AI-Driven Tuberculosis Hotspot Mapping to Optimize Active Case-Finding: Implementing the Epi-Control Platform in Uganda

Tuberculosis remains a major public health concern in Uganda and one among the thirty high TB burden countries globally. Despite national progress, gaps persist due to asymptomatic infections, diagnostic limitations, and uneven access to healthcare within the country. This study implemented the Epi-Control platform, an AI-driven predictive modelling tool, to predict community level hotspots and support data driven active case finding (ACF). Using retrospec-tive chest x-ray screening data, we integrated demographic, environmental and human development indicators from open-source databases to model TB risk at sub-parish level. A proprietary Bayesian modelling framework was deployed and validated by comparing TB yields between predicted hotspots and non-hotspot locations. Across Uganda, the mod-el identified significantly higher TB yields in hotspot areas (risk ratio= 1.69, 95% CI 1.41-2.02; p< 0.001). The Central and Western regions showed the highest concentrations of hotspots, consistent with their population density and urbaniza-tion patterns. The results demonstrate that AI-based predictive modelling can enhance the efficiency of ACF by targeting high-risk areas for screening. Integrating such predictive tools within national TB programs can optimize resource allo-cation and accelerate progress toward Uganda’s TB elimination goals.