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dc.contributor.authorParro, V.C.
dc.contributor.authorLafetá, M.L.M.
dc.contributor.authorPait, F.
dc.contributor.authorIpólito, F.B.
dc.contributor.authorToporcov, T.N.
dc.date.accessioned2024-10-15T21:36:52Z
dc.date.available2024-10-15T21:36:52Z
dc.date.issued2021
dc.identifier.issn1932-6203
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85108988349&doi=10.1371%2fjournal.pone.0253146&partnerID=40&md5=ca674f2d479ad31e96c57095f3932566
dc.identifier.urihttps://repositorio.maua.br/handle/MAUA/1389
dc.description.abstractThis work presents a practical proposal for estimating health system utilization for COVID-19 cases. The novel methodology developed is based on the dynamic model known as Susceptible, Infected, Removed and Dead (SIRD). The model was modified to focus on the healthcare system dynamics, rather than modeling all cases of the disease. It was tuned using data available for each Brazilian state and updated with daily figures. A figure of merit that assesses the quality of the model fit to the data was defined and used to optimize the free parameters. The parameters of an epidemiological model for the whole of Brazil, comprising a linear combination of the models for each state, were estimated considering the data available for the 26 Brazilian states. The model was validated, and strong adherence was demonstrated in most cases. Copyright: © 2021 Parro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.languageInglêspt_BR
dc.publisherPublic Library of Scienceen
dc.relation.ispartofPLoS ONE
dc.rightsAcesso Aberto
dc.sourceScopusen
dc.subjectBrazilen
dc.subjectCOVID-19en
dc.subjectDelivery of Health Careen
dc.subjectHumansen
dc.subjectMachine Learningen
dc.subjectModels, Statisticalen
dc.subjectSARS-CoV-2en
dc.subjectpioglitazoneen
dc.subjectAedes aegyptien
dc.subjectArticleen
dc.subjectbasic reproduction numberen
dc.subjectBrazilen
dc.subjectcoronavirus disease 2019en
dc.subjectdisease surveillanceen
dc.subjectdisease transmissionen
dc.subjectepidemicen
dc.subjecthealth care facilityen
dc.subjecthealth care systemen
dc.subjecthumanen
dc.subjectmachine learningen
dc.subjectmathematical modelen
dc.subjectnonhumanen
dc.subjectpopulation sizeen
dc.subjectpredictionen
dc.subjectprocess optimizationen
dc.subjectrisk assessmenten
dc.subjectseasonal variationen
dc.subjectseroconversionen
dc.subjectsolid waste managementen
dc.subjecttonic clonic seizureen
dc.subjectvaccinationen
dc.subjectZika feveren
dc.subjectZika virusen
dc.subjectepidemiologyen
dc.subjecthealth care deliveryen
dc.subjectisolation and purificationen
dc.subjectstatistical modelen
dc.titlePredicting COVID-19 in very large countries: The case of Brazilen
dc.typeArtigo de Periódicopt_BR
dc.identifier.doi10.1371/journal.pone.0253146
dc.description.affiliationInstituto Mauá de Tecnologia, Electrical Engineering, São Caetano do Sul, Brazil
dc.description.affiliationEscola Politécnica, Universidade de São Paulo, São Paulo, Brazil
dc.description.affiliationFaculdade de Saúde Pública, Universidade de São Paulo, São Paulo, Brazil
dc.identifier.scopus2-s2.0-85108988349
dc.citation.issue7 July
dc.citation.volume16


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