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Bayesian inference for partially observed stochastic epidemics

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  • P. D. O’Neill
  • G. O. Roberts

Abstract

The analysis of infectious disease data is usually complicated by the fact that real life epidemics are only partially observed. In particular, data concerning the process of infection are seldom available. Consequently, standard statistical techniques can become too complicated to implement effectively. In this paper Markov chain Monte Carlo methods are used to make inferences about the missing data as well as the unknown parameters of interest in a Bayesian framework. The methods are applied to real life data from disease outbreaks.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:162:y:1999:i:1:p:121-129
    DOI: 10.1111/1467-985X.00125
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