Abstract Details
Activity Number:
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550
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #310478 |
Title:
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Bayesian Inference for Early Detection of Influenza Epidemics
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Author(s):
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Xian Yu*+ and Didun Peng
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Companies:
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University of Arkansas at Little Rock and University of Arkansas at Little Rock
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Keywords:
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Bayesian inference ;
Triangular prior ;
Binomial Markov chain ;
Influenza epidemics ;
Hierarchical Bayes Model
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Abstract:
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Early and accurate detection of outbreaks of epidemics is one of the most important objectives of influenza surveillance systems. We propose a hierarchical Bayesian change-point model for detection of influenza epidemics under binomial Markov processes framework. Prior probability of a change-point depends on factors that affect the spread of influenza. The proposed hierarchical Bayesian models with triangular prior distribution couple with CUSUM detection procedures are applied to the 2001-2009, 2010-2013 seasonal influenza and 2009 H1N1 influenza pandemic data published by the Centers for Disease Control and Prevention (CDC).
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Authors who are presenting talks have a * after their name.
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