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dc.contributor.authorSilva, Cristiana
dc.contributor.authorCruz, Carla
dc.contributor.authorTorres, Delfim F. M.
dc.contributor.authorPérez Muñuzuri, Alberto
dc.contributor.authorCarballosa Calleja, Alejandro
dc.contributor.authorArea Carracedo, Iván Carlos
dc.contributor.authorNieto Roig, Juan José
dc.contributor.authorFonseca-Pinto, Rui
dc.contributor.authorPassadouro da Fonseca, Rui
dc.contributor.authorSoares dos Santos, Estevão
dc.contributor.authorAbreu, Wilson
dc.contributor.authorMira Pérez, Jorge
dc.date.accessioned2021-05-19T07:40:37Z
dc.date.available2021-05-19T07:40:37Z
dc.date.issued2021
dc.identifier.citationSilva, C.J., Cruz, C., Torres, D.F.M. et al. Optimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugal. Sci Rep 11, 3451 (2021). https://doi.org/10.1038/s41598-021-83075-6
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10347/26210
dc.description.abstractThe COVID-19 pandemic has forced policy makers to decree urgent confinements to stop a rapid and massive contagion. However, after that stage, societies are being forced to find an equilibrium between the need to reduce contagion rates and the need to reopen their economies. The experience hitherto lived has provided data on the evolution of the pandemic, in particular the population dynamics as a result of the public health measures enacted. This allows the formulation of forecasting mathematical models to anticipate the consequences of political decisions. Here we propose a model to do so and apply it to the case of Portugal. With a mathematical deterministic model, described by a system of ordinary differential equations, we fit the real evolution of COVID-19 in this country. After identification of the population readiness to follow social restrictions, by analyzing the social media, we incorporate this effect in a version of the model that allow us to check different scenarios. This is realized by considering a Monte Carlo discrete version of the previous model coupled via a complex network. Then, we apply optimal control theory to maximize the number of people returning to “normal life” and minimizing the number of active infected individuals with minimal economical costs while warranting a low level of hospitalizations. This work allows testing various scenarios of pandemic management (closure of sectors of the economy, partial/total compliance with protection measures by citizens, number of beds in intensive care units, etc.), ensuring the responsiveness of the health system, thus being a public health decision support tool
dc.description.sponsorshipThis research is partially supported by the Portuguese Foundation for Science and Technology (FCT) within “Project Nr. 147 – Controlo Ótimo e Modelação Matemática da Pandemia COVID-19: contributos para uma estratégia sistémica de intervenção em saúde na comunidade”, in the scope of the “RESEARCH 4 COVID-19” call financed by FCT, and by project UIDB/04106/2020 (CIDMA). Silva is also supported by national funds (OE), through FCT, I.P., in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19. This research is also partially supported by the “Instituto de Salud Carlos III and the Ministerio de Ciencia e Innovación” of Spain, research grant COV20/00617, and by Xunta de Galicia, research grant 2018-PG082. APM and AC are part of the CRETUS Strategic Partnership (AGRUP2015/02) and JM is part of the AeMAT Strategic Partnership (ED431E2018/08), both supported by Xunta de Galicia. All these programs are co-funded by FEDER (EU)
dc.language.isoeng
dc.publisherSpringer Nature
dc.rights© The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
dc.rightsAtribución 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectApplied mathematics
dc.subjectControl theory
dc.subjectDifferential equations
dc.subjectDynamic networks
dc.subjectDynamical systems
dc.subjectInfectious diseases
dc.subjectPopulation dynamics
dc.titleOptimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugal
dc.typeinfo:eu-repo/semantics/article
dc.identifier.DOI10.1038/s41598-021-83075-6
dc.relation.publisherversionhttps://doi.org/10.1038/s41598-021-83075-6
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Física Aplicada
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Física de Partículas
dc.contributor.affiliationUniversidade de Santiago de Compostela. Instituto Interdisciplinar de Tecnoloxías Ambientais (CRETUS)
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización
dc.contributor.affiliationUniversidade de Santiago de Compostela. Instituto de Matemáticas
dc.description.peerreviewedSI


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© The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as  © The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/





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