Investigating the Relationship Between Malaria Incidence and Public Health Infrastructure in Sub-Saharan Africa

This study examines the relationship between malaria incidence and three fundamental public health indicators — access to clean water, access to sanitation, and vitamin A supplementation coverage — among sub-Saharan African countries. Using country-level data from global health databases, the analysis applied nonparametric statistical tests and machine learning models to estimate how strongly these variables relate to malaria burden. Kruskal-Wallis and Dunn’s post hoc tests indicated significant differences in malaria incidence between water and sanitation access groups, with countries in the lowest access groups exhibiting significantly higher incidence. Cliff’s delta effect sizes indicated large differences, particularly between low and high access groups. Vitamin A supplementation showed some statistically significant differences, but its effect sizes were generally small. Machine learning models, specifically tree-based models, demonstrated moderate predictive ability, outperforming logistic regression in terms of classification accuracy and recall. The findings demonstrate the importance of basic infrastructure, particularly clean water and sanitation, in reducing malaria incidence. They underscore the value of ensuring access to these services as part of malaria control efforts and point to opportunities for enhancing predictive modeling in future global health research.