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Updated: Jul 16, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

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

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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Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

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Published on: June 17, 2019

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.
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.