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Optimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugal

2021Journal article Open access
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dc.contributor.authorSilva, Cristiana J.
dc.contributor.authorCruz, Carla
dc.contributor.authorTorres, Delfim F. M.
dc.contributor.authorMuñuzuri, Alberto P.
dc.contributor.authorCarballosa, Alejandro
dc.contributor.authorArea, Iván
dc.contributor.authorNieto, Juan J.
dc.contributor.authorFonseca-Pinto, Rui
dc.contributor.authorPassadouro, Rui
dc.contributor.authorSantos, Estevão Soares dos
dc.contributor.authorAbreu, Wilson
dc.contributor.authorMira, Jorge
dc.date.accessioned2022-02-15T17:42:10Z
dc.date.available2022-02-15T17:42:10Z
dc.date.issued2021
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 mode 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
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/s41598-021-83075-6pt_PT
dc.identifier.doi10.1038/s41598-021-83075-6pt_PT
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10400.8/6681
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherNature Researchpt_PT
dc.relationAGRUP2015/02pt_PT
dc.relationCenter for Research and Development in Mathematics and Applications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.titleOptimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugalpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCenter for Research and Development in Mathematics and Applications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04106%2F2020/PT
oaire.citation.issue1pt_PT
oaire.citation.titleScientific Reportspt_PT
oaire.citation.volume11pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePassadouro da Fonseca
person.givenNameRui
person.identifier.ciencia-id421B-F6A0-9EE9
person.identifier.orcid0000-0002-7766-576X
person.identifier.scopus-author-id8306731000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication144e558f-c0df-45fa-90b7-be31d81411bf
relation.isAuthorOfPublication.latestForDiscovery144e558f-c0df-45fa-90b7-be31d81411bf
relation.isProjectOfPublication198b0a26-89c6-4507-9459-313b8f692514
relation.isProjectOfPublication.latestForDiscovery198b0a26-89c6-4507-9459-313b8f692514

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