BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Statistical Analysis of Hospital Infection Data: Models\, Inferenc
 e and Model Choice - Theo Kypraios\, University of Nottingham
DTSTART:20100121T163000Z
DTEND:20100121T173000Z
UID:TALK22553@talks.cam.ac.uk
CONTACT:Olivier Restif
DESCRIPTION:High-profile hospital "superbugs" such as methicillin-resistan
 t Staphylococcus aureus (MRSA) etc have a major impact on healthcare withi
 n the UK and elsewhere. Despite enormous research attention\, many basic q
 uestions concerning the spread  of such pathogens remain unanswered. For i
 nstance what value do  specific control measures such as isolation have?  
 how the spread in the  ward is related to "colonisation pressure"? what ro
 le do the  antibiotics play? how useful it is to have new molecular rapid 
 tests instead of conventional culture-based swab tests?\n\nA wide range of
  biologically-meaningful stochastic transmission models that overcome unre
 alistic assumptions of methods which have been previously used in the lite
 rature are constructed\, in order to address specific scientific hypothese
 s of interest using detailed data from hospital studies. Efficient Markov 
 Chain Monte Carlo (MCMC) algorithms are developed to draw Bayesian inferen
 ce for the parameters which govern transmission. The extent to which the d
 ata support specific scientific\nhypotheses is  investigated by considerin
 g 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.
LOCATION:Meeting room 12\, Centre for Mathematical Sciences
END:VEVENT
END:VCALENDAR