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 25th, 2020. By June 7th, 2020, there were 691,758 confirmed cases, 36,455 confirmed deaths, and a mortality rate of 5.3%. To assist with the establishment of measures for the strategic planning to combat the COVID-19 pandemic in Brazil, we present the first Brazilian geographic study with the aims to examine active hand emerging space-time clusters of COVID-19. We examine the associations between clusters and mortality rate, vulnerability, and social inequality. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk from February 25th-June 7th, 2020 in 5,570 Brazilian municipalities. We apply a Spearman statistic to measure correlation between the relative risk of each cluster and mortality rate, GINI index, and social inequality. We detected 11 emerging space-time clusters of COVID-19 occurring in all Brazilian regions, with seven of them with a relative risk greater than one, and the highest in the Amapa state in the northern region of Brazil. We observed a positive and significant correlation between the relative risk and mortality rate, Brazilian Social Vulnerability Index, and GINI Index. The results can be utilized to improve COVID-19 response and planning in all Brazilian states.

Spatiotemporal dynamics and risk estimates of COVID-19 epidemic in Minas Gerais State: analysis of an expanding process

“…A geographic study on active spatiotemporal clusters of COVID-19 conducted from February 25 th to June 7 th , 2020 in 5,570 Brazilian municipalities showed that one of the clusters was located in the Southeast region of Brazil covering the States of Minas Gerais, Espirito Santo and Rio de Janeiro. This cluster included 274 municipalities, 48 of which presented RR greater than 1.0 40 .…”

Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19

“…The contribution of cluster detections and analysis in COVID-19 pandemic is becoming useful nowadays as it detects active and emerging clusters of COVID-19 and notify epidemiologist, decision makers, and public health care officials, which can help in eradicating infections from affected sites, and improving interventions, quarantine, and isolation measures. The significant applications of clustering with respect to infectious diseases modelling are demonstrated across the world (Zarikas et al 2020 ), for example, India (Bhosale and Shinde 2020 ), USA (Desjardins et al 2020 ; Hohl et al 2020 ), Brazil (Martines et al 2020 ), Italy (Cereda et al 2020 ), China (Ji et al 2020 ; Liu et al 2020a , b ; Qiu et al 2020 ; Zhang et al 2020 ), Singapore (Bhosale and Shinde 2020 ; Pung et al 2020 ), South Korea (Shim et al 2020 ), French Alps (Danis et al 2020 ), Germany (Pfefferle et al 2020 ), Sergipe (Andrade et al 2020 ), etc.…”

Incidência e mortalidade por COVID-19 nos municípios de Santa Catarina: associação com indicadores sociodemográficos

“…Nesse sentido, o coeficiente de Gini é uma medida de distribuição de renda usada frequentemente para aferir desigualdade social. Estudos nos Estados Unidos e no Brasil demonstram que localidades com maiores coeficientes experienciam taxas mais elevadas de incidência por Covid-19, evidenciando que municípios mais desiguais exibem os maiores riscos da doença (Martines et al, 2020;Mukherji & Mukherji, 2020). Porém, a realidade social de Santa Catarina está distante da realidade brasileira.…”

“…Mesmo assim, a análise da pandemia Covid-19 por meio do aspecto da desigualdade social exige elementos suficientes para detalhar particularidades demográficas da população, como sexo, idade e etnia. E, nesse ponto, torna-se necessário que todos os estados brasileiros divulguem esses dados (Martines et al, 2020).…”