Review of fractional epidemic models

被引:79
作者
Chen, Yuli [1 ]
Liu, Fawang [2 ,3 ]
Yu, Qiang [2 ]
Li, Tianzeng [4 ]
机构
[1] Fuzhou Univ, Zhicheng Coll, Fuzhou 350001, Fujian, Peoples R China
[2] Queensland Univ Technol, Sch Math Sci, GPO Box 2434, Brisbane, Qld 4001, Australia
[3] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Fujian, Peoples R China
[4] Sichuan Univ Sci & Engn, Sch Math & Stat, Zigong 643000, Peoples R China
基金
澳大利亚研究理事会; 中国国家自然科学基金;
关键词
Epidemic models; Fractional order differential equations; Multi-term epidemic models; Parameter estimation; Implicit numerical method; Hybrid simplex search and particle swarm optimisation; PARAMETER-ESTIMATION; MATHEMATICAL-MODEL; PANDEMIC INFLUENZA; DYNAMICAL MODELS; CLIMATE-CHANGE; UNITED-STATES; COMMON-SOURCE; TRANSMISSION; DISEASE; SMOKING;
D O I
10.1016/j.apm.2021.03.044
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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. (C) 2021 Elsevier Inc. All rights reserved.
引用
收藏
页码:281 / 307
页数:27
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