From data to decisions: Statistical tools and Artificial Intelligence in tuberculosis Operational Research

Tuberculosis (TB) remains a major public health challenge, especially in low- and middle-income countries. Operational Research (OR), supported by robust statistical methods, plays a critical role in optimizing TB control strategies.

This review highlights the statistical tools applied in TB Operational Research, their applications, and the emerging role of Artificial Intelligence (AI) in strengthening data-driven decision-making.

We examine classical statistical approaches alongside predictive modeling, cost-effectiveness analysis, and AI-based frameworks. Case examples from diverse settings illustrate their practical impact.

Statistical methods underpin surveillance, diagnosis, treatment evaluation, and policy modeling in TB programs. AI-driven techniques, such as machine learning and deep learning, are expanding the analytical landscape by enhancing prediction, identifying high-risk populations, and enabling real-time program monitoring.

Statistical tools from traditional inference to AI-modeling are essential for advancing TB control. Strengthening methodological rigor, reporting standards and interdisciplinary collaboration will be pivotal in harnessing data for effective TB elimination strategies.