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Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil.
Ferreira, Ricardo Vicente; Martines, Marcos Roberto; Toppa, Rogério Hartung; Assunção, Luiza Maria de; Desjardins, Michael Richard; Delmelle, Eric.
Afiliação
  • Ferreira RV; Universidade Federal do Triângulo Mineiro, Programa de Pós-graduação Stricto Sensu em Ciência e Tecnologia Ambiental, Uberaba, MG, Brasil.
  • Martines MR; Universidade Federal de São Carlos, Centro de Ciências Humanas e Biológicas, Sorocaba, SP, Brasil.
  • Toppa RH; Universidade Federal de São Carlos, Departamento de Ciências Ambientais, Sorocaba, SP, Brasil.
  • Assunção LM; Universidade do Estado de Minas Gerais, Faculdade de Ciências Jurídicas, Ituiutaba, MG, Brasil.
  • Desjardins MR; Department of Epidemiology, Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Delmelle E; University of North Carolina-Charlotte, Center for Applied Geographic Information Science, Department of Geography and Earth Sciences, Charlotte, NC, USA.
Rev Soc Bras Med Trop ; 55: e0607, 2022.
Article em En | MEDLINE | ID: mdl-35946634
BACKGROUND: The number of deaths and people infected with coronavirus disease 2019 (COVID-19) in Brazil has steadily increased in the first few months of the pandemic. Despite the underreporting of coronavirus cases by government agencies across the country, São Paulo has the highest rate among all Brazilian states. METHODS: To identify the highest-risk municipalities during the initial outbreak, we utilized daily confirmed case data from official reports between February 25 and May 5, 2020, which were aggregated to the municipality level. A prospective space-time scan statistic was conducted to detect active clusters in three different time periods. RESULTS: Our findings suggest that approximately 4.6 times more municipalities belong to a significant space-time cluster with a relative risk (RR) > 1 on May 5, 2020. CONCLUSIONS: Our study demonstrated the applicability of the space-time scan statistic for the detection of emerging clusters of COVID-19. In particular, we identified the clusters and RR of municipalities in the initial months of the pandemic, explaining the spatiotemporal patterns of COVID-19 transmission in the state of São Paulo. These results can be used to improve disease monitoring and facilitate targeted interventions.
Assuntos

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: America do sul / Brasil Idioma: En Revista: Rev Soc Bras Med Trop Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: America do sul / Brasil Idioma: En Revista: Rev Soc Bras Med Trop Ano de publicação: 2022 Tipo de documento: Article