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Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network
Recently, four new strains of SARS-COV-2 were reported in different countries which are mutants and considered as 70 [Formula: see text] more dangerous than the existing covid-19 virus. In this paper, hybrid mathematical models of new strains and co-infection in Caputo, Caputo-Fabrizio, and Atangana...
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/PMC8096208/ https://www.ncbi.nlm.nih.gov/pubmed/33967409 http://dx.doi.org/10.1016/j.chaos.2021.111008 |
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author | Rehman, Attiq ul Singh, Ram Agarwal, Praveen |
author_facet | Rehman, Attiq ul Singh, Ram Agarwal, Praveen |
author_sort | Rehman, Attiq ul |
collection | PubMed |
description | Recently, four new strains of SARS-COV-2 were reported in different countries which are mutants and considered as 70 [Formula: see text] more dangerous than the existing covid-19 virus. In this paper, hybrid mathematical models of new strains and co-infection in Caputo, Caputo-Fabrizio, and Atangana-Baleanu are presented. The idea behind this co-infection modeling is that, as per medical reports, both dengue and covid-19 have similar symptoms at the early stages. Our aim is to evaluate and predict the transmission dynamics of both deadly viruses. The qualitative study via stability analysis is discussed at equilibria and reproduction number [Formula: see text] is computed. For the numerical purpose, Adams-Bashforth-Moulton and Newton methods are employed to obtain the approximate solutions of the proposed model. Sensitivity analysis is carried out to assessed the effects of various biological parameters and rates of transmission on the dynamics of both viruses. We also compared our results with some reported data against infected, recovered, and death cases. |
format | Online Article Text |
id | pubmed-8096208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80962082021-05-05 Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network Rehman, Attiq ul Singh, Ram Agarwal, Praveen Chaos Solitons Fractals Article Recently, four new strains of SARS-COV-2 were reported in different countries which are mutants and considered as 70 [Formula: see text] more dangerous than the existing covid-19 virus. In this paper, hybrid mathematical models of new strains and co-infection in Caputo, Caputo-Fabrizio, and Atangana-Baleanu are presented. The idea behind this co-infection modeling is that, as per medical reports, both dengue and covid-19 have similar symptoms at the early stages. Our aim is to evaluate and predict the transmission dynamics of both deadly viruses. The qualitative study via stability analysis is discussed at equilibria and reproduction number [Formula: see text] is computed. For the numerical purpose, Adams-Bashforth-Moulton and Newton methods are employed to obtain the approximate solutions of the proposed model. Sensitivity analysis is carried out to assessed the effects of various biological parameters and rates of transmission on the dynamics of both viruses. We also compared our results with some reported data against infected, recovered, and death cases. Elsevier Ltd. 2021-09 2021-05-04 /pmc/articles/PMC8096208/ /pubmed/33967409 http://dx.doi.org/10.1016/j.chaos.2021.111008 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 Rehman, Attiq ul Singh, Ram Agarwal, Praveen Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network |
title | Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network |
title_full | Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network |
title_fullStr | Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network |
title_full_unstemmed | Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network |
title_short | Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network |
title_sort | modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8096208/ https://www.ncbi.nlm.nih.gov/pubmed/33967409 http://dx.doi.org/10.1016/j.chaos.2021.111008 |
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