Geospatial modelling of COVID-19 vaccination coverage inequalities: evidence from 192 countries

Geospatial modelling is a useful analytical tool for efficient characterization of inequalities in COVID-19 vaccination coverage for effective management control of the pandemic, which is limited in the literature on a global scale. This study investigated the spatial distribution of disparities in COVID-19 vaccine coverage indicators, namely, total vaccination rate (TVR), population proportion with at least one dose (1 + Dose rate), full vaccination rate (FVR), and booster vaccination rate (BVR) globally by accounting for socio-economic and healthcare accessibility indices, which included the number of vaccine types, COVID-19 containment and health index (CHI), universal healthcare service coverage index (USCI), human development index (HDI), and gross domestic product per capita (GDP/capita), as covariates. The analysis used a global dataset on the four vaccine coverage indicators and covariates. In the multivariate analysis, USCI independently predicted each vaccine coverage rate; CHI independently predicted all coverage rates except BVR; GDP/capita independently predicted only BVR; and number of vaccine types independently predicted FVR and 1 + Dose rate. Spatial autocorrelation tests and cokriging predictions produced spatial maps indicating clustering of countries with low coverage rates (0–29/100 people), mostly in the WHO African region and high clustering of TVR and 1 + Dose rates (101–185 and 49–76 per 100 population, respectively) in other parts, mostly in the developed countries. The study findings highlight the importance of global efforts of improving vaccine diplomacy and dose-sharing to address the inequalities in vaccine coverage.