Pakistan
Predictive Modelling for Efficient TB Case Finding with Real-Time Data
Project Context
Pakistan has the fifth highest TB burden globally, and a significant number of cases go undetected each year. Traditional strategies for Active Case Finding (ACF) are often resource-intensive and based on broad assumptions or outdated data. In response, a collaboration between EPCON, Mercy Corps, KIT Royal Tropical Institute, and national stakeholders aimed to modernize TB case-finding using predictive modeling and real-time program data. The project was financed by the Bill & Melinda Gates Foundation and designed to support Pakistan’s National TB Control Program in optimizing the deployment of mobile diagnostic teams and resources.
Project Objectives
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Predict which communities have the highest likelihood of identifying TB cases
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Improve cost-effectiveness of mobile screening interventions (e.g., mobile chest X-ray vans)
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Integrate real-time screening data
EPCON's Approach
EPCON developed and implemented a predictive model that integrated multiple data sources, including:
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Historical TB case data
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Contextual and sociodemographic variables (e.g., population density, urbanization, poverty levels, health access)
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Real-time program data from ongoing ACF campaigns
The model generated weekly risk predictions at subdistrict (union council) level, highlighting areas with high expected case yields. These insights were shared through the Epi-control platform, enabling Mercy Corps and other partners to dynamically adjust their ACF plans.
EPCON also helped automate the integration of data from mobile screening units to continuously improve model accuracy.
Key Outcomes and Impact
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TB case-finding yield was significantly higher in model-prioritized areas compared to conventional ACF planning
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Number Needed to Screen (NNS) was reduced, enabling more efficient use of mobile diagnostic resources
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Results from the pilot were published in BMJ Global Health, demonstrating the model’s accuracy and public health value
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The approach helped validate a new paradigm for TB control: data-driven microplanning combined with digital surveillance
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This project laid the groundwork for national scale-up and ongoing digital surveillance innovations in Pakistan
Link to publication: BMJ Global Health article - https://bmjpublichealth.bmj.com/content/3/1/e001424
Partners and Collaborations
