A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil

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Campo DCValoridioma
dc.contributorDiretoria Técnicapt_BR
dc.contributor.authorTang, Yuanjipt_BR
dc.contributor.authorSerdan, Tamires D. A.pt_BR
dc.contributor.authorAlecrim, Amanda L.pt_BR
dc.contributor.authorSouza, Diego R.pt_BR
dc.contributor.authorNacano, Bruno R. M.pt_BR
dc.contributor.authorSilva, Flaviano L. R.pt_BR
dc.contributor.authorSilva, Eliane B.pt_BR
dc.contributor.authorPoma, Sarah O.pt_BR
dc.contributor.authorGennari-Felipe, Matheuspt_BR
dc.contributor.authorIser-Bem, Patrícia N.pt_BR
dc.contributor.authorMasi, Laureane N.pt_BR
dc.contributor.authorTang, Sherrypt_BR
dc.contributor.authorLevada-Pires, Adriana C.pt_BR
dc.contributor.authorHatanaka, Elainept_BR
dc.contributor.authorCury-Boaventura, Maria F.pt_BR
dc.contributor.authorBorges, Fernanda T.pt_BR
dc.contributor.authorPithon-Curi, Tania C.pt_BR
dc.contributor.authorCurpertino, Marli C.pt_BR
dc.contributor.authorFiamoncini, Jarleipt_BR
dc.contributor.authorLeandro, Carol Goispt_BR
dc.contributor.authorGorjao, Renatapt_BR
dc.contributor.authorCuri, Ruipt_BR
dc.contributor.authorHirabara, Sandro Massaopt_BR
dc.date.accessioned2021-09-03T16:51:00Z-
dc.date.available2021-09-03T16:51:00Z-
dc.date.issued2021pt_BR
dc.identifier.citationTang Y, Serdan TD.A., Alecrim AL., Nacano BR.M., Silva FL.R., Silva EB., et al. A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil. Sci. Rep.. 2021 Aug;11:16400. doi:10.1038/s41598-021-95815-9.pt_BR
dc.identifier.urihttps://repositorio.butantan.gov.br/handle/butantan/3920-
dc.description.abstractWe propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic.pt_BR
dc.description.sponsorship(FAPESP) Fundação de Amparo à Pesquisa do Estado de São Paulopt_BR
dc.description.sponsorship(CAPES) Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.description.sponsorship(CNPq) Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorship(JSGMF) John Simon Guggenheim Memorial Foundationpt_BR
dc.description.sponsorship(UNICSUL) Universidade Cruzeiro do Sulpt_BR
dc.format.extent16400pt_BR
dc.language.isoEnglishpt_BR
dc.relation.ispartofScientific Reportspt_BR
dc.rightsOpen accesspt_BR
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_BR
dc.titleA simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazilpt_BR
dc.typeArticlept_BR
dc.rights.licenseCC BYpt_BR
dc.identifier.doi10.1038/s41598-021-95815-9pt_BR
dc.identifier.urlhttps://doi.org/10.1038/s41598-021-95815-9pt_BR
dc.contributor.externalApplied NanoFemto Technologiespt_BR
dc.contributor.external(UNICSUL) Universidade Cruzeiro do Sulpt_BR
dc.contributor.externalKaiser Southern California Permanente Medical Grouppt_BR
dc.contributor.external(FADIP) Faculdade Dinâmica do Vale do Pirangapt_BR
dc.contributor.external(UFV) Universidade Federal de Viçosapt_BR
dc.contributor.external(USP) Universidade de São Paulopt_BR
dc.contributor.external(FoRC) Food Research Centerpt_BR
dc.contributor.external(UFPE) Universidade Federal de Pernambucopt_BR
dc.identifier.citationvolume11pt_BR
dc.subject.keywordComputational modelspt_BR
dc.subject.keywordepidemiologypt_BR
dc.subject.keywordViral infectionpt_BR
dc.relation.ispartofabbreviatedSci Reppt_BR
dc.identifier.citationabntv. 11, 16400, ago. 2021pt_BR
dc.identifier.citationvancouver2021 Aug;11:16400pt_BR
dc.contributor.butantanCuri, Rui|:Outros|:Diretoria Técnicapt_BR
dc.sponsorship.butantan(FAPESP) Fundação de Amparo à Pesquisa do Estado de São Paulo¦¦16/14529-1pt_BR
dc.sponsorship.butantan(FAPESP) Fundação de Amparo à Pesquisa do Estado de São Paulo¦¦18/09868-7pt_BR
dc.sponsorship.butantan(FAPESP) Fundação de Amparo à Pesquisa do Estado de São Paulo¦¦2020/09107-6pt_BR
dc.sponsorship.butantan(CAPES) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior¦¦pt_BR
dc.sponsorship.butantan(CNPq) Conselho Nacional de Desenvolvimento Científico e Tecnológico¦¦pt_BR
dc.sponsorship.butantan(JSGMF) John Simon Guggenheim Memorial Foundation¦¦pt_BR
dc.sponsorship.butantan(UNICSUL) Universidade Cruzeiro do Sul¦¦pt_BR
dc.identifier.bvsccBR78.1pt_BR
dc.identifier.bvsdbIBProdpt_BR
dc.description.dbindexedYespt_BR
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