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Proceedings Paper

Data modeling for detection of epidemic outbreak
Author(s): Holger M. Jaenisch; James W. Handley; Kristina L. Jaenisch; Michael S. Conn; Jeffrey P. Faucheux
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Paper Abstract

Data Modeling is successfully applied to outbreak detection using epidemicological time series data. With proper selection of features, same day detection was demonstrated. Predictive Data Models are derived from the features in the form of integro-differential equations or their solution. These models are used as real-time change detectors. Data Modeling enables change detection using only nominal (no-outbreak) examples for training. Modeling naturally occurring dynamics due to assignable causes such as flu season enables distinction to be made of chemical and biological (chem-bio) causes.

Paper Details

Date Published: 20 May 2005
PDF: 12 pages
Proc. SPIE 5778, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IV, (20 May 2005); doi: 10.1117/12.603374
Show Author Affiliations
Holger M. Jaenisch, dtech Systems Inc. (United States)
James Cook Univ. (Australia)
James W. Handley, James Cook Univ. (United States)
Sparta, Inc. (United States)
Kristina L. Jaenisch, Huntsville Hospital (United States)
Michael S. Conn, Sparta, Inc. (United States)
Jeffrey P. Faucheux, Sparta, Inc. (United States)


Published in SPIE Proceedings Vol. 5778:
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IV
Edward M. Carapezza, Editor(s)

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