  The celebrated Abakaliki smallpox data have appeared numerous times in the
epidemic modelling literature, but in almost all cases only a specific subset
of the data is considered. There is one previous analysis of the full data set,
but this relies on approximation methods to derive a likelihood. The data
themselves continue to be of interest due to concerns about the possible
re-emergence of smallpox as a bioterrorism weapon. We present the first full
Bayesian analysis using data-augmentation Markov chain Monte Carlo methods
which avoid the need for likelihood approximations. Results include estimates
of basic model parameters as well as reproduction numbers and the likely path
of infection. Model assessment is carried out using simulation-based methods.
