Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil

• Main Author: Ferreira,Ricardo Vicente
• Publication Date: 2022
• Other Authors: Martines,Marcos Roberto, Toppa,Rogério Hartung, Assunção,Luiza Maria de, Desjardins,Michael Richard, Delmelle,Eric
• Format: Article
• Language: eng
• Source: Revista da Sociedade Brasileira de Medicina Tropical
• Download full: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822022000100326
• topic: Relative risk; Space-time statistics; SARS-CoV-2; Geographic information systems; Disease surveillance

ABSTRACT 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.