title: Characterizing an outbreak of vancomycin-resistant enterococci using hidden Markov models.
abstract: BACKGROUND
  Antibiotic-resistant nosocomial pathogens can arise in epidemic clusters or sporadically. Genotyping is commonly used to distinguish epidemic from sporadic vancomycin-resistant enterococci (VRE). We compare this to a statistical method to determine the transmission characteristics of VRE.
  
  
  RESULTS
  A structured continuous-time hidden Markov model (HMM) was developed. The hidden states were the number of VRE-colonized patients (both detected and undetected). The input for this study was weekly point-prevalence data; 157 weeks of VRE prevalence. We estimated two parameters: one to quantify the cross-transmission of VRE and the other to quantify the level of VRE colonization from sporadic sources. We compared the results to those obtained by concomitant genotyping and phenotyping. We estimated that 89% of transmissions were due to ward cross-transmission while 11% were sporadic. Genotyping found that 90% had identical glycopeptide resistance genes and 84% were identical or nearly identical on pulsed-field gel electrophoresis (PFGE). There was some evidence, based on model selection criteria, that the cross-transmission parameter changed throughout the study period. The model that allowed for a change in transmission just prior to the outbreak and again at the peak of the outbreak was superior to other models. This model estimated that cross-transmission increased at week 120 and declined after week 135, coinciding with environmental decontamination.
  
  
  CONCLUSIONS
  We found that HMMs can be applied to serial prevalence data to estimate the characteristics of acquisition of nosocomial pathogens and distinguish between epidemic and sporadic acquisition. This model was able to estimate transmission parameters despite imperfect detection of the organism. The results of this model were validated against PFGE and glycopeptide resistance genotype data and produced very similar results. Additionally, HMMs can provide information about unobserved events such as undetected colonization.
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland 4001, Australia. e.mcbryde@qut.edu.au
authors.forename: E S
authors.initials: ES
authors.lastname: McBryde
authors.forename: A N
authors.initials: AN
authors.lastname: Pettitt
authors.forename: B S
authors.initials: BS
authors.lastname: Cooper
authors.forename: D L S
authors.initials: DL
authors.lastname: McElwain
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D004196:Disease Outbreaks; D018562:Disease Transmission, Infectious; D016521:Electrophoresis, Gel, Pulsed-Field; D013293:Enterococcus faecalis / Q000378:metabolism*; D016984:Enterococcus faecium / Q000378:metabolism*; D016908:Gram-Positive Bacterial Infections / Q000453:epidemiology* / Q000382:microbiology / Q000635:transmission; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D020713:Vancomycin Resistance