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Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.

Jake Carson1,2,3, Matt Keeling1,2,3, David Wyllie4

  • 1Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.

Molecular Biology and Evolution
|January 3, 2024
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This study introduces a new method for reconstructing pathogen transmission networks using multiple pathogen genomes per host. The approach improves accuracy with more data and estimates key epidemiological parameters, aiding outbreak investigations.

Keywords:
genomic epidemiologyinfectious disease outbreaktransmission analysiswithin-host diversity and evolution

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Area of Science:

  • Epidemiology
  • Genomics
  • Computational Biology

Background:

  • Pathogen genome sequencing aids infectious disease outbreak investigations by resolving transmission pathways.
  • Within-host pathogen evolution complicates direct phylogenetic analysis of transmission events.
  • High uncertainty exists in transmission networks when only one genome per host is available.

Purpose of the Study:

  • To develop a novel methodology for reconstructing pathogen transmission networks using variable numbers of pathogen genomes per host.
  • To remove the assumption of a complete transmission bottleneck in phylogenetic analyses.
  • To enable more precise inference of transmission dynamics and epidemiological parameters.

Main Methods:

  • Developed a new computational methodology to reconstruct transmission networks from multiple pathogen genomes per host.
  • Utilized simulated data to validate the method's accuracy and performance with increasing genomic data per host.
  • Applied the method to real-world outbreak data for Pseudomonas aeruginosa and Klebsiella pneumoniae.

Main Results:

  • Method accuracy increases with the number of genomes sampled per host.
  • The methodology successfully infers key infectious disease parameters, including transmission bottleneck size, within-host growth rate, basic reproduction number, and sampling fraction.
  • Demonstrated practical utility in analyzing cystic fibrosis-associated Pseudomonas aeruginosa and nosocomial Klebsiella pneumoniae outbreaks.

Conclusions:

  • The novel methodology offers a robust framework for transmission network reconstruction, accommodating multiple pathogen genomes per host.
  • It provides a more accurate and flexible approach compared to methods relying on single genomes or complete bottleneck assumptions.
  • This tool enhances the ability to investigate infectious disease outbreaks and understand pathogen transmission dynamics.