Application of Optimal Control of Infectious Diseases in a Model-Free Scenario
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
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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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 |
|
_version_ |
1808129110100148224 |