ARTIFICIAL INTELLIGENCE-BASED EARLY WARNING SYSTEMS FOR PREDICTING EMERGING INFECTIOUS DISEASE OUTBREAKS USING MULTISOURCE POPULATION HEALTH DATA

Abstract

The rapid global emergence of novel infectious diseases has exposed the limitations of traditional, reactive public health surveillance systems, which depend on clinical case reporting and consequently suffer from multi-week diagnostic delays. This review examines the paradigm shift toward Artificial Intelligence (AI)-based early warning systems that leverage multisource population health data to enable proactive, predictive epidemic intelligence. We synthesize current evidence on four primary data streams: wastewater-based epidemiology, pathogen genomics and metagenomic deconvolution, digital syndromic signals including Internet-of-Things data, and clinical electronic health records. The analytical architectures underpinning these systems spatiotemporal graph neural networks with dynamic attention mechanisms, epidemiology- and physics-informed neural networks, metapopulation attention networks, and reinforcement learning agents are critically evaluated for their forecasting capabilities. Empirical evidence indicates that wastewater surveillance can detect pathogen signals four to fourteen days before clinical surges, while epidemiology-informed models retain balanced accuracy of 81.53% and AUROC of 96.79% at seven-day horizons, substantially outperforming unconstrained deep learning baselines that underestimate outbreak peaks by 20–40%. However, significant operational bottlenecks persist, including a performance-translation gap, severe false-alert burdens with positive predictive values as low as 12%, algorithmic bias arising from the digital divide, and cross-jurisdictional data governance barriers. We conclude that realizing the full potential of AI-driven early warning requires embedding epidemiological constraints within model architectures, deploying privacy-preserving federated learning frameworks, implementing mandatory fairness auditing, and developing closed-loop prediction-intervention-evaluation systems that translate predictive intelligence into equitable, actionable public health responses.