期刊
BIOSTATISTICS
卷 13, 期 1, 页码 153-165出版社
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxr019
关键词
Gibbs sampler; Kinetic constants; Maximum likelihood; SIR model; Stochastic kinetics network
资金
- US National Science Foundation [DMS-0840695, DMS-0941113]
- US National Institutes of Health [R01-DE19243]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0840695] Funding Source: National Science Foundation
We present a new method for Bayesian Markov Chain Monte Carlo-based inference in certain types of stochastic models, suitable for modeling noisy epidemic data. We apply the so-called uniformization representation of a Markov process, in order to efficiently generate appropriate conditional distributions in the Gibbs sampler algorithm. The approach is shown to work well in various data-poor settings, that is, when only partial information about the epidemic process is available, as illustrated on the synthetic data from SIR-type epidemics and the Center for Disease Control and Prevention data from the onset of the H1N1 pandemic in the United States.
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