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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/62558
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dc.creatorAirandes de Sousa Pintopt_BR
dc.creatorCarlos Alberto Rodriguespt_BR
dc.creatorCarlito Lopes Nascimento Sobrinhopt_BR
dc.creatorLivia Almeida da Cruzpt_BR
dc.creatorEdval Gomes Dos Santos Júniorpt_BR
dc.creatorPaulo Cesar Mendes Nunespt_BR
dc.creatorMatheus Gomes Reis Costapt_BR
dc.creatorManoel Otavio da Costa Rochapt_BR
dc.date.accessioned2024-01-10T21:59:40Z-
dc.date.available2024-01-10T21:59:40Z-
dc.date.issued2022-
dc.citation.volume55pt_BR
dc.citation.issuee0118-2021pt_BR
dc.citation.spage1pt_BR
dc.citation.epage7pt_BR
dc.identifier.doi10.1590/0037-8682-0118-2021pt_BR
dc.identifier.issn16789849pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/62558-
dc.description.resumoBackground: The epidemic curve has been obtained based on the 7-day moving average of the events. Although it facilitates the visualization of discrete variables, it does not allow the calculation of the absolute variation rate. Recently, we demonstrated that the polynomial interpolation method can be used to accurately calculate the daily acceleration of cases and deaths due to COVID-19. This study aimed to measure the diversity of epidemic curves and understand the importance of socioeconomic variables in the acceleration, peak cases, and deaths due to COVID-19 in Brazilian states. Methods: Epidemiological data for COVID-19 from federative units in Brazil were obtained from the Ministry of Health’s website from February 25 to July 11, 2020. Socioeconomic data were obtained from the Instituto Brasileiro de Geografia e Estatística (www.ibge.gov.br). Using the polynomial interpolation methods, daily cases, deaths and acceleration were calculated. Moreover, the correlation coefficient between the epidemic curve data and socioeconomic data was determined. Results: The combination of daily data and case acceleration determined that Brazilian states were in different stages of the epidemic. Maximum case acceleration, peak of cases, maximum death acceleration, and peak of deaths were associated with the Gini index of the gross domestic product of Brazilian states and population density but did not correlate with the per capita gross domestic product of Brazilian states. Conclusions: Brazilian states showed heterogeneous data curves. Population density and socioeconomic inequality were correlated with a more rapid exponential growth in new cases and deathspt_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentMED - DEPARTAMENTO DE CLÍNICA MÉDICApt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofRevista da Sociedade Brasileira de Medicina Tropical-
dc.rightsAcesso Abertopt_BR
dc.subjectCOVID-19pt_BR
dc.subjectSARS-CoV-2pt_BR
dc.subjectPolynomial interpolationpt_BR
dc.subjectGrowth ratept_BR
dc.subjectAccelerationpt_BR
dc.subjectCommunicable Disease Controlpt_BR
dc.subject.otherCOVID-19pt_BR
dc.subject.otherSARS-CoV-2pt_BR
dc.subject.otherSpatial Analysispt_BR
dc.subject.otherAccelerationpt_BR
dc.subject.otherEpidemic curvept_BR
dc.titleCovid-19 epidemic curve in brazil: a sum of multiple epidemics, whose inequality and population density in the states are correlated with growth rate and daily acceleration. an ecological studypt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://doi.org/10.1590/0037-8682-0118-2021pt_BR
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