Spatio-temporal variation of malaria incidence in Sub-Saharan Africa from 2011 to 2020

Abstract

BACKGROUND : Malaria continues to pose a severe danger to health, social, and economic development in sub-Saharan Africa (SSA). Malaria in SSA requires a tremendous effort to eradicate. This study aimed to explore clusters of malaria incidence both spatially and temporally in the regions of the SSA countries using spatial autocorrelation statistics and univariate functional spatial scan statistics. METHODS : The study was done in 634 administrative areas throughout SSA nations. We used secondary data from the Malaria Atlas Project between 2011 and 2020. To convert malaria cases into incidence, the population at risk has been projected. To measure spatial autocorrelation, Global Moran’s 𝐼 was selected to examine feature positions and values. The Local Moran statistic was applied to identify regions dissimilar from their neighborhood. The Getis-Ord Local G* was employed to identify hot spots and cold spots areas in the SSA region. Furthermore, the univariate functional spatial scan statistic was used to see if spatial data tended to aggregate. RESULTS : The results revealed that the spatial and temporal clusters were statistically significant during the study period. Spatio-temporal scanning identified three high-risk malaria incidence areas in SSA. The regions around West-central, Central, and Southeast of SSA have a high-risk malaria incidence throughout the study period. CONCLUSIONS : The West-Central, Central, and Southeast regions of SSA had a high risk of malaria. Due to that, global end malaria councils should mobilize multi-sectional action, resources, advocacy, and accountability to support the national malaria program and national strategic plan to take and coordinate action across sectors to fight malaria.