A data fusion approach to biosurveillance

Joshua E. Introne, Igor Levit, Scott Harrison, Subrata Das

Research output: Chapter in Book/Entry/PoemConference contribution

3 Scopus citations


A significant hurdle in biosurveillance is how to detect attacks with unknown bioagents. One reason for this is that there is a high degree of variance in medical reporting in such cases, masking statistical anomalies that might otherwise be apparent. In this article, we present an application that employs a novel two-level fusion architecture designed to contend with this problem. The lower-level fusion step detects and tracks the indication of an outbreak of some sort given a set of noisy patient records, based on an information retrieval technique called Latent Semantic Analysis. The higher level fusion step then determines the type of outbreak, based on dynamic Bayesian Networks that model cause-effect interrelationships among several sources of information such as terrorist activities, environment, diseases and symptoms. We have developed and demonstrated feasibility of the approach via simulated outbreak events provided by the BioWar simulation platform.

Original languageEnglish (US)
Title of host publication2005 7th International Conference on Information Fusion, FUSION
PublisherIEEE Computer Society
Number of pages8
ISBN (Print)0780392868, 9780780392861
StatePublished - 2005
Externally publishedYes
Event2005 8th International Conference on Information Fusion, FUSION - Philadelphia, PA, United States
Duration: Jul 25 2005Jul 28 2005

Publication series

Name2005 7th International Conference on Information Fusion, FUSION


Conference2005 8th International Conference on Information Fusion, FUSION
Country/TerritoryUnited States
CityPhiladelphia, PA


  • Biosurveillance
  • Data fusion
  • Dynamic bayesian networks
  • Latent semantic analysis
  • Syndromic surveillance

ASJC Scopus subject areas

  • General Engineering


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