Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in African continent

Although local spatiotemporal analysis can improve understanding of the geographic variation of infectious diseases, its drivers, and the search for targeted interventions, it is limited in Africa; therefore, monitoring the spread and pattern is essential. Information on the spatial patterns will have substantial inferences for real awareness in future pandemics. Primarily, the linkage between infectious disease and socioeconomic indicators based on the perception of spatial connectivity should be understood as the spread nowadays between regions is progressively connected. This study applied spatial analysis, including the Moran-I index, the Local Indicator of Spatial Association (LISA) and spatial regression models to study the spatial variations in the effects of socioeconomic indicators on COVID-19 mortality in the African region. The preliminary test on the spatial correlation of COVID-19 showed a significant result; thus, a spatial regression approach was employed. The spatial clustering relationships between COVID-19 mortality and the socioeconomic indicators were analyzed in all five regions of Africa using ordinary least squares (OLS), spatial lag models (SLM), and spatial error models (SEM). We observed the clustering of countries for COVID-19 mortality, signifying spatial correlation within the countries of Africa. In addition, among all the socioeconomic indicators included, the effects of Gross Domestic Product and age dependency ratio on COVID-19 mortality were the most critical indicators that described the pandemic evolution across the subregion. These results highlight the need to draw preventive and response policies applicable to infectious diseases with more significant consideration of the different geographical points in the region.