Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak
•Investigation of a mathematical model of the novel coronavirus•Pioneer research on policies regarding COVID-19•A multi-objective genetic algorithm which proposes strategies to achieve high-quality schedules by adjusting various factors•The first research that proposes policies for COVID-19 by consi...
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| Veröffentlicht in: | Chaos, solitons and fractals Jg. 136; S. 109883 |
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01.07.2020
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| Abstract | •Investigation of a mathematical model of the novel coronavirus•Pioneer research on policies regarding COVID-19•A multi-objective genetic algorithm which proposes strategies to achieve high-quality schedules by adjusting various factors•The first research that proposes policies for COVID-19 by considering its economic consequences
Understanding the early transmission dynamics of diseases and estimating 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 disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully 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 the proposed optimal policies, governments could find useful and practical ways for control of the disease. |
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| AbstractList | Understanding the early transmission dynamics of diseases and estimating 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 disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully 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 the proposed optimal policies, governments could find useful and practical ways for control of the disease. Understanding the early transmission dynamics of diseases and estimating 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 disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully 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 the proposed optimal policies, governments could find useful and practical ways for control of the disease.Understanding the early transmission dynamics of diseases and estimating 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 disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully 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 the proposed optimal policies, governments could find useful and practical ways for control of the disease. • Investigation of a mathematical model of the novel coronavirus • Pioneer research on policies regarding COVID-19 • A multi-objective genetic algorithm which proposes strategies to achieve high-quality schedules by adjusting various factors • The first research that proposes policies for COVID-19 by considering its economic consequences Understanding the early transmission dynamics of diseases and estimating 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 disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully 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 the proposed optimal policies, governments could find useful and practical ways for control of the disease. •Investigation of a mathematical model of the novel coronavirus•Pioneer research on policies regarding COVID-19•A multi-objective genetic algorithm which proposes strategies to achieve high-quality schedules by adjusting various factors•The first research that proposes policies for COVID-19 by considering its economic consequences Understanding the early transmission dynamics of diseases and estimating 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 disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully 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 the proposed optimal policies, governments could find useful and practical ways for control of the disease. |
| ArticleNumber | 109883 |
| Author | Jahanshahi, Hadi Yousefpour, Amin Bekiros, Stelios |
| Author_xml | – sequence: 1 givenname: Amin surname: Yousefpour fullname: Yousefpour, Amin organization: School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, 11155-4563, Iran – sequence: 2 givenname: Hadi orcidid: 0000-0001-7810-6479 surname: Jahanshahi fullname: Jahanshahi, Hadi email: jahanshahi.hadi90@gmail.com organization: Department of Mechanical Engineering, University of Manitoba, Winnipeg R3T 5V6, Canada – sequence: 3 givenname: Stelios orcidid: 0000-0002-9376-8726 surname: Bekiros fullname: Bekiros, Stelios organization: European University Institute, Department of Economics, Via delle Fontanelle, 18, I-50014, Florence, Italy |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32427205$$D View this record in MEDLINE/PubMed |
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| Keywords | Multi-objective genetic algorithm Mathematical modeling Novel coronavirus Optimal control |
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| Title | Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak |
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