BIG DATA ANALYTICS IN PUBLIC HEALTH: OPPORTUNITIES FOR DISEASE PREVENTION

Abstract

The convergence of high-volume data streams—electronic health records (EHRs), laboratory feeds, mobility traces, social media, environmental sensors, and pathogen genomics—has transformed public health from retrospective reporting to proactive, prevention-oriented intelligence. This paper surveys the foundations, infrastructure, and analytical methods that enable population-scale risk detection and the precise targeting of interventions. We highlight use cases across communicable and non-communicable disease prevention: outbreak early warning, vaccination micro-targeting, behavioral risk stratification, environmental exposure alerts, and policy evaluation. We discuss data governance, privacy-preserving computation, bias mitigation, and equity-centered implementation with a focus on low- and middle-income countries (LMICs). Finally, we propose a stepwise roadmap for building trustworthy, sustainable big-data public health programs.