A stochastic epidemic model with several kinds of susceptible is used to analyse temporal disease outbreak data from a Bayesian perspective. Prior distributions are used to model uncertainty in the actual numbers of susceptibles initially present. The posterior distribution of the parameters of the model is explored via Markov chain Monte Carlo methods. The methods are illustrated using two datasets, and the results are compared where possible to results obtained by previous analyses.
- Bayesian inference
- Gibbs sampler
- Markov chain Monte Carlo methods
- Metropolis-Hastings algorithm
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty