AI-based epidemic early warning using social media and search engine signals: A scoping review

Abstract

Background Early detection of infectious disease outbreaks is essential for timely public health response. Artificial intelligence (AI) combined with digital traces from social media and search engines may strengthen epidemic early warning systems (EWS). Objective To map how AI-based EWS use social media and search engine signals for outbreak detection/forecasting, and to characterize whether studies integrate clinical and climate/environmental data. Methods Following PRISMA-ScR, we reviewed 38 studies (past five years) across six databases. We extracted study characteristics, data sources, target diseases, prediction tasks, validation practices, and performance metrics. For cross-tabulated evidence maps, each study was coded once using its primary data-source category and primary modeling approach to avoid double-counting. Results Deep learning and ensemble approaches were the most common methods. COVID-19 and influenza-like illness dominated the literature. Few studies combined social media and search engine signals. Clinical and climate/environmental data were rarely integrated. No study reported prospective, real-time evaluation. Geographic coverage was skewed toward high-income settings. Conclusion AI-based epidemic early warning systems using digital signals show promise for outbreak detection and forecasting. However, the evidence remains largely retrospective, fragmented, and geographically uneven. Future research should prioritize multi-stream data integration, equity-aware design, and prospective validation to support real-world global applicability.