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Analysing pneumococcal invasiveness using Bayesian models of pathogen progression rates.

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New Bayesian models quantify how often asymptomatic bacteria like Streptococcus pneumoniae become invasive disease. This helps identify high-risk strains and track vaccine effectiveness, crucial for public health surveillance.

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Area of Science:

  • Microbiology and Infectious Diseases
  • Computational Biology and Bioinformatics
  • Epidemiology and Public Health

Background:

  • Opportunistic pathogens cause disease based on colonization prevalence and progression to symptomatic illness.
  • Emergence of "hyperinvasive" strains of commensals can increase infection rates.
  • Identifying these strains requires quantifying progression from carriage to disease.

Purpose of the Study:

  • To develop and implement Bayesian models for analyzing pathogen progression rates from carriage to invasive disease.
  • To assess the accuracy and informativeness of these models using existing datasets.
  • To enable hypothesis testing and identify high-risk microbial variants.

Main Methods:

  • Development of Bayesian statistical models implemented in an RStan package.
  • Analysis of matched samples from disease cases and healthy carriers.
  • Model convergence assessment and comparison with meta-analysis data.

Main Results:

  • Bayesian models provided stable and accurate fits, reproducing meta-analysis observations for Streptococcus pneumoniae.
  • Estimates of invasiveness correlated with existing metrics, with Bayesian models offering more informative results at smaller sample sizes.
  • Identification of rare but high-risk S. pneumoniae serotypes and significant within-strain/serotype variation in invasiveness.

Conclusions:

  • The developed Bayesian models are effective tools for quantifying pathogen invasiveness and identifying high-risk strains.
  • Genomic surveillance is crucial for evaluating public health interventions and detecting emerging invasive variants.
  • Understanding geographical variations in pathogen genotypes is key to assessing vaccination impact.