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Efficient MCMC for temporal epidemics via parameter reduction

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  • Xiang, Fei
  • Neal, Peter

Abstract

An efficient, generic and simple to use Markov chain Monte Carlo (MCMC) algorithm for partially observed temporal epidemic models is introduced. The algorithm is designed to be adaptive so that it can easily be used by non-experts. There are two key features incorporated in the algorithm to develop an efficient algorithm, parameter reduction and efficient, multiple updates of the augmented infection times. The algorithm is successfully applied to two real life epidemic data sets, the Abakaliki smallpox data and the 2001 UK foot-and-mouth epidemic in Cumbria.

Suggested Citation

  • Xiang, Fei & Neal, Peter, 2014. "Efficient MCMC for temporal epidemics via parameter reduction," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 240-250.
    • Handle: RePEc:eee:csdana:v:80:y:2014:i:c:p:240-250
    DOI: 10.1016/j.csda.2014.07.002
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    References listed on IDEAS

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    1. P. D. O’Neill & G. O. Roberts, 1999. "Bayesian inference for partially observed stochastic epidemics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 121-129.
    2. McKinley, Trevelyan J. & Ross, Joshua V. & Deardon, Rob & Cook, Alex R., 2014. "Simulation-based Bayesian inference for epidemic models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 434-447.
    3. Tom Britton & Theodore Kypraios & Philip D. O'Neill, 2011. "Inference for Epidemics with Three Levels of Mixing: Methodology and Application to a Measles Outbreak," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(3), pages 578-599, September.
    4. Tom Britton & Philip D. O'Neill, 2002. "Bayesian Inference for Stochastic Epidemics in Populations with Random Social Structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 375-390, September.
    5. Gareth O. Roberts & Jeffrey S. Rosenthal, 1998. "Optimal scaling of discrete approximations to Langevin diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 255-268.
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    Cited by:

    1. Peter Neal & Fei Xiang, 2017. "Collapsing of Non-centred Parameterized MCMC Algorithms with Applications to Epidemic Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 81-96, March.

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