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Analysis of novel fractional COVID-19 model with real-life data application
The current work is of interest to introduce a detailed analysis of the novel fractional COVID-19 model. Non-local fractional operators are one of the most efficient tools in order to understand the dynamics of the disease spread. For this purpose, we intend as an attempt at investigating the fracti...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906878/ https://www.ncbi.nlm.nih.gov/pubmed/33654656 http://dx.doi.org/10.1016/j.rinp.2021.103968 |
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author | Inc, Mustafa Acay, Bahar Berhe, Hailay Weldegiorgis Yusuf, Abdullahi Khan, Amir Yao, Shao-Wen |
author_facet | Inc, Mustafa Acay, Bahar Berhe, Hailay Weldegiorgis Yusuf, Abdullahi Khan, Amir Yao, Shao-Wen |
author_sort | Inc, Mustafa |
collection | PubMed |
description | The current work is of interest to introduce a detailed analysis of the novel fractional COVID-19 model. Non-local fractional operators are one of the most efficient tools in order to understand the dynamics of the disease spread. For this purpose, we intend as an attempt at investigating the fractional COVID-19 model through Caputo operator with order [Formula: see text]. Employing the fixed point theorem, it is shown that the solutions of the proposed fractional model are determined to satisfy the existence and uniqueness conditions under the Caputo derivative. On the other hand, its iterative solutions are indicated by making use of the Laplace transform of the Caputo fractional operator. Also, we establish the stability criteria for the fractional COVID-19 model via the fixed point theorem. The invariant region in which all solutions of the fractional model under investigation are positive is determined as the non-negative hyperoctant [Formula: see text]. Moreover, we perform the parameter estimation of the COVID-19 model by utilizing the non-linear least squares curve fitting method. The sensitivity analysis of the basic reproduction number [Formula: see text] is carried out to determine the effects of the proposed fractional model’s parameters on the spread of the disease. Numerical simulations show that all results are in good agreement with real data and all theoretical calculations about the disease. |
format | Online Article Text |
id | pubmed-7906878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Author(s). Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79068782021-02-26 Analysis of novel fractional COVID-19 model with real-life data application Inc, Mustafa Acay, Bahar Berhe, Hailay Weldegiorgis Yusuf, Abdullahi Khan, Amir Yao, Shao-Wen Results Phys Article The current work is of interest to introduce a detailed analysis of the novel fractional COVID-19 model. Non-local fractional operators are one of the most efficient tools in order to understand the dynamics of the disease spread. For this purpose, we intend as an attempt at investigating the fractional COVID-19 model through Caputo operator with order [Formula: see text]. Employing the fixed point theorem, it is shown that the solutions of the proposed fractional model are determined to satisfy the existence and uniqueness conditions under the Caputo derivative. On the other hand, its iterative solutions are indicated by making use of the Laplace transform of the Caputo fractional operator. Also, we establish the stability criteria for the fractional COVID-19 model via the fixed point theorem. The invariant region in which all solutions of the fractional model under investigation are positive is determined as the non-negative hyperoctant [Formula: see text]. Moreover, we perform the parameter estimation of the COVID-19 model by utilizing the non-linear least squares curve fitting method. The sensitivity analysis of the basic reproduction number [Formula: see text] is carried out to determine the effects of the proposed fractional model’s parameters on the spread of the disease. Numerical simulations show that all results are in good agreement with real data and all theoretical calculations about the disease. The Author(s). Published by Elsevier B.V. 2021-04 2021-02-26 /pmc/articles/PMC7906878/ /pubmed/33654656 http://dx.doi.org/10.1016/j.rinp.2021.103968 Text en © 2021 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 Inc, Mustafa Acay, Bahar Berhe, Hailay Weldegiorgis Yusuf, Abdullahi Khan, Amir Yao, Shao-Wen Analysis of novel fractional COVID-19 model with real-life data application |
title | Analysis of novel fractional COVID-19 model with real-life data application |
title_full | Analysis of novel fractional COVID-19 model with real-life data application |
title_fullStr | Analysis of novel fractional COVID-19 model with real-life data application |
title_full_unstemmed | Analysis of novel fractional COVID-19 model with real-life data application |
title_short | Analysis of novel fractional COVID-19 model with real-life data application |
title_sort | analysis of novel fractional covid-19 model with real-life data application |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906878/ https://www.ncbi.nlm.nih.gov/pubmed/33654656 http://dx.doi.org/10.1016/j.rinp.2021.103968 |
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