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Theoretical and numerical analysis of COVID-19 pandemic model with non-local and non-singular kernels
The global consequences of Coronavirus (COVID-19) have been evident by several hundreds of demises of human beings; hence such plagues are significantly imperative to predict. For this purpose, the mathematical formulation has been proved to be one of the best tools for the assessment of present cir...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9615634/ https://www.ncbi.nlm.nih.gov/pubmed/36307434 http://dx.doi.org/10.1038/s41598-022-21372-4 |
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author | Cui, Ting Liu, Peijiang Din, Anwarud Ali, Fawad |
author_facet | Cui, Ting Liu, Peijiang Din, Anwarud Ali, Fawad |
author_sort | Cui, Ting |
collection | PubMed |
description | The global consequences of Coronavirus (COVID-19) have been evident by several hundreds of demises of human beings; hence such plagues are significantly imperative to predict. For this purpose, the mathematical formulation has been proved to be one of the best tools for the assessment of present circumstances and future predictions. In this article, we propose a fractional epidemic model of coronavirus (COVID-19) with vaccination effects. An arbitrary order model of COVID-19 is analyzed through three different fractional operators namely, Caputo, Atangana-Baleanu-Caputo (ABC), and Caputo-Fabrizio (CF), respectively. The fractional dynamics are composed of the interaction among the human population and the external environmental factors of infected peoples. It gives an extra description of the situation of the epidemic. Both the classical and modern approaches have been tested for the proposed model. The qualitative analysis has been checked through the Banach fixed point theory in the sense of a fractional operator. The stability concept of Hyers-Ulam idea is derived. The Newton interpolation scheme is applied for numerical solutions and by assigning values to different parameters. The numerical works in this research verified the analytical results. Finally, some important conclusions are drawn that might provide further basis for in-depth studies of such epidemics. |
format | Online Article Text |
id | pubmed-9615634 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96156342022-10-28 Theoretical and numerical analysis of COVID-19 pandemic model with non-local and non-singular kernels Cui, Ting Liu, Peijiang Din, Anwarud Ali, Fawad Sci Rep Article The global consequences of Coronavirus (COVID-19) have been evident by several hundreds of demises of human beings; hence such plagues are significantly imperative to predict. For this purpose, the mathematical formulation has been proved to be one of the best tools for the assessment of present circumstances and future predictions. In this article, we propose a fractional epidemic model of coronavirus (COVID-19) with vaccination effects. An arbitrary order model of COVID-19 is analyzed through three different fractional operators namely, Caputo, Atangana-Baleanu-Caputo (ABC), and Caputo-Fabrizio (CF), respectively. The fractional dynamics are composed of the interaction among the human population and the external environmental factors of infected peoples. It gives an extra description of the situation of the epidemic. Both the classical and modern approaches have been tested for the proposed model. The qualitative analysis has been checked through the Banach fixed point theory in the sense of a fractional operator. The stability concept of Hyers-Ulam idea is derived. The Newton interpolation scheme is applied for numerical solutions and by assigning values to different parameters. The numerical works in this research verified the analytical results. Finally, some important conclusions are drawn that might provide further basis for in-depth studies of such epidemics. Nature Publishing Group UK 2022-10-28 /pmc/articles/PMC9615634/ /pubmed/36307434 http://dx.doi.org/10.1038/s41598-022-21372-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cui, Ting Liu, Peijiang Din, Anwarud Ali, Fawad Theoretical and numerical analysis of COVID-19 pandemic model with non-local and non-singular kernels |
title | Theoretical and numerical analysis of COVID-19 pandemic model with non-local and non-singular kernels |
title_full | Theoretical and numerical analysis of COVID-19 pandemic model with non-local and non-singular kernels |
title_fullStr | Theoretical and numerical analysis of COVID-19 pandemic model with non-local and non-singular kernels |
title_full_unstemmed | Theoretical and numerical analysis of COVID-19 pandemic model with non-local and non-singular kernels |
title_short | Theoretical and numerical analysis of COVID-19 pandemic model with non-local and non-singular kernels |
title_sort | theoretical and numerical analysis of covid-19 pandemic model with non-local and non-singular kernels |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9615634/ https://www.ncbi.nlm.nih.gov/pubmed/36307434 http://dx.doi.org/10.1038/s41598-022-21372-4 |
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