Non-spatial modeling

To examine the role of socioeconomic characteristics on the presence of COVID-19 clusters, we select three indicators reflecting population characteristics and COVID-19 mortality: the GINI index, (IPEA 2015) the Brazilian Social Vulnerability Index (SVI) (Atlas Brasil 2013), and COVID-19 mortality rate. The GINI coefficient has been applied in the area of health to measure disparities (Han et al. 2016) and is based on population income per municipality and ranges between 0 in the case of perfect equality and 1 in the case of perfect inequality. The SVI is an index that varies between 0 and 1 and summarizes three attributes: urban infrastructure, human capital, and income and labor. The closer to 1, the greater the social vulnerability of a municipality. These dimensions correspond to sets of variables that indicate that the standard of living of families is low, suggesting non-access and non-observance of social variables. For municipalities with an SVI between 0 and 0.200, this indicates very low social vulnerability; between 0.201 and 0.300 indicates low social vulnerability; between 0.301 and 0.400 indicates middle social vulnerability; between 0.401 and 0.500 indicates high social vulnerability; and between 0.501 and 1 indicates that the municipality has very high social vulnerability (Brazil 2015). The mortality rate was selected because it is a criticality indicator since it is influenced by the structure of the population, sex, and age, in turn, conditioned by socioeconomic factors.

To analyze the correlation between the RR and the selected independent variables, we used the RR value of each municipality located in the space–time clusters, from February 25, 2020, to July 20, 2020 (n = 3304). We evaluate the effect of socioeconomic variables and mortality rate on RR using a Generalized Linear Model (GLM) (Eq 3).

with Y the relative risk, β the regression coefficients, “a” reflecting the GINI variable, “b” the SVI variable, “c” the mortality rate, and ε the error of the terms. Descriptive statistics for the variables used in the GLM model are provided in appendix Table 12. The GLM technique is conducted in the R software (version 4.0.1.).

Descriptive statistics for the variables used in the GLM model