Introduction: Coronavirus disease 2019 (COVID-19) has become a global public health emergency with lethality ranging from 1% to 5%. This study aimed to identify active high-risk transmission clusters of COVID-19 in Sergipe. Methods: We performed a prospective space-time analysis using confirmed cases of COVID-19 during the first 7 weeks of the outbreak in Sergipe. Results: The prospective space-time statistic detected "active" and emerging spatio-temporal clusters comprising six municipalities in the south-central region of the state. Conclusions: The Geographic Information System (GIS) associated with spatio-temporal scan statistics can provide timely support for surveillance and assist in decision-making.

This study aimed to analyse the trend and spatial–temporal clusters of risk of transmission of COVID-19 in northeastern Brazil. We conducted an ecological study using spatial and temporal trend analysis. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May 2020. We used the segmented log-linear regression model to assess time trends, and the local empirical Bayesian estimator, the global and local Moran indexes for spatial analysis. The prospective space–time scan statistic was performed using the Poisson probability distribution model. There were 113 951 confirmed cases of COVID-19. The average incidence rate was 199.73 cases/100 000 inhabitants. We observed an increasing trend in the incidence rate in all states. Spatial autocorrelation was reported in metropolitan areas, and 178 municipalities were considered a priority, especially in the states of Ceará and Maranhão. We identified 11 spatiotemporal clusters of COVID-19 cases; the primary cluster included 70 municipalities from Ceará state. COVID-19 epidemic is increasing rapidly throughout the Northeast region of Brazil, with dispersion towards countryside. It was identified high risk clusters for COVID-19, especially in the coastal side.

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Introduction: Schistosomiasis, caused by infection from Schistosoma mansoni, is a disease that represents an important public health problem for Brazil, especially for states in the Northeast region. Thus, the aim of this study is to present a new epidemiological profile for the disease in a municipality with low prevalence in the state of Alagoas, Brazil. Methods: A cross-sectional study was conducted through a coproparasitological and malacological survey. A structured questionnaire was applied to the study participants to survey possible risk factors and a spatial analysis (kernel density) was used to measure the risk of infection. Results: Of the 347 participants, 106 (30.5%) were infected by Schistosoma mansoni, most of them from the urban area of the municipality (68.9%; 73/106). A 3-fold risk of infection was found for individuals living in the urban area and a risk of 2.15 times for self-declared farmers. Biomphalaria glabrata and B. straminea were the species found in the municipality, but no animals were diagnosed as infected by the parasite. Spatial analysis showed a random distribution of vectors and human cases of the disease, and the formation of two clusters of human cases in the urban area was seen. Conclusions: A new epidemiological profile for schistosomiasis from S. mansoni infection was presented in a municipality of low endemicity: a high proportion of positive individuals in the urban area; presence of snails without positive diagnosis for S. mansoni infection; random distribution of vectors and human cases; and absence of association between classical risk factors and human infection.

This study aimed to analyse the spatial-temporal distribution of COVID-19 mortality in Sergipe, Northeast, Brazil. It was an ecological study utilising spatiotemporal analysis techniques that included all deaths confirmed by COVID-19 in Sergipe, from 2 April to 14 June 2020. Mortality rates were calculated per 100000 inhabitants and the temporal trends were analysed using a segmented log-linear model. For spatial analysis, the Kernel estimator was used and the crude mortality rates were smoothed by the empirical Bayesian method. The space-time prospective scan statistics applied the Poisson's probability distribution model. There were 391 COVID-19 registered deaths, with the majority among ≥60 years old (62%) and males (53%). The most prevalent comorbidities were hypertension (40%), diabetes (31%) and cardiovascular disease (15%). Observed was an increasing mortality trend across the state, with a higher increase in the countryside. An active spatiotemporal cluster of mortality comprising the metropolitan area and neighbouring cities was identified. The trend of COVID-19 mortality in Sergipe was increasing and the spatial distribution of deaths was heterogeneous with progression towards the countryside. Therefore, the use of spatial analysis techniques may contribute to surveillance and control of COVID-19 pandemic.

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Resumo Objetivo: Analisar aspectos relacionados com a positividade para esquistossomose em área de baixa prevalência, no Brasil. Métodos: Estudo transversal, realizado no primeiro semestre de 2020, quando foram analisadas a proporção de positividade, em função do número de lâminas de Kato-Katz, o desempenho diagnóstico do teste e a estimação da positividade a partir dos dados do Sistema de Informação do Programa de Vigilância e Controle da Esquistossomose (SISPCE). Resultados: Foram analisadas 2.088 lâminas de 348 indivíduos, sendo a proporção de positividade de 11,8%, 26,7% e 31,0% para 1, 4 e 6 lâminas analisadas, respectivamente. Houve concordância excelente (índice Kappa = 0,91) na comparação entre as leituras de 4 e 6 lâminas. Foi estimada subnotificação de 2,1 vezes nos dados do SISPCE. Conclusão: Ampliar o número de lâminas aumentou a positividade do Kato-Katz, o que pode contribuir para maximizar o controle da doença enquanto problema de Saúde Pública.