dc.contributor.author | Parro, V.C. | |
dc.contributor.author | Lafetá, M.L.M. | |
dc.contributor.author | Pait, F. | |
dc.contributor.author | Ipólito, F.B. | |
dc.contributor.author | Toporcov, T.N. | |
dc.date.accessioned | 2024-10-15T21:36:52Z | |
dc.date.available | 2024-10-15T21:36:52Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 1932-6203 | |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108988349&doi=10.1371%2fjournal.pone.0253146&partnerID=40&md5=ca674f2d479ad31e96c57095f3932566 | |
dc.identifier.uri | https://repositorio.maua.br/handle/MAUA/1389 | |
dc.description.abstract | This 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.language | Inglês | pt_BR |
dc.publisher | Public Library of Science | en |
dc.relation.ispartof | PLoS ONE | |
dc.rights | Acesso Aberto | |
dc.source | Scopus | en |
dc.subject | Brazil | en |
dc.subject | COVID-19 | en |
dc.subject | Delivery of Health Care | en |
dc.subject | Humans | en |
dc.subject | Machine Learning | en |
dc.subject | Models, Statistical | en |
dc.subject | SARS-CoV-2 | en |
dc.subject | pioglitazone | en |
dc.subject | Aedes aegypti | en |
dc.subject | Article | en |
dc.subject | basic reproduction number | en |
dc.subject | Brazil | en |
dc.subject | coronavirus disease 2019 | en |
dc.subject | disease surveillance | en |
dc.subject | disease transmission | en |
dc.subject | epidemic | en |
dc.subject | health care facility | en |
dc.subject | health care system | en |
dc.subject | human | en |
dc.subject | machine learning | en |
dc.subject | mathematical model | en |
dc.subject | nonhuman | en |
dc.subject | population size | en |
dc.subject | prediction | en |
dc.subject | process optimization | en |
dc.subject | risk assessment | en |
dc.subject | seasonal variation | en |
dc.subject | seroconversion | en |
dc.subject | solid waste management | en |
dc.subject | tonic clonic seizure | en |
dc.subject | vaccination | en |
dc.subject | Zika fever | en |
dc.subject | Zika virus | en |
dc.subject | epidemiology | en |
dc.subject | health care delivery | en |
dc.subject | isolation and purification | en |
dc.subject | statistical model | en |
dc.title | Predicting COVID-19 in very large countries: The case of Brazil | en |
dc.type | Artigo de Periódico | pt_BR |
dc.identifier.doi | 10.1371/journal.pone.0253146 | |
dc.description.affiliation | Instituto Mauá de Tecnologia, Electrical Engineering, São Caetano do Sul, Brazil | |
dc.description.affiliation | Escola Politécnica, Universidade de São Paulo, São Paulo, Brazil | |
dc.description.affiliation | Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo, Brazil | |
dc.identifier.scopus | 2-s2.0-85108988349 | |
dc.citation.issue | 7 July | |
dc.citation.volume | 16 | |