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Bayesian Inference for Stochastic Epidemics in Populations with Random Social Structure

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  • TOM BRITTON
  • PHILIP D. O'NEILL

Abstract

A single‐population Markovian stochastic epidemic model is defined so that the underlying social structure of the population is described by a Bernoulli random graph. The parameters of the model govern the rate of infection, the length of the infectious period, and the probability of social contact with another individual in the population. Markov chain Monte Carlo methods are developed to facilitate Bayesian inference for the parameters of both the epidemic model and underlying unknown social structure. The methods are applied in various examples of both illustrative and real‐life data, with two different kinds of data structure considered.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:scjsta:v:29:y:2002:i:3:p:375-390
    DOI: 10.1111/1467-9469.00296
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    Cited by:

    1. Gail E. Potter & Niel Hens, 2013. "A penalized likelihood approach to estimate within-household contact networks from egocentric data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 629-648, August.
    2. Razvan G. Romanescu & Rob Deardon, 2017. "Fast Inference for Network Models of Infectious Disease Spread," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 666-683, September.
    3. Tom Britton, 2020. "Epidemic models on social networks—With inference," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 222-241, August.
    4. Chris Groendyke & David Welch & David R. Hunter, 2012. "A Network-based Analysis of the 1861 Hagelloch Measles Data," Biometrics, The International Biometric Society, vol. 68(3), pages 755-765, September.
    5. Xiang, Fei & Neal, Peter, 2014. "Efficient MCMC for temporal epidemics via parameter reduction," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 240-250.
    6. Kerstin Awiszus & Thomas Knispel & Irina Penner & Gregor Svindland & Alexander Vo{ss} & Stefan Weber, 2022. "Modeling and Pricing Cyber Insurance -- Idiosyncratic, Systematic, and Systemic Risks," Papers 2209.07415, arXiv.org, revised Dec 2022.
    7. Stephen E. Chick & Sada Soorapanth & James S. Koopman, 2003. "Inferring Infection Transmission Parameters That Influence Water Treatment Decisions," Management Science, INFORMS, vol. 49(7), pages 920-935, July.

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