AI-Driven Hotspot Detection and Program Performance Analysis of Schistosomiasis in Africa

Schistosomiasis is one of the top neglected tropical diseases in sub-Saharan Africa with endemic locales of disease transmission and subnational heterogeneity despite repeated mass drug administration (MDA). This study created an AI-operated framework that identified the hotspots of spread, characterized the risk of endemicity and examined the performance of the programme based on 70,372 Admin2-years observations in endemic countries in Africa. It was a multi-component analytical methodology that combined machine learning clustering, supervised hotspots prediction, geographic stratification, and spatiotemporal trend modelling. Structurally imbalanced epidemiological patterns were detected by k-means clustering (k = 4), and the outcome is a dominant cluster attaining 65.9 percent of districts indicating a systemic pattern of similarity in coverage and endemicity, and small atypical clusters that are suggestive of extreme-risk or high-performance situations. The target hotspots were predicted by a random forest with 88 per cent accuracy and AUCs equal to 0.80 which indicates good discriminatory ability. Nevertheless, their extreme class imbalance led to high levels of recall (0.99) in hotspots too but close to zero in non-hotspots, with which methodological problems in elimination-phase modelling are identified. Combined risk stratification was able to show that most districts are in moderate-priority category, and not extreme high-risk one. Spatiotemporal analysis resulted in the overall negative mean endemicity trend (p= -7.59), but zero median slope of the slope indicated the widespread stability with significant inter-district variability. These results indicate that the process of eradication is discontinuous and geographically embedded. The study offers an AI hybrid stratification architecture that integrates clustering and predictive modelling to overcome the targeted intervention planning and optimal resource allocation problems. Findings highlight the importance of modular, evidence-based and geographically diverse eradication measures throughout Africa.