Public Health Emergency Monitoring (HEM) System for Early Disease Outbreak Detection and Transmission Patterns Estimation

An effective public health monitoring system is essential to detecting infectious disease outbreaks in time, i.e., before they spread, cost lives and become difficult to control. In this paper, we propose a novel Health Emergency Monitoring (HEM) system which detects and identifies an emerging health emergency and corresponding infectious disease characteristics from Open Source Data (OSD) using a taxonomy driven actionable knowledge extraction and fuzzy preference argumentation, while correlating the detections about the disease with georeferenced census data to infer any pertinent social, cultural, demographic, economic and geographic indicators of the disease spread. As part of HEM, a probabilistic epidemiological model is used to compute the estimates of disease transmission patterns and other epidemiological outcomes using data from public health records. The HEM system has been tested and validated using relevant COVID-19 and Ebola OSD, proving its feasibility.