THE ROLE OF BIG DATA ANALYTICS IN PUBLIC HEALTH SURVEILLANCE AND DISEASE OUTBREAK PREDICTION

Abstract

Big data analytics has emerged as a transformative tool in public health, enabling real-time surveillance, early outbreak detection, and effective response to health crises. This paper explores the integration of big data sources—ranging from electronic health records (EHRs) and social media to environmental sensors and mobile applications—into predictive public health models. We discuss analytical frameworks such as machine learning, natural language processing, and spatial-temporal modeling, which are used to detect patterns, anomalies, and signals indicative of potential disease outbreaks. The paper also reviews notable case studies from Pakistan and global contexts, identifies challenges related to data privacy, infrastructure, and model accuracy, and recommends strategic directions for enhancing public health resilience using big data.