Detecting space-time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities

Abstract

The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect "active" and "emerging" space-time clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25-June 7, 2020, and February 25-July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 "active" clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.

Keywords: COVID-19; Disease surveillance; Geographic information systems; Relative risk; Space–time statistics; Spatial models.

Fig. 1

Daily number of reported COVID-19 cases in Brazil between February 25 and June 7, 2020, and descriptive statistics

Fig. 2

Spatial distribution of emerging space–time clusters of COVID-19 showing the relative risk in Brazil from February 25 to June 7, 2020

Fig. 3

Spatial distribution of emerging space–time clusters of COVID-19 at the municipality level of Brazil from February 25 to July 20, 2020

Fig. 4

Linear fit of the Generalized Linear Models (blue line) with confidence intervals (shaded area) for relative risk of the municipalities belonging to the space–time clusters of COVID-19 in Brazil

Fig. 5. a

Lisa map for GLM residual, b Moran’s I scatterplot residual, c residual scatterplot for GLM

Fig. 6. a

Lisa map for LM-lag residual, b Moran’s I scatterplot residual, c residual scatterplot for LM-Lag

Fig. 7. a

Lisa map for LM-error residual, b Moran’s I scatterplot residual, c residual scatterplot for LM-Error