title: Inference for discretely observed stochastic kinetic networks with applications to epidemic modeling.
abstract: 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.
authors.affiliation: Department of Computer Science and Statistics, Daegu University, Gyeongbuk 712-714, Republic of Korea.
authors.forename: Boseung
authors.initials: B
authors.lastname: Choi
authors.forename: Grzegorz A
authors.initials: GA
authors.lastname: Rempala
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D056808:Biostatistics; D003627:Data Interpretation, Statistical; D058872:Epidemics / Q000706:statistics & numerical data*; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000453:epidemiology; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D058873:Pandemics / Q000706:statistics & numerical data; D013269:Stochastic Processes; D014481:United States / Q000453:epidemiology