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This study developed a new evolutionary model to improve microbial strain typing for epidemiological surveillance. The model accurately estimates genetic distance thresholds and mutation rates for foodborne outbreaks, enhancing public health response.

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Area of Science:

  • Microbiology
  • Epidemiology
  • Genomics

Background:

  • Microbial strain typing is crucial for epidemiological surveillance to identify outbreak sources.
  • Current methods often use predefined thresholds, neglecting pathogen-specific factors like mutation rate and contamination duration.
  • A need exists for models that incorporate outbreak-specific features for accurate genetic distance threshold estimation.

Purpose of the Study:

  • To develop a hypothesis-based model for estimating genetic distance thresholds and mutation rates in point-source, single-strain food or environmental outbreaks.
  • To create a flexible framework that accounts for pathogen mutation rate and outbreak duration.

Main Methods:

  • Developed a forward model simulating bacterial evolution based on mutation rate (μ) and outbreak duration (D).
  • Integrated the model into a Markov Chain Monte Carlo inference framework for parameter estimation.
  • Validated the model using simulated data and analyzed 16 published foodborne outbreak datasets with whole-genome sequencing data.

Main Results:

  • The model accurately discriminated between outbreak and non-outbreak cases and estimated outbreak parameters.
  • Estimation precision increased with higher outbreak durations and mutation rates.
  • Analysis of 16 outbreaks showed consistency with original classifications, identifying outliers and refining outbreak parameters in some cases.

Conclusions:

  • An evolutionary approach provides a robust method for estimating genetic thresholds in single-strain outbreaks.
  • The developed forward model is applicable to foodborne and environmental outbreaks, aiding epidemiological surveillance and control measures.
  • This approach refines the understanding of microbial relatedness in outbreaks by considering evolutionary dynamics.