number is calculated through next generation matrix and numerical analysis will be used to verify the model with real data. We consider also the this model under fractional order derivative. We use Grunwald–Letnikov concept to establish a numerical scheme. We use nonstandard finite difference (NSFD) scheme to simulate the results. Graphical presentations are given corresponding to classical and fractional order derivative. According to our graphical results for the model with numerical parameters, the population’s risk of infection can be reduced by adhering to the WHO’s suggestions, which include keeping social distances, wearing facemasks, washing one’s hands, avoiding crowds, etc. The Author(s). Published by Elsevier B.V. 2023-06 2023-05-15 /pmc/articles/PMC10184875/ /pubmed/37214757 http://dx.doi.org/10.1016/j.rinp.2023.106536 Text en © 2023 The Author(s) 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
Alrabaiah, Hussam
Din, Rahim Ud
Ansari, Khursheed J.
ur Rehman Irshad, Ateeq
Ozdemir, Burhanettin
Stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model
title Stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model
title_full Stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model
title_fullStr Stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model
title_full_unstemmed Stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model
title_short Stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model
title_sort stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184875/
https://www.ncbi.nlm.nih.gov/pubmed/37214757
http://dx.doi.org/10.1016/j.rinp.2023.106536
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