Abstract
Health surveillance can be viewed as an ongoing systematic collection, analysis, and interpretation of data for use in planning, implementation, and evaluation of a given health system, in potentially multiple spheres (ex: animal, human, environment). As we move into a sophisticated technologically advanced era, there is a need for cost-effective and efficient health surveillance methods and systems that will rapidly identify potential bioterrorism attacks and infectious disease outbreaks. The main objective of such methods and systems would be to reduce the impact of an outbreak by enabling appropriate officials to detect it quickly and implement timely and appropriate interventions. Identifying an outbreak and/or potential bioterrorism attack days to weeks earlier than traditional surveillance methods would potentially result in a reduction in morbidity, mortality, and outbreak associated economic consequences. Proposed here is a novel framework that would enable a user and/or a system to interpret the anomaly detection results generated via multiple aberration detection algorithms with some indication of confidence. A framework that takes into account the relationships between algorithms and produces an unbiased confidence measure for identification of start of an outbreak.