Distributed information fusion models for regional public health surveillance

Biosurveillance systems designed and deployed in the United States and abroad to allow public health authorities to monitor the health of their communities have significant design limitations despite their wide usage. One limitation is the lack of algorithmic solutions to combine local data sources for regional situation awareness. The objective of the current study is to develop and demonstrate the value of automated information fusion methods applied to the distributed neighboring public health sites. A prototype system consisting of distributed Bayesian models was designed to enable informed regional and local cognitive decision support response. The Intelligent Decision Support Network (IDSN) is composed of Bayesian Information Fusion Models (BIFMs) that target a particular syndrome or disease type. Using local data from county health departments in Northern Virginia for the time period between August 2005 and May 2007, we estimated the probability of a gastrointestinal (GI) outbreak in two ways: First, based on data from the local hospitals only; and second, based on the relative probability of outbreak by combining local hospital data and probabilities of GI events from the neighboring counties’ BIFMs. Preliminary findings showed that the network of distributed models detected events that would be undetected without multi-jurisdictional data.

Research highlights
► Biosurveillance systems provide detailed awareness of a community’s health.
► We study the value of fusing regional epidemiologic data for timely detection.
► Distributed Bayesian models enable regional and local decision support response.
► Inclusion of multi-jurisdictional information may improve outbreak sensitivity.