A bayesian network for outbreak detection and prediction

Xia Jiang, Garrick L. Wallstrom

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Health care officials are increasingly concerned with knowing early whether an outbreak of a particular disease is unfolding. We often have daily counts of some variable that are indicative of the number of individuals in a given community becoming sick each day with a particular disease. By monitoring these daily counts we can possibly detect an outbreak in an early stage. A number of classical time-series methods have been applied to outbreak detection based on monitoring daily counts of some variables. These classical methods only give us an alert as to whether there may be an outbreak. They do not predict properties of the outbreak such as its size, duration, and how far we are into the outbreak. Knowing the probable values of these variables can help guide us to a cost-effective decision that maximizes expected utility. Bayesian networks have become one of the most prominent architectures for reasoning under uncertainty in artificial intelligence. We present an intelligent system, implemented using a Bayesian network, which not only detects an outbreak, but predicts its size and duration, and estimates how far we are into the outbreak. We show results of investigating the performance of the system using simulated outbreaks based on real outbreak data. These results indicate that the system shows promise of being able to predict properties of an outbreak.

Original languageEnglish (US)
Title of host publicationProceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
Pages1155-1160
Number of pages6
StatePublished - Nov 13 2006
Event21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06 - Boston, MA, United States
Duration: Jul 16 2006Jul 20 2006

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Other

Other21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
CountryUnited States
CityBoston, MA
Period7/16/067/20/06

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Jiang, X., & Wallstrom, G. L. (2006). A bayesian network for outbreak detection and prediction. In Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06 (pp. 1155-1160). (Proceedings of the National Conference on Artificial Intelligence; Vol. 2).