Abstract
Tuberculosis (TB) is a global health challenge, particularly in the Central African Republic (CAR), which is classified as a high TB burden country. In the CAR, factors like poverty, limited healthcare access, high HIV prevalence, malnutrition, inadequate sanitation, low measles vaccination coverage, and conflict-driven crowded living conditions elevate TB risk. Improved AI-driven surveillance is hypothesized to address under-reporting and underdiagnosis. Therefore, we created an epidemiological digital representation of TB in Bangui by employing passive data collection, spatial analysis using a 100 × 100 m grid, and mapping TB treatment services. Our approach included estimating undiagnosed TB cases through the integration of TB incidence, notification rates, and diagnostic data. High-resolution predictions are achieved by subdividing the area into smaller units while considering influencing variables within the Bayesian model. By designating moderate and high-risk hotspots, the model highlighted the potential for precise resource allocation in TB control. The strength of our model lies in its adaptability to overcome challenges, although this may have been to the detriment of precision in some areas. Research is envisioned to evaluate the model's accuracy, and future research should consider exploring the integration of multidrug-resistant TB within the model.