The increased threat of bioterrorism and naturally occurring diseases, such as pandemic influenza, continually forces public health authorities to review methods for evaluating data and reports. The objective of bio-surveillance is to automatically process large amounts of information in order to rapidly provide the user with a situational awareness. Most systems currently deployed in health departments use only statistical algorithms to filter data for decision-making. These algorithms are capable of high sensitivity, but this sensitivity comes at the cost of excessive false positives [2], especially when multiple syndrome groups and data types are processed.
Objective
An intelligent information fusion approach is proposed to identify and provide early alerting of naturally-occurring disease outbreaks, as well as bioterrorist attacks, while reducing false positives. The proposed system statistically preprocesses information from multiple sources and fuses it in a manner comparable with the domain expert's decision-making process. Currently, system users lower the false alarm rate by "explaining away" the statistical data anomalies with alternative hypotheses derived from external, non-syndromic knowledge. We seek to incorporate this heuristic decision-making into a probabilistic network that accepts the outputs of statistical algorithms in a hybrid model of domain knowledge and data inference.