Spatial Autocorrelation and Predictive Modeling of COVID-19 Vaccine Coverage in Thailand (2021–2022)

Abstract

The COVID-19 pandemic posed significant challenges globally, with vaccine distribution being a critical factor for recovery. Despite achieving vaccination targets, Thailand faced disparities in vaccine coverage across regions. This study aims to investigate the spatial patterns and predictive determinants of COVID-19 vaccine coverage during 2021–2022 using spatial analysis techniques, including autocorrelation and regression models. Moran’s I and LISA showed significant positive spatial autocorrelation in vaccine coverage in 2021 (0.427) and 2022 (0.467). High-High clusters were found to be predominantly located in the Eastern region. The Spatial Error Model was the most suitable for predictions. In 2021, vaccine coverage was associated with population density, nighttime light, public transport, income, medical personnel, COVID-19 cases, and deaths (R² = 0.699). In 2022, key factors were the elderly population, nighttime light, internet access, and medical personnel (R² = 0.610). We conclude that in 2021, COVID-19 vaccine availability saw limited vaccine quantities distributed unevenly. Areas with substantial economic growth, high COVID-19 incidence rates, and a significant presence of medical personnel tended to have higher COVID-19 vaccine coverage. In 2022, vaccine availability improved with extensive distribution and community-level services targeting vulnerable groups, supported by online decision-making tools and a reservation system.