Indian J Tuberc. 2025 Oct;72(4):455-459. doi: 10.1016/j.ijtb.2025.09.001. Epub 2025 Sep 2.
ABSTRACT
BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: We examine classical statistical approaches alongside predictive modeling, cost-effectiveness analysis, and AI-based frameworks. Case examples from diverse settings illustrate their practical impact.
FINDINGS: 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.
CONCLUSION: 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.
PMID:40975573 | DOI:10.1016/j.ijtb.2025.09.001
