Bayesian inference for partially observed stochastic epidemics

The analysis of infectious disease data is usually complicated by the fact that real life epidemics are only partially observed. In particular, data c oncerning the process of infection are seldom available. Consequently, stan dard statistical techniques can become too complicated to implement effecti vely. In this paper Markov chain Monte Carlo methods are used to make infer ences 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 dis ease outbreaks.