Syndromic Surveillance and Modeling for Infectious Diseases

The outbreaks of SARS and swine flu have exposed the need for early outbreak detection and effective disease-spread simulation analysis for health resource management under pandemic outbreaks. Current surveillance systems lack the ability to interrogate disparate data and diverse datasets and sources, and are inaccurate in predicting infectious disease outbreaks and spread trends. This research will develop a radically new “syndromic surveillance” approach to enable reliable data-oriented infectious disease forecasting, simulation, and risk analysis. We shall:Develop advanced data-mining methods to understand and extract disease transmission dynamics and mechanisms based on multiple infectious disease data sources.Develop syndromic surveillance methods for analyzing public health related data for early detection of infectious disease outbreaks.Develop stochastic influenza simulation and health economics models for mimicking disease-spread and risk assessment.Validate the proposed research models through simulated outbreaks, clinical experiments and field experiments, and medical data from previous pandemic periods.