Efficient MCMC for temporal epidemics via parameter reduction

An efficient, generic and simple to use Markov chain Monte Carlo (MCMC) algorithm for partially observed temporal epidemic models is introduced. The algorithm is designed to be adaptive so that it can easily be used by non-experts. There are two key features incorporated in the algorithm to develop an efficient algorithm, parameter reduction and efficient, multiple updates of the augmented infection times. The algorithm is successfully applied to two real life epidemic data sets, the Abakaliki smallpox data and the 2001 UK foot-and-mouth epidemic in Cumbria.