RESUMO
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.
RESUMO
Dengue fever (DENF), chikungunya (CHIK), and Zika are responsible for the majority of the burden caused by vector-borne diseases (VBDs); which are produced by viruses primarily transmitted by the Aedes mosquito. Aedes have become prolific in urban areas due to a combination of climate change, rapid urbanization, increased human mobility, and globalization, causing the three VBDs to emerge in novel regions. Community knowledge can provide detailed insights about the spatial heterogeneity of disease risk and rates within a particular region, improving public health interventions. Knowledge, Attitude, and Practice (KAP) surveys are used to shed light on at-risk communities' understanding of the vector, the pathogen, prevention and treatment strategies. Little is known how KAP varies among diseases, and among neighborhoods within a city. Understanding KAP variation among co-circulating VBDs at a fine-level, especially differences between endemic and emerging diseases, can improve targeted interventions, education programs, and health policy. We administered KAP surveys to 327 individuals in healthcare centers and selected neighborhoods in Cali, Colombia in June 2019. We utilized generalized linear models (GLMs) to identify significant predictors of KAP. Our findings suggest that knowledge is related to community characteristics (e.g. strata), while attitudes and practices are more related to individual-level factors. Access to healthcare also forms significant predictor of residents participating in preventative practices. The results can be leveraged to inform public health officials and communities to motivate at-risk neighborhoods to take an active role in vector surveillance and control, while improving educational and surveillance resources in Cali, Colombia.