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Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model
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AI-Driven Tuberculosis Hotspot Mapping to Optimize Active Case-Finding: Implementing the Epi-Control Platform in

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  • 1Ministry of Health Uganda, Lourdel Road, Plot 6, Lourdel Road, Nakasero, Kampala P.O. Box 7272, Uganda.

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|February 26, 2026
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Summary
This summary is machine-generated.

Artificial intelligence can predict tuberculosis hotspots in Uganda, improving active case-finding (ACF). The Epi-control platform identified high-yield areas, enhancing screening efficiency for the national TB program.

Keywords:
active case-findingartificial intelligencehotspotstuberculosis

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Area of Science:

  • Public Health
  • Epidemiology
  • Artificial Intelligence

Background:

  • Tuberculosis (TB) is a significant public health issue in Uganda, a high-burden country.
  • Challenges in TB control include asymptomatic cases, diagnostic limitations, and unequal healthcare access.
  • Effective strategies are needed to improve active case-finding (ACF) and reduce transmission.

Purpose of the Study:

  • To implement and evaluate the Epi-control platform, an AI tool, for predicting community-level TB hotspots.
  • To support data-driven ACF by identifying high-risk areas for targeted screening.
  • To assess the model's ability to prioritize areas with higher TB case detection.

Main Methods:

  • Utilized retrospective chest X-ray screening data from Uganda.
  • Integrated demographic, environmental, and human development indicators into a predictive model.
  • Employed a proprietary Bayesian modelling framework to predict TB risk at the sub-parish level.
  • Validated the model by comparing TB yields in predicted hotspots versus non-hotspot areas.

Main Results:

  • The AI model identified significantly higher TB yields in predicted hotspot areas (Risk Ratio = 1.69).
  • The Central and Western regions exhibited the highest concentration of predicted hotspots.
  • The model successfully prioritized areas with higher observed ACF yield in the retrospective data.

Conclusions:

  • AI-based predictive modelling, like the Epi-control platform, can enhance the efficiency of ACF in Uganda.
  • Targeting high-risk areas identified by the model can optimize screening efforts and resource allocation.
  • Integration into national TB programs could improve planning and prioritization, though prospective validation is recommended.