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A Two-interval Forced-choice Task for Multisensory Comparisons
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Comparing

Benjamin Sobkowiak1,2, Kamila Romanowski2,3, Inna Sekirov2,4

  • 1Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada.

Epidemiology and Infection
|June 9, 2023
PubMed
Summary
This summary is machine-generated.

Genomic epidemiology tools for tracking tuberculosis (TB) transmission show variable accuracy. While specific, models like TransPhylo, Outbreaker2, and Phybreak identified true TB transmission links more effectively.

Keywords:
Bacterial infectionsbioinformaticsinfectious disease epidemiologytransmissiontuberculosis (TB)

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Area of Science:

  • Genomic epidemiology
  • Computational biology
  • Infectious disease dynamics

Background:

  • Genomic epidemiology tools are vital for understanding infectious disease transmission.
  • Tuberculosis (TB) presents complex epidemiological challenges, including latency and within-host variation.
  • The performance of genomic tools for TB transmission reconstruction remains unevaluated.

Purpose of the Study:

  • To systematically compare the accuracy of six computational tools for reconstructing Mycobacterium tuberculosis transmission networks.
  • To evaluate tool performance using simulated and real-world TB outbreak data.
  • To inform the selection of appropriate tools for TB transmission analyses.

Main Methods:

  • Systematic comparison of six publicly available transmission reconstruction models.
  • Evaluation using simulated Mycobacterium tuberculosis outbreaks.
  • Assessment against real-world TB cluster data, focusing on epidemiologically supported case-contact pairs.

Main Results:

  • Observed significant variability in the number and accuracy of predicted transmission links across models.
  • Simulated outbreaks showed low accuracy in predicting known transmission events.
  • Real-world TB clusters revealed a low proportion of identified epidemiologically supported case-contact pairs.
  • High specificity was noted for all models; TransPhylo, Outbreaker2, and Phybreak showed a higher proportion of true transmission links.

Conclusions:

  • The performance of genomic epidemiology tools varies for tuberculosis transmission reconstruction.
  • Models like TransPhylo, Outbreaker2, and Phybreak demonstrated better identification of true TB transmission links.
  • Caution is advised when interpreting probabilistic transmission networks for TB due to performance limitations.