REAL-TIME PUBLIC HEALTH SURVEILLANCE: ADVANCING EARLY DETECTION THROUGH STREAM PROCESSING

Abstract

Public health surveillance has evolved significantly through the integration of stream processing and advanced machine learning capabilities. The implementation described presents a transformative architecture that addresses longstanding challenges in disease outbreak detection and response coordination. By leveraging real-time data processing from diverse healthcare sources, including emergency departments, primary care facilities, and laboratory networks, the system demonstrates substantial improvements in detection speed and accuracy. The multi-tiered framework incorporates sophisticated data validation protocols, anomaly detection mechanisms, and spatiotemporal clustering algorithms to ensure reliable health monitoring. Enhanced by deep learning models and transfer learning techniques, the system achieves exceptional performance in identifying various health events, from respiratory disease clusters to foodborne illness outbreaks. The integration of Internet of Things (IoT) sensors and genomic surveillance capabilities further strengthens the system's ability to provide early warnings and facilitate rapid response to emerging health threats, ultimately contributing to more effective public health interventions and resource allocation.