A study into detection of bio-events in multiple streams of surveillance data

Authors:
Josep Roure;Artur Dubrawski;Jeff Schneider
Affiliations:
The Auton Lab, Carnegie Mellon University, Pittsburgh, PA;The Auton Lab, Carnegie Mellon University, Pittsburgh, PA;The Auton Lab, Carnegie Mellon University, Pittsburgh, PA
Venue:
BioSurveillance'07 Proceedings of the 2nd NSF conference on Intelligence and security informatics: BioSurveillance
Year:
2007

Citing 1
Cited 1

Monitoring food safety by detecting patterns in consumer complaints

IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2

Dynamic Network Model for Predicting Occurrences of Salmonella at Food Facilities

BioSecure '08 Proceedings of the 2008 International Workshop on Biosurveillance and Biosecurity

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 nonspecific multi-stream detectors, to account for both anticipated and emerging types of bio-events.