title: A stochastic model for MRSA transmission within a hospital ward incorporating environmental contamination.
abstract: Methicillin-resistant Staphylococcus aureus (MRSA) transmission in hospital wards is associated with adverse outcomes for patients and increased costs for hospitals. The transmission process is inherently stochastic and the randomness emphasized by the small population sizes involved. As such, a stochastic model was proposed to describe the MRSA transmission process, taking into account the related contribution and modelling of the associated microbiological environmental contamination. The model was used to evaluate the performance of five common interventions and their combinations on six potential outcome measures of interest under two hypothetical disease burden settings. The model showed that the optimal intervention combination varied depending on the outcome measure and burden setting. In particular, it was found that certain outcomes only required a small subset of targeted interventions to control the outcome measure, while other outcomes still reported reduction in the outcome distribution with up to all five interventions included. This study describes a new stochastic model for MRSA transmission within a ward and highlights the use of the generalized Mann-Whitney statistic to compare the distribution of the outcome measures under different intervention combinations to assist in planning future interventions in hospital wards under different potential outcome measures and disease burden.
authors.affiliation: School of Mathematical Sciences,Queensland University of Technology,Brisbane,Queensland,Australia.
authors.forename: X J
authors.identifier: 0000-0002-8803-2810
authors.initials: XJ
authors.lastname: Lee
authors.affiliation: Centre of Research Excellence in Reducing Healthcare Associated Infections (CRE-RHAI), Queensland University of Technology,Brisbane,Queensland,Australia.
authors.forename: G R
authors.initials: GR
authors.lastname: Fulford
authors.affiliation: School of Mathematical Sciences,Queensland University of Technology,Brisbane,Queensland,Australia.
authors.forename: A N
authors.initials: AN
authors.lastname: Pettitt
authors.affiliation: Institute of Applied Mathematics and Information Technology (CNR IMATI), Italian National Research Council,Milano,Italy.
authors.forename: F
authors.initials: F
authors.lastname: Ruggeri
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D003428:Cross Infection / Q000517:prevention & control / Q000635:transmission*; D018562:Disease Transmission, Infectious / Q000517:prevention & control*; D004783:Environmental Microbiology; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D017053:Infection Control / Q000379:methods*; D008297:Male; D055624:Methicillin-Resistant Staphylococcus aureus / Q000302:isolation & purification*; D008875:Middle Aged; D015233:Models, Statistical; D013203:Staphylococcal Infections / Q000517:prevention & control / Q000635:transmission*; D055815:Young Adult
keywords: Methicillin-resistant S. aureus (MRSA); modelling