Optimal control of normalized simr models with vaccination and treatment

Detalhes bibliográficos
Autor(a) principal: Maria do Rosário de Pinho
Data de Publicação: 2018
Outros Autores: Helmut Maurer, Hasnaa Zidani
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/107853
Resumo: We study a model based on the so called SIR model to control the spreading of a disease in a varying population via vaccination and treatment. Since we assume that medical treatment is not immediate we add a new compartment, M, to the SIR model. We work with the normalized version of the proposed model. For such model we consider the problem of steering the system to a specified target. We consider both a fixed time optimal control problem with L-1 cost and the minimum time problem to drive the system to the target. In contrast to the literature, we apply different techniques of optimal control to our problems of interest. Using the direct method, we first solve the fixed time problem and then proceed to validate the computed solutions using both necessary conditions and second order sufficient conditions. Noteworthy, we perform a sensitivity analysis of the solutions with respect to some parameters in the model. We also use the Hamiltonian Jacobi approach to study how the minimum time function varies with respect to perturbations of the initial conditions. Additionally, we consider a multi-objective approach to study the trade off between the minimum time and the social costs of the control of diseases. Finally, we propose the application of Model Predictive Control to deal with uncertainties of the model.
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spelling Optimal control of normalized simr models with vaccination and treatmentWe study a model based on the so called SIR model to control the spreading of a disease in a varying population via vaccination and treatment. Since we assume that medical treatment is not immediate we add a new compartment, M, to the SIR model. We work with the normalized version of the proposed model. For such model we consider the problem of steering the system to a specified target. We consider both a fixed time optimal control problem with L-1 cost and the minimum time problem to drive the system to the target. In contrast to the literature, we apply different techniques of optimal control to our problems of interest. Using the direct method, we first solve the fixed time problem and then proceed to validate the computed solutions using both necessary conditions and second order sufficient conditions. Noteworthy, we perform a sensitivity analysis of the solutions with respect to some parameters in the model. We also use the Hamiltonian Jacobi approach to study how the minimum time function varies with respect to perturbations of the initial conditions. Additionally, we consider a multi-objective approach to study the trade off between the minimum time and the social costs of the control of diseases. Finally, we propose the application of Model Predictive Control to deal with uncertainties of the model.2018-01-012018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10216/107853eng1531-349210.3934/dcdsb.2018006Maria do Rosário de PinhoHelmut MaurerHasnaa Zidaniinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T13:21:53Zoai:repositorio-aberto.up.pt:10216/107853Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:39:12.102373Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Optimal control of normalized simr models with vaccination and treatment
title Optimal control of normalized simr models with vaccination and treatment
spellingShingle Optimal control of normalized simr models with vaccination and treatment
Maria do Rosário de Pinho
title_short Optimal control of normalized simr models with vaccination and treatment
title_full Optimal control of normalized simr models with vaccination and treatment
title_fullStr Optimal control of normalized simr models with vaccination and treatment
title_full_unstemmed Optimal control of normalized simr models with vaccination and treatment
title_sort Optimal control of normalized simr models with vaccination and treatment
author Maria do Rosário de Pinho
author_facet Maria do Rosário de Pinho
Helmut Maurer
Hasnaa Zidani
author_role author
author2 Helmut Maurer
Hasnaa Zidani
author2_role author
author
dc.contributor.author.fl_str_mv Maria do Rosário de Pinho
Helmut Maurer
Hasnaa Zidani
description We study a model based on the so called SIR model to control the spreading of a disease in a varying population via vaccination and treatment. Since we assume that medical treatment is not immediate we add a new compartment, M, to the SIR model. We work with the normalized version of the proposed model. For such model we consider the problem of steering the system to a specified target. We consider both a fixed time optimal control problem with L-1 cost and the minimum time problem to drive the system to the target. In contrast to the literature, we apply different techniques of optimal control to our problems of interest. Using the direct method, we first solve the fixed time problem and then proceed to validate the computed solutions using both necessary conditions and second order sufficient conditions. Noteworthy, we perform a sensitivity analysis of the solutions with respect to some parameters in the model. We also use the Hamiltonian Jacobi approach to study how the minimum time function varies with respect to perturbations of the initial conditions. Additionally, we consider a multi-objective approach to study the trade off between the minimum time and the social costs of the control of diseases. Finally, we propose the application of Model Predictive Control to deal with uncertainties of the model.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
2018-01-01T00:00:00Z
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dc.relation.none.fl_str_mv 1531-3492
10.3934/dcdsb.2018006
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