ARTIFICIAL INTELLIGENCE IN DISEASE OUTBREAK PREDICTION AND MODELING: A COMPREHENSIVE REVIEW

Abstract

Infectious disease outbreaks continue to pose a persistent threat to global health, and the tools used to anticipate them have undergone a rapid transformation over the past decade. Traditional epidemiological approaches, including compartmental models such as SIR and SEIR and classical time-series methods, provided the backbone of outbreak forecasting for much of the twentieth century but are increasingly limited by rigid assumptions, poor scalability, and an inability to absorb heterogeneous real-time data. Artificial intelligence (AI), spanning classical machine learning, deep learning, graph-based architectures, and most recently large language models (LLMs), has emerged as a complementary and in many cases superior tool for outbreak prediction. This review synthesizes recent literature (2022-2026) on AI applications across disease surveillance, forecasting, and modeling. It examines the major technical approaches (machine learning, deep learning, graph neural networks, hybrid physics-informed models, and NLP/LLM-based epidemic intelligence), the data streams that feed them (epidemiological records, mobility and travel data, social media and news text, wastewater signals, and climate/ecological data), and representative case studies including COVID-19, seasonal influenza, avian influenza (H5N1), dengue, and pox. It then critically appraises the field's persistent challenges, namely data quality and bias, model interpretability, integration into public health decision-making, and ethical concerns around privacy and equity, before outlining emerging directions such as federated learning, explainable AI, and multimodal LLM-based forecasting systems.