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Abstract Details
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Activity Number:
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620
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Quality and Productivity
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| Abstract - #302525 |
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Title:
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A Recursive Bayesian Approach in Biosurveillance
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Author(s):
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Gideon Zamba*+
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Companies:
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University of Iowa
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Address:
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Department of Biostatistics, Iowa City, ,
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Keywords:
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Dynamic Control ;
Bayesian Sequential Update ;
Syndromic Surveillance
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Abstract:
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We developed a three-state recursive sequential Bayesian method for signal detection in biosurveillance. The method accounts for non-stationarity, irregularity, seasonality, and captures the serial structural details of an epidemic curve. At stage n, a trichotomous variable governing the states of an epidemic dynamic is defined, and a prior distribution for the time-indexed serial readings is set. The technicality consists of finding a posterior state probability based on the observed data history, using the posterior as a prior distribution for stage n + 1 and sequentially monitoring surges in posterior state probabilities. A sensitivity analysis for validation is conducted and analytical formulas for the predictive distribution are supplied for error management purposes. The method is applied to syndromic surveillance data gathered in the United States (U.S.) District of Columbia metropolitan area.
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