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Abstract Details
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Activity Number:
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117
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 1, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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| Abstract - #301878 |
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Title:
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Bayesian Spatio-Temporal Syndromic Surveillance: An Epidemiological Prospective
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Author(s):
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Jian Zou*+
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Companies:
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National Institute of Statistical Sciences
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Address:
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19 T.W. Alexander Drive, RTP, NC, 27709,
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Keywords:
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Spatio-temporal ;
Syndromic surveillance ;
Markov random field ;
SIR model
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Abstract:
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Early and accurate detection of outbreaks is one of the most important objectives of syndromic surveillance systems. This talk proposes a general Bayesian framework for the syndromic surveillance system. The methodology incorporates Gaussian Markov random field (GMRF) and spatial-temporal CAR modeling. The disease transmission mechanism is modeled through a person-to-person contact basis. This is comparable to traditional epidemiological models such as the SIR model. The model has some nice probabilistic representations, as well as attractive statistical properties. We demonstrated that the model is capable of capturing early outbreaks through extensive simulation studies and synthetic data generated from a dynamic SIR model.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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