Surveillance of the first cases of COVID-19 in Sergipe using a prospective spatiotemporal analysis: the spatial dispersion and its public health implications

• Autor(a) principal: Andrade, Lucas Almeida
• Data de Publicação: 2020
• Outros Autores: Gomes, Dharliton Soares, Góes, Marco Aurélio de Oliveira, Souza, Mércia Simone Feitosa de, Teixeira, Daniela Cabral Pizzi, Ribeiro, Caíque Jordan Nunes, Alves, José Antônio Barreto, Araújo, Karina Conceição Gomes Machado de, Santos, Allan Dantas dos
• Tipo de documento: preprint
• Idioma: eng
• Título da fonte: SciELO Preprints
• Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/609
• topic: COVID-19; Spatial analysis; Space-time clusters; Pandemic; Disease surveillance

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.