Data Mining for Recognizing Patterns in Foodborne Disease Outbreaks
*Sigurdur Olafsson, Iowa State University Department of Industrial and Manufacturing Systems Engineering Keywords: Foodborne disease outbreaks, surveillance databases, data mining, classification, association rule mining, attribute selection This presentation features three data mining methods, attribute selection, decision tree learning, and association rule discovery, to extract previously unknown and meaningful patterns concerning foodborne disease outbreaks, applied to study the four most common outbreak etiologies in 2006, Salmonella enteritidis, Salmonella typhimurium, Escherichia coli, and Norovirus. The analysis reveals various patterns relating each of these outbreak types to specific foods and consumption locations. Discovery of patterns in foodborne disease outbreak data is useful in determination and implementation of suitable intervention techniques. In particular, custom intervention techniques including specific training methods can be tailored to train individuals in hygienic food handling, preparation, and consumption practices.
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
Short Course materials due from Instructors -
August 26, 2013
Housing Deadline -
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC