Wednesday 12th May 2010 16.00 - 17.00, Room G.107, Alan Turing
Building, University of Manchester. Tea and coffee from 3.30 pm.
Theodore Kypraios (University of Nottingham)
Statistical Analysis of Hospital Infection Data: Models, Inference and Model Choice
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.
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