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General Departmental Seminar Series

Bayesian Biosurveillance Using Causal Networks

Greg Cooper, PhD, Dept. of Medicine, University of Pittsburgh.

Friday, October 17, 2003, 12:00 - 1:00 p.m.

1221 Computer Sciences and Statistics Center, (1210 W. Dayton St.)

ABSTRACT

This talk will describe recent research on a method for detecting disease outbreaks that is based on Bayesian causal modeling of each individual in the population. For people who have entered the healthcare system recently (e.g., visited an emergency department), symptoms and other evidence may be available (in a de-identified format). For other individuals, evidence might only include demographic information, such as age, gender, and home zip code. The models of individuals are linked by common causes of an outbreak (e.g., airborne anthrax). The linked models of individuals form a population model. Detection involves performing inference on the population model to derive the posterior probabilities of various types of outbreaks given available evidence. In addition to providing an overview of the modeling framework being used, this talk will describe techniques for achieving computational tractability when performing inference on a population model that contains millions of variables.

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