Bayesian CAR models for syndromic surveillance on multiple data streams: Theory and practice |
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Authors: | David Banks Gauri DattaAlan Karr James LynchJarad Niemi Francisco Vera |
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Affiliation: | a Dept. of Statistical Science, Duke University, Box 90251, Durham, NC 27708, United States b Department of Statistics, University of Georgia, Athens, GA 30602, United States c National Institute of Statistical Sciences, Research Triangle Park, NC 27709, United States d Department of Statistics, University of South Carolina, Columbia, SC 29208, United States e Department of Mathematical Sciences, Clemson University, Clemson, SC 29634, United States |
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Abstract: | Syndromic surveillance has, so far, considered only simple models for Bayesian inference. This paper details the methodology for a serious, scalable solution to the problem of combining symptom data from a network of US hospitals for early detection of disease outbreaks. The approach requires high-end Bayesian modeling and significant computation, but the strategy described in this paper appears to be feasible and offers attractive advantages over the methods that are currently used in this area. The method is illustrated by application to ten quarters worth of data on opioid drug abuse surveillance from 636 reporting centers, and then compared to two other syndromic surveillance methods using simulation to create known signal in the drug abuse database. |
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Keywords: | CAR models Bayes Syndromic surveillance Gibbs distribution Markov random field |
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