Litcius/Paper detail

Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling

Dharliton Soares Gomes, Lucas Almeida Andrade, Caí­que Jordan Nunes Ribeiro, Marcus Valerius da Silva Peixoto, Shirley Verônica Melo Almeida Lima, Andrezza Marques Duque, Tatyane Martins Cirilo, Marco Aurélio de Oliveira Góes, Alanna Gleice Carvalho Fontes Lima, M. B. Santos, Karina Conceição Gomes Machado de Araújo, Allan Dantas dos Santos

2020Epidemiology and Infection58 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

GeographyPoisson regressionScan statisticPoisson distributionCluster (spacecraft)Coronavirus disease 2019 (COVID-19)Spatial analysisMetropolitan areaDemographyTransmission (telecommunications)StatisticsCartographyMedicinePopulationMathematicsSociologyArchaeologyInfectious disease (medical specialty)PathologyElectrical engineeringEngineeringProgramming languageDiseaseComputer scienceRemote sensingCOVID-19 epidemiological studiesData-Driven Disease SurveillanceZoonotic diseases and public health