A Study into Detection of Bio-Events in Multiple Streams of Surveillance Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4506)


This paper reviews the results of a study into combining evidence from multiple streams of surveillance data in order to improve timeliness and specificity of detection of bio-events. In the experiments we used three streams of real food- and agriculture-safety related data that is being routinely collected at slaughter houses across the nation, and which carry mutually complementary information about potential outbreaks of bio-events. The results indicate that: (1) Non-specific aggregation of p-values produced by event detectors set on individual streams of data can lead to superior detection power over that of the individual detectors, and (2) Design of multi-stream detectors tailored to the particular characteristics of the events of interest can further improve timeliness and specificity of detection. In a practical setup, we recommend combining a set of specific multi-stream detectors focused on individual types of predictable and definable scenarios of interest, with non-specific multi-stream detectors, to account for both anticipated and emerging types of bio-events.


Data Stream Syndromic Surveillance Multiple Stream Real Food Slaughter House 
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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.The Auton Lab, Carnegie Mellon University, Pittsburgh, PAUSA

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