tetano
Editor, Senior Moderator
Chaos Solitons Fractals
. 2020 May 16;109883.
doi: 10.1016/j.chaos.2020.109883. Online ahead of print.
Optimal Policies for Control of the Novel Coronavirus (COVID-19)
Amin Yousefpour[SUP] 1 [/SUP], Hadi Jahanshahi[SUP] 2 [/SUP], Stelios Bekiros[SUP] 3 4 [/SUP]
Affiliations
Abstract
Understanding the early transmission dynamics of diseases and estimation of the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus (COVID-19). A mathematical model of severe SARS-CoV-2 transmission based on Wuhan's data is considered in this study. To solve the presented problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors such as contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing the contact rate and the transition rate of symptomatic infected individuals to the quarantined infected class, two optimal policies are designed. This study has two main scientific contributions that are: (1) This is pioneer research that proposes policies regarding COVID-19, (2) This is also the first research that addresses COVID-19 and considers its economic consequences through a multi-objective evolutionary algorithm. Numerical simulations conspicuously demonstrate that by applying optimal policies, governments could find useful and practical ways for control of the disease.
Keywords: Mathematical modeling; Multi-objective genetic algorithm; Novel coronavirus; Optimal control.
. 2020 May 16;109883.
doi: 10.1016/j.chaos.2020.109883. Online ahead of print.
Optimal Policies for Control of the Novel Coronavirus (COVID-19)
Amin Yousefpour[SUP] 1 [/SUP], Hadi Jahanshahi[SUP] 2 [/SUP], Stelios Bekiros[SUP] 3 4 [/SUP]
Affiliations
- PMID: 32427205
- PMCID: PMC7229919
- DOI: 10.1016/j.chaos.2020.109883
Abstract
Understanding the early transmission dynamics of diseases and estimation of the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus (COVID-19). A mathematical model of severe SARS-CoV-2 transmission based on Wuhan's data is considered in this study. To solve the presented problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors such as contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing the contact rate and the transition rate of symptomatic infected individuals to the quarantined infected class, two optimal policies are designed. This study has two main scientific contributions that are: (1) This is pioneer research that proposes policies regarding COVID-19, (2) This is also the first research that addresses COVID-19 and considers its economic consequences through a multi-objective evolutionary algorithm. Numerical simulations conspicuously demonstrate that by applying optimal policies, governments could find useful and practical ways for control of the disease.
Keywords: Mathematical modeling; Multi-objective genetic algorithm; Novel coronavirus; Optimal control.