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.

南美洲首例 COVID-19 病例于 2020 年 2 月 25 日发生在巴西。截至 2020 年 7 月 20 日，已有 2,118,646 例确诊病例和 80,120 例确诊死亡病例。为了协助制定预防措施和有针对性的干预措施来对抗巴西的大流行，我们提出了一项地理研究，以检测 COVID-19 的“活跃”和“新兴”时空集群。我们记录了 COVID-19 的相对风险与死亡率、不平等、社会经济脆弱性变量之间的关系。我们使用前瞻性时空扫描统计数据来检测每日 COVID-19 集群，并检查 2020 年 2 月 25 日至 6 月 7 日和 2020 年 2 月 25 日至 7 月 20 日期间巴西 5570 个城市的相对风险。我们应用广义线性模型 (GLM) 来评估死亡率、GINI 指数、和社会不平等是每个集群相对风险的预测因素。我们在第一时间段检测到 7 个“活跃”星团，分别是北部一个、东北部两个、东南部两个、南部一个和巴西首都一个。在第二阶段，我们发现 9 个 RR > 1 的集群位于巴西所有地区。通过 GLM 获得的结果表明，预测变量与 COVID-19 的相对风险之间存在显着的正相关。鉴于 GLM 残差中存在空间自相关，进行了空间滞后模型，该模型揭示了空间效应以及 GINI 指数和死亡率都是巴西 COVID-19 相对风险增加的有力预测因素。我们的研究可用于改善巴西所有州的 COVID-19 应对和规划。这项研究的结果对公共卫生特别重要，因为它们可以指导有针对性的干预措施，降低 COVID-19 的规模和传播。他们还可以通过向主要公共卫生官员通报 COVID-19 的最高风险领域来改善资源分配，例如检测和疫苗（如果有）。