The application of spatial analysis to the study of human epidemiological data has gained great momentum in the last two decades. This article approaches the scan statistic for the detection of spatial-temporal clusters of COVID-19 cases in the State of Santa Catarina, Brazil. The aim is to apply the scan statistic to identify active clusters, determining their location, size, and order (priority). Organization of the descriptive based included COVID-19 cases from March 1 to August 31, 2020, available in the Santa Catarina State Open Data Portal. The vector base of the municipal limits and mesoregions in Santa Catarina and the population estimates for 2020 were obtained from the Brazilian Institute of Geography and Statistics (IBGE) website. The workplace mobility trend covariable was obtained from the document COVID-19: Report on Community Mobility in Google. Execution of the statistic considered the discrete Poisson model, supported by the prospective approach. The study’s results evidenced the procedure’s capacity to demarcate clusters, identifying 17 active clusters with the response variable and 18 active clusters after inclusion of the covariable, distributed throughout the state and predominantly on the coast and the Western region. The primary cluster was in Southern Santa Catarina. The workplace mobility trend covariable moderately influenced 38.89% of the clusters. The method proved to be efficient for understanding the epidemic’s spatial distribution. This characterizes the scan statistic as a tool to support the execution of actions by policymakers, prioritizing areas most affected by the disease.
Coronavirus; Medical Geography; Spatio-Temporal Analysis