Application of Optimal Control of Infectious Diseases in a Model-Free Scenario

Detalhes bibliográficos
Autor(a) principal: Nepomuceno, Erivelton G.
Data de Publicação: 2021
Outros Autores: Peixoto, Márcia L. C., Lacerda, Márcio J., Campanharo, Andriana S. L. O. [UNESP], Takahashi, Ricardo H. C., Aguirre, Luis A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s42979-021-00794-3
http://hdl.handle.net/11449/240816
Resumo: Optimal control for infectious diseases has received increasing attention over the past few decades. In general, a combination of cost state variables and control effort have been applied as cost indices. Many important results have been reported. Nevertheless, it seems that the interpretation of the optimal control law for an epidemic system has received less attention. In this paper, we have applied Pontryagin’s maximum principle to develop an optimal control law to minimize the number of infected individuals and the vaccination rate. We have adopted the compartmental model SIR to test our technique. We have shown that the proposed control law can give some insights to develop a control strategy in a model-free scenario. Numerical examples show a reduction of 50% in the number of infected individuals when compared with constant vaccination. There is not always a prior knowledge of the number of susceptible, infected, and recovered individuals required to formulate and solve the optimal control problem. In a model-free scenario, a strategy based on the analytic function is proposed, where prior knowledge of the scenario is not necessary. This insight can also be useful after the development of a vaccine to COVID-19, since it shows that a fast and general cover of vaccine worldwide can minimize the number of infected, and consequently the number of deaths. The considered approach is capable of eradicating the disease faster than a constant vaccination control method.
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spelling Application of Optimal Control of Infectious Diseases in a Model-Free ScenarioComplex systemsCOVID-19EpidemiologyOptimal controlSIR modelVaccinationOptimal control for infectious diseases has received increasing attention over the past few decades. In general, a combination of cost state variables and control effort have been applied as cost indices. Many important results have been reported. Nevertheless, it seems that the interpretation of the optimal control law for an epidemic system has received less attention. In this paper, we have applied Pontryagin’s maximum principle to develop an optimal control law to minimize the number of infected individuals and the vaccination rate. We have adopted the compartmental model SIR to test our technique. We have shown that the proposed control law can give some insights to develop a control strategy in a model-free scenario. Numerical examples show a reduction of 50% in the number of infected individuals when compared with constant vaccination. There is not always a prior knowledge of the number of susceptible, infected, and recovered individuals required to formulate and solve the optimal control problem. In a model-free scenario, a strategy based on the analytic function is proposed, where prior knowledge of the scenario is not necessary. This insight can also be useful after the development of a vaccine to COVID-19, since it shows that a fast and general cover of vaccine worldwide can minimize the number of infected, and consequently the number of deaths. The considered approach is capable of eradicating the disease faster than a constant vaccination control method.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Control and Modelling Group (GCOM) Department of Electrical Engineering Federal University of São João del-ReiGraduate Program in Electrical Engineering (PPGEE) Federal University of Minas GeraisDepartment of Biostatistics Institute of Biosciences of Botucatu São Paulo State UniversityDepartment of Mathematics Federal University of Minas GeraisDepartment of Electronic Engineering Federal University of Minas GeraisDepartment of Biostatistics Institute of Biosciences of Botucatu São Paulo State UniversityCNPq: 302079/2011-4CNPq: 425509/2018-4CNPq: 465704/2014-0FAPEMIG: APQ-00870-17Federal University of São João del-ReiUniversidade Federal de Minas Gerais (UFMG)Universidade Estadual Paulista (UNESP)Nepomuceno, Erivelton G.Peixoto, Márcia L. C.Lacerda, Márcio J.Campanharo, Andriana S. L. O. [UNESP]Takahashi, Ricardo H. C.Aguirre, Luis A.2023-03-01T20:34:00Z2023-03-01T20:34:00Z2021-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s42979-021-00794-3SN Computer Science, v. 2, n. 5, 2021.2661-89072662-995Xhttp://hdl.handle.net/11449/24081610.1007/s42979-021-00794-32-s2.0-85124587854Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSN Computer Scienceinfo:eu-repo/semantics/openAccess2023-03-01T20:34:00Zoai:repositorio.unesp.br:11449/240816Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:43:54.213448Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Application of Optimal Control of Infectious Diseases in a Model-Free Scenario
title Application of Optimal Control of Infectious Diseases in a Model-Free Scenario
spellingShingle Application of Optimal Control of Infectious Diseases in a Model-Free Scenario
Nepomuceno, Erivelton G.
