Inference for discretely observed stochastic kinetic networks with applications to epidemic modeling.

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Boseung Choi, Grzegorz A Rempala

Author Information

Boseung Choi: Department of Computer Science and Statistics, Daegu University, Gyeongbuk 712-714, Republic of Korea.

PMID: 21835814 DOI: 10.1093/biostatistics/kxr019

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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R01 DE019243/NIDCR NIH HHS
R01-DE19243/NIDCR NIH HHS

Algorithms

Bayes Theorem

Biostatistics

Data Interpretation, Statistical

Epidemics

Humans

Influenza A Virus, H1N1 Subtype

Influenza, Human

Markov Chains

Models, Statistical

Monte Carlo Method

Pandemics

Stochastic Processes

United States

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

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Open Library of Bioscience