Abstract Details
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Activity Number:
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492
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #310309 |
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Title:
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Bayesian Inference for Stochastic Epidemic Models with Underlying Network Structure
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Author(s):
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Sudeshna Paul*+
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Companies:
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Emory University
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Keywords:
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epidemic ;
disease ;
social ;
networks ;
stochastic ;
Bayesian
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Abstract:
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Traditional epidemic models assume that disease spreads uniformly in the population. However, this assumption often understates the role of non-homogeneous mixing in populations with geographical and social structure. In this paper, we integrate a social network component to the existing stochastic epidemic models. We utilize Bayesian inference method to estimate the parameters of the epidemic and network model respectively. We use both simulated and real-life epidemic data set to illustrate different features of the model.
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Authors who are presenting talks have a * after their name.
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