Spatial distribution of COVID-19 infection in Thailand and its related factors during 2020-2021

Abstract

Background: The COVID-19 pandemic, with over 759 million confirmed cases and 6.8 million deaths globally as of March 2023, underscores the critical need for effective control measures. Objectives: This study investigates the spatial dynamics of the disease in Thailand from 2020 to 2021, focusing on the association between demographic, environmental, and socioeconomic factors and COVID-19 infection rates, utilizing Geographic Information Systems. Methods: In this cross-sectional study, QGIS and GeoDa were employed to analyze the spatial pattern of COVID-19 infection rates using Local Moran’s I statistics for spatial autocorrelation. Spatial regression, specifically the Spatial Lag Model and Spatial Error Model was utilized to identify factors related to the observed spatial patterns. Results: The study identified high incidence clusters in southern and central Thailand. Population density, age demographics, and urbanization were found to be positively correlated with incidence rates. Environmental factors such as temperature showed a negative correlation, while forest area coverage exhibited a complex influence. Socioeconomic variables, including income and alcohol density, displayed varying effects, while tobacco density was positively associated with incidence. Conclusion: This comprehensive study underlines the importance of considering spatial dynamics in pandemic analysis. It reveals the multifaceted interplay between demographic, environmental, and socioeconomic factors in COVID-19 transmission. These findings provide valuable insights for targeted interventions and effective pandemic control strategies in Thailand and similar regions.