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Abstract Details
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Activity Number:
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3
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Type:
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Invited
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #300365 |
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Title:
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Multivariate Bayesian Modeling of Unknown Diseases for Biosurveillance
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Author(s):
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Yanna Shen*+
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Companies:
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National Institutes of Health
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Address:
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National Library of Medicine, Bethesda, MD, 20894,
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Keywords:
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Bayesian modeling ;
unknown causes of events ;
disease outbreak detection
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Abstract:
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This research investigates Bayesian modeling of known and unknown causes of events in the context of disease-outbreak detection. We introduce a multivariate Bayesian approach that models multiple evidential features of every person in the population. This approach models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A contribution of this research is that it introduces a multivariate Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has general applicability in domains where the space of known causes is incomplete.
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Authors who are presenting talks have a * after their name.
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