Complex systems
COVID-19
Epidemiology
Optimal control
SIR model
Vaccination
title_short Application of Optimal Control of Infectious Diseases in a Model-Free Scenario
title_full Application of Optimal Control of Infectious Diseases in a Model-Free Scenario
title_fullStr Application of Optimal Control of Infectious Diseases in a Model-Free Scenario
title_full_unstemmed Application of Optimal Control of Infectious Diseases in a Model-Free Scenario
title_sort Application of Optimal Control of Infectious Diseases in a Model-Free Scenario
author Nepomuceno, Erivelton G.
author_facet Nepomuceno, Erivelton G.
Peixoto, Márcia L. C.
Lacerda, Márcio J.
Campanharo, Andriana S. L. O. [UNESP]
Takahashi, Ricardo H. C.
Aguirre, Luis A.
author_role author
author2 Peixoto, Márcia L. C.
Lacerda, Márcio J.
Campanharo, Andriana S. L. O. [UNESP]
Takahashi, Ricardo H. C.
Aguirre, Luis A.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Federal University of São João del-Rei
Universidade Federal de Minas Gerais (UFMG)
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Nepomuceno, Erivelton G.
Peixoto, Márcia L. C.
Lacerda, Márcio J.
Campanharo, Andriana S. L. O. [UNESP]
Takahashi, Ricardo H. C.
Aguirre, Luis A.
dc.subject.por.fl_str_mv Complex systems
COVID-19
Epidemiology
Optimal control
SIR model
Vaccination
topic Complex systems
COVID-19
Epidemiology
Optimal control
SIR model
Vaccination
description Optimal control for infectious diseases has received increasing attention over the past few decades. In general, a combination of cost state variables and control effort have been applied as cost indices. Many important results have been reported. Nevertheless, it seems that the interpretation of the optimal control law for an epidemic system has received less attention. In this paper, we have applied Pontryagin’s maximum principle to develop an optimal control law to minimize the number of infected individuals and the vaccination rate. We have adopted the compartmental model SIR to test our technique. We have shown that the proposed control law can give some insights to develop a control strategy in a model-free scenario. Numerical examples show a reduction of 50% in the number of infected individuals when compared with constant vaccination. There is not always a prior knowledge of the number of susceptible, infected, and recovered individuals required to formulate and solve the optimal control problem. In a model-free scenario, a strategy based on the analytic function is proposed, where prior knowledge of the scenario is not necessary. This insight can also be useful after the development of a vaccine to COVID-19, since it shows that a fast and general cover of vaccine worldwide can minimize the number of infected, and consequently the number of deaths. The considered approach is capable of eradicating the disease faster than a constant vaccination control method.
publishDate 2021
dc.date.none.fl_str_mv 2021-09-01
2023-03-01T20:34:00Z
2023-03-01T20:34:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/s42979-021-00794-3
SN Computer Science, v. 2, n. 5, 2021.
2661-8907
2662-995X
http://hdl.handle.net/11449/240816
10.1007/s42979-021-00794-3
2-s2.0-85124587854
url http://dx.doi.org/10.1007/s42979-021-00794-3
http://hdl.handle.net/11449/240816
identifier_str_mv SN Computer Science, v. 2, n. 5, 2021.
2661-8907
2662-995X
10.1007/s42979-021-00794-3
2-s2.0-85124587854
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv SN Computer Science
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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