Abstract
While the challenges of the next pandemic outbreak are overwhelming, either from swine flu, other infectious disease, bioterrorism, timely detection of disease outbreaks is most important for public health surveillance and society safety and stability. In public health surveillance, the objective is to systematically collect, analyze, and interpret public health data (chronic or infectious diseases) in order to understand trends, to detect changes in disease incidence and death rates, and to plan, implement, and evaluate public health practice. Recently much research has been conducted to develop methods and algorithms for health surveillance and disease detection. This paper presents an overview and reviews the recent research methods on temporal and spatiotemporal surveillance. Specific research challenges and future research directions are discussed. A real life example is used to compare the performance of three currently used surveillance methods, scan, EWMA, and CUSUM.
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