Predictive AI-driven epidemiology for tuberculosis outbreak prevention in achieving Zero TB City vision.

The global effort to eradicate tuberculosis (TB) remains a formidable public health challenge, particularly in urban environments where dense populations and health disparities contribute to rapid disease transmission.The "Zero TB City" initiative aims to eliminate TB at the municipal level through early detection, integrated treatment, and comprehensive prevention strategies.Achieving this vision requires transformative tools capable of forecasting outbreaks before they escalate.In this context, predictive artificial intelligence (AI) emerges as a powerful enabler of data-driven epidemiology.By leveraging vast and heterogeneous datasets-including clinical records, demographic profiles, geospatial data, and real-time social determinants-AI models can detect subtle patterns and predict emerging TB hotspots with high accuracy.This paper explores the integration of predictive AI in urban TB surveillance systems, focusing on its ability to model spatiotemporal trends, identify high-risk populations, and optimize resource allocation.It discusses the superiority of machine learning and deep learning approaches over traditional statistical methods in handling noisy, incomplete, and nonlinear data typical of TB epidemiology.Furthermore, we examine how Bayesian inference, recurrent neural networks, and ensemble learning frameworks can dynamically adjust predictions based on new data inputs, improving the timeliness and accuracy of outbreak warnings.The paper also addresses the ethical, infrastructural, and policy considerations necessary for deploying AI-driven epidemiological tools, emphasizing the need for transparent algorithms, robust data governance, and community engagement.By narrowing the focus to actionable implementation strategies in the context of the Zero TB City campaign, this study offers a comprehensive roadmap for operationalizing AI in the fight against urban TB outbreaks-bridging the gap between digital innovation and public health impact.