Artificial Intelligence-Driven Approaches to Disease Surveillance and Outbreak Prediction

Artificial Intelligence (AI) has emerged as a promising approach to enhance disease surveillance and support outbreak prediction in public health. Conventional surveillance systems, while foundational, are often limited by reporting delays, under-detection, and challenges in handling large and complex data streams. Recent advances in AI including machine learning, natural language processing, and deep learning offer new opportunities to address these limitations by enabling automated case detection, syndromic surveillance, real-time anomaly detection, and predictive modeling. This review synthesizes current evidence on AI-driven approaches to disease surveillance and outbreak prediction, focusing on methodological frameworks, data sources, and applications across infectious disease contexts. Key AI-based surveillance strategies, outbreak prediction models, and forecasting techniques are discussed alongside emerging data sources such as electronic health records, environmental data, mobility data, and digital media. The review also highlights challenges related to data quality, interpretability, ethical considerations, and integration with traditional surveillance systems. By summarizing existing knowledge, this review aims to inform future research and support the responsible adoption of AI technologies in public health surveillance and outbreak preparedness.