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Review of fractional epidemic models

The global impact of corona virus (COVID-19) has been profound, and the public health threat it represents is the most serious seen in a respiratory virus since the 1918 influenza A(H1N1) pandemic. In this paper, we have focused on reviewing the results of epidemiological modelling especially the fr...

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Autores principales: Chen, Yuli, Liu, Fawang, Yu, Qiang, Li, Tianzeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056944/
https://www.ncbi.nlm.nih.gov/pubmed/33897091
http://dx.doi.org/10.1016/j.apm.2021.03.044
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author Chen, Yuli
Liu, Fawang
Yu, Qiang
Li, Tianzeng
author_facet Chen, Yuli
Liu, Fawang
Yu, Qiang
Li, Tianzeng
author_sort Chen, Yuli
collection PubMed
description The global impact of corona virus (COVID-19) has been profound, and the public health threat it represents is the most serious seen in a respiratory virus since the 1918 influenza A(H1N1) pandemic. In this paper, we have focused on reviewing the results of epidemiological modelling especially the fractional epidemic model and summarized different types of fractional epidemic models including fractional Susceptible-Infective-Recovered (SIR), Susceptible-Exposed-Infective-Recovered (SEIR), Susceptible-Exposed-Infective-Asymptomatic-Recovered (SEIAR) models and so on. Furthermore, we propose a general fractional SEIAR model in the case of single-term and multi-term fractional differential equations. A feasible and reliable parameter estimation method based on modified hybrid Nelder-Mead simplex search and particle swarm optimisation is also presented to fit the real data using fractional SEIAR model. The effective methods to solve the fractional epidemic models we introduced construct a simple and effective analytical technique that can be easily extended and applied to other fractional models, and can help guide the concerned bodies in preventing or controlling, even predicting the infectious disease outbreaks.
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spelling pubmed-80569442021-04-21 Review of fractional epidemic models Chen, Yuli Liu, Fawang Yu, Qiang Li, Tianzeng Appl Math Model Article The global impact of corona virus (COVID-19) has been profound, and the public health threat it represents is the most serious seen in a respiratory virus since the 1918 influenza A(H1N1) pandemic. In this paper, we have focused on reviewing the results of epidemiological modelling especially the fractional epidemic model and summarized different types of fractional epidemic models including fractional Susceptible-Infective-Recovered (SIR), Susceptible-Exposed-Infective-Recovered (SEIR), Susceptible-Exposed-Infective-Asymptomatic-Recovered (SEIAR) models and so on. Furthermore, we propose a general fractional SEIAR model in the case of single-term and multi-term fractional differential equations. A feasible and reliable parameter estimation method based on modified hybrid Nelder-Mead simplex search and particle swarm optimisation is also presented to fit the real data using fractional SEIAR model. The effective methods to solve the fractional epidemic models we introduced construct a simple and effective analytical technique that can be easily extended and applied to other fractional models, and can help guide the concerned bodies in preventing or controlling, even predicting the infectious disease outbreaks. Elsevier Inc. 2021-09 2021-04-20 /pmc/articles/PMC8056944/ /pubmed/33897091 http://dx.doi.org/10.1016/j.apm.2021.03.044 Text en © 2021 Elsevier Inc. 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
Chen, Yuli
Liu, Fawang
Yu, Qiang
Li, Tianzeng
Review of fractional epidemic models
title Review of fractional epidemic models
title_full Review of fractional epidemic models
title_fullStr Review of fractional epidemic models
title_full_unstemmed Review of fractional epidemic models
title_short Review of fractional epidemic models
title_sort review of fractional epidemic models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056944/
https://www.ncbi.nlm.nih.gov/pubmed/33897091
http://dx.doi.org/10.1016/j.apm.2021.03.044
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