Bayesian reconstruction of transmission within outbreaks using genomic variants

Area of Science:

• Genomics
• Epidemiology
• Computational Biology

Background:

• Pathogen genome sequencing aids in understanding infectious disease transmission.
• Within-host pathogen genomic variants offer insights into transmission links and direction.
• Challenges include distinguishing true variants from sequencing errors and modeling pathogen dynamics.

Purpose of the Study:

• To introduce BadTrIP, a novel Bayesian method for pathogen transmission inference.
• To explicitly model pathogen evolution, transmission bottlenecks, and sequencing errors.
• To infer host-to-host transmission using both pathogen sequencing and epidemiological data.

Main Methods:

• BadTrIP utilizes a Bayesian framework to model pathogen evolution and transmission.
• It assumes unlinked genomic variants, avoiding complex haplotype reconstruction.
• The method integrates pathogen genomic data with epidemiological information.

Main Results:

• Simulations demonstrate BadTrIP's robustness and accuracy in inferring transmission events.
• The approach efficiently combines genetic and epidemiological data for improved accuracy.
• BadTrIP outperforms existing methods due to its realistic modeling.

Conclusions:

• BadTrIP provides a powerful tool for reconstructing transmission histories from pathogen genomic data.
• The method was successfully applied to the 2014 Ebola outbreak, highlighting its utility.
• