Abstract

High-profile hospital “superbugs” such as methicillin-resistant Staphylococcus aureus (MRSA) etc have a major impact on healthcare within the UK and elsewhere. Despite enormous research attention, many basic questions concerning the spread of such pathogens remain unanswered. For instance what value do specific control measures such as isolation have? how the spread in the ward is related to “colonisation pressure”? what role do the antibiotics play? how useful it is to have new molecular rapid tests instead of conventional culture-based swab tests?

A wide range of biologically-meaningful stochastic transmission models that overcome unrealistic assumptions of methods which have been previously used in the literature are constructed, in order to address specific scientific hypotheses of interest using detailed data from hospital studies. Efficient Markov Chain Monte Carlo (MCMC) algorithms are developed to draw Bayesian inference for the parameters which govern transmission. The extent to which the data support specific scientific hypotheses is investigated by considering and comparing different models under a Bayesian framework by employing a trans-dimensional MCMC algorithm while a method of matching the within-model prior distributions is discussed how to avoid miscalculation of the Bayes Factors. Finally, the methodology is illustrated by analysing real data which were obtained from a hospital in Boston.