AI for Public Health: Intelligent Surveillance and Early Detection of Emerging Diseases

Abstract

The rapid emergence of infectious diseases poses a persistent and escalating threat to global public health. Traditional disease surveillance systems, though foundational, are increasingly inadequate in detecting and responding to novel pathogens in a timely manner. Artificial Intelligence (AI) has emerged as a transformative force capable of augmenting these systems through real-time analysis of heterogeneous data streams, predictive modelling, and automated pattern recognition. This paper presents a comprehensive review of AI-powered intelligent surveillance frameworks and their role in the early detection of emerging infectious diseases. It examines key application domains including natural language processing for digital epidemiology, machine learning for outbreak forecasting, deep learning for clinical diagnostics, and network analysis for contact tracing. A detailed case study centred on the HealthMap system is presented, including performance metrics, comparative data, and visualised results. The study further addresses methodological approaches commonly used in AI-driven surveillance, limitations such as data privacy, algorithmic bias, and infrastructure gaps, as well as future directions involving federated learning, edge computing, and global AI governance. Findings confirm that AI-based systems significantly outperform traditional surveillance methods in sensitivity, lead time, and geographic coverage.