Digital Epidemiology in Urban Health

Abstract

Urban environments concentrate both health risks and health resources in ways that create complex, spatially patterned disease burdens that conventional epidemiological methods -- designed for populations rather than places -- capture poorly. Digital epidemiology, using real-time data streams from electronic health records, environmental sensors, mobility tracking, social media, and administrative systems, offers the spatial and temporal resolution needed to characterise urban health dynamics and identify intervention opportunities at neighbourhood scale. We evaluated five digital epidemiology frameworks applied across eight European cities -- Madrid, Rome, Amsterdam, Copenhagen, Warsaw, Athens, Prague, and Vienna -- covering 42 million urban residents over 2018 to 2023. Frameworks ranged from enhanced conventional surveillance through fully integrated real-time urban health intelligence platforms combining environmental, mobility, clinical, and social determinant data streams. Performance was benchmarked using our Urban Digital Epidemiology Score (UDES), integrating disease burden characterisation accuracy, health disparity detection sensitivity, intervention targeting precision, policy response speed, and data privacy compliance. The integrated real-time urban health intelligence framework achieved the highest UDES (0.917), detecting neighbourhood-level health disparity signals 12.4 days earlier than conventional surveillance and enabling targeted intervention deployment that reduced respiratory emergency department attendance by 18.6% in high-burden areas. Heat-related illness showed the strongest digital epidemiology advantage, with real-time temperature and vulnerability mapping enabling proactive outreach to high-risk households 48 hours before heat wave peaks