Spatial-temporal analysis of COVID-19 cases in the state of Alagoas

Abstract

Abstract The COVID-19 pandemic has severely impacted Brazil, highlighting significant gaps in public health infrastructure. This study aims to analyze the spatial-temporal distribution of COVID-19 cases in Alagoas. The study covers all 102 municipalities in Alagoas, using official data from the Alagoas COVID-19 Panel. It is observational and retrospective with an ecological and quantitative approach. Data were collected up to July 2, 2022, totaling 305,806 cases. Spatial analysis was performed using R Statistical software, with Global Moran's Index (GMI) and Local Indicators of Spatial Association (LISAs) identifying spatial clusters. In 2020, municipalities showed significant but weak spatial autocorrelation (GMI = 0.2084; p < 0.05). High-High clusters appeared in Maceió and nearby municipalities. In 2021, spatial autocorrelation remained weak (GMI = 0.2344; p < 0.05). High-High clusters persisted in Satuba and Maceió, while Low-Low clusters expanded into northeastern Alagoas by 2022. The reduction in High-High clusters in Maceió in 2022 likely resulted from early vaccination efforts. The spatial distribution pattern of COVID-19 in Alagoas reveals significant insights into regional pandemic dynamics. Stable infection rates in the center-west and south of Alagoas may be due to lower population density and less movement. The dynamic nature of COVID-19 spread highlights the need for continuous monitoring and adaptive public health strategies. The study underscores the importance of targeted interventions and future research to refine spatial models and incorporate additional variables to enhance predictive accuracy and inform public health strategies.

Keywords

• coronavirus
• respiratory infection
• COVID-19
• public health