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Related Experiment Videos

Characterizing an outbreak of vancomycin-resistant enterococci using hidden Markov models.

E S McBryde1, A N Pettitt, B S Cooper

  • 1School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland 4001, Australia. e.mcbryde@qut.edu.au

Journal of the Royal Society, Interface
|March 16, 2007
PubMed
Summary

A novel statistical model using hidden Markov models (HMMs) effectively distinguished epidemic from sporadic vancomycin-resistant enterococci (VRE) transmission. This method accurately estimated transmission dynamics, even with undetected cases, and validated against genotyping.

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Area of Science:

  • Infectious disease epidemiology
  • Mathematical modeling in healthcare
  • Nosocomial pathogen surveillance

Background:

  • Antibiotic-resistant pathogens like vancomycin-resistant enterococci (VRE) pose significant healthcare challenges.
  • Distinguishing epidemic from sporadic VRE transmission is crucial for effective control.
  • Genotyping is a common but potentially resource-intensive method for VRE transmission analysis.

Purpose of the Study:

  • To develop and validate a statistical model for estimating VRE transmission characteristics.
  • To compare the performance of a hidden Markov model (HMM) against traditional genotyping methods.
  • To assess the utility of HMMs in analyzing serial prevalence data for nosocomial pathogens.

Main Methods:

  • A continuous-time hidden Markov model (HMM) was developed to analyze weekly VRE prevalence data over 157 weeks.

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  • The model estimated parameters for ward cross-transmission and sporadic colonization.
  • Model performance was compared with concomitant genotyping (glycopeptide resistance genes) and pulsed-field gel electrophoresis (PFGE).

    • Main Results:

    • The HMM estimated that 89% of VRE transmissions were due to ward cross-transmission and 11% were sporadic.
    • Genotyping results showed high similarity (90% identical genes, 84% identical PFGE patterns) among cases.
    • The HMM identified a dynamic change in transmission rates, increasing before an outbreak and declining post-intervention, aligning with environmental decontamination efforts.

    Conclusions:

    • Hidden Markov models (HMMs) are effective for analyzing serial prevalence data to characterize nosocomial pathogen acquisition.
    • HMMs can accurately estimate transmission parameters, even with imperfect detection of colonized individuals.
    • The statistical approach provided comparable and complementary insights to genotyping, offering a robust method for VRE surveillance.