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, one among the thirty high TB burden countries globally. Despite national progress, gaps persist due to asymptomatic disease, 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 retrospective 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 model 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 urbanization patterns. The results show that the model prioritized areas with higher observed ACF yield in this retrospective dataset, supporting its potential operational use for screening prioritization under similar implementation conditions. 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 programmes may support screening planning and resource prioritization; prospective evaluation and external validation are needed to assess generalisability and incremental impact.
Executive Impact Summary
This study demonstrates the critical impact of AI in public health, showcasing how predictive analytics can transform tuberculosis control by enabling highly targeted interventions and maximizing resource efficiency.
1.69x
Higher TB Yield in Hotspots
30%
Hotspot Prioritization Threshold
48.7%
Hotspots in Central Region
Deep Analysis & Enterprise Applications
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Tuberculosis (TB) continues to pose a significant public health challenge in Uganda. The country is among the thirty high TB burden countries globally, with an estimated 240 new cases reported daily [1]. While stakeholders use national estimates of incidence and prevalence to determine high-burden areas, these figures can vary locally due to differences in local risk factors [2]. Moreover, passive case-finding often fails to fully capture the true TB incidence [3,4]. This is attributable to factors such as limited healthcare access, asymptomatic cases, diagnostic shortcomings, or misdiagnosis as other respiratory ailments [4]. Active case-finding (ACF) proactively identifies individuals at risk of TB, a shift from reactive healthcare [3,5]. It is vital for diagnosing TB in both urban and rural areas, stopping its spread through early detection and treatment [6]. ACF also helps to understand challenges faced by individuals that prevent them from seeking care, allowing efficient resource allocation and reducing disease burden [7].
Enterprise Process Flow
Retrospective Chest X-ray Data & Contextual Covariates
→
Bayesian Network Regression Model Training
→
Sub-parish TB Positivity Rate Prediction
→
Hotspot Identification (Top 30%)
→
ACF Yield Validation & Prioritization
1.69x
Higher TB Yield in AI-Predicted Hotspots (p < 0.001)
| Region | Hotspots (TB Positive) | Non-Hotspots (TB Positive) | Risk Ratio | P-Value |
|---|---|---|---|---|
| Country | 215 | 250 | 1.69 (1.41–2.02) | <0.001 |
| Central | 18 | 93 | 2.18 (1.32–3.60) | 0.004 |
| Eastern | 75 | 71 | 1.52 (1.10–2.10) | 0.011 |
| Northern | 49 | 66 | 1.74 (1.21–2.51) | 0.003 |
| Western | 54 | 39 | 2.04 (1.35–3.06) | <0.001 |
AI-Driven Hotspot Targeting for Enhanced ACF
The Epi-control platform's ability to predict TB hotspots based on ACF data and contextual factors significantly improves the efficiency of case-finding efforts. By concentrating resources on high-risk areas, national TB programs can achieve a higher yield of identified cases. This approach addresses limitations of traditional notification-based methods, offering a more granular and proactive strategy for disease control.
Outcome: Improved ACF efficiency and resource allocation by identifying underserved, high-prevalence areas.
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Estimated Annual Savings
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Implementation Roadmap
A structured approach to integrating AI for optimized public health interventions, from data preparation to prospective validation.
Phase 1: Data Integration & Model Training
Integrate diverse data sources (ACF data, demographic, environmental, human development indicators) and train the Bayesian network model on sub-parish level data to predict TB positivity rates.
Phase 2: Hotspot Generation & Geoportal Deployment
Generate country-wide TB hotspot maps at sub-parish level and deploy on a customized geoportal for stakeholder access and visualization.
Phase 3: Operational Prioritization & Targeted Interventions
Utilize predicted hotspots to prioritize areas for community-based active case-finding interventions, optimizing resource allocation and screening efforts.
Phase 4: Prospective Evaluation & Validation
Conduct prospective studies and external validation to assess the generalizability, incremental impact, and cost-effectiveness of the AI-driven approach in real-world settings.
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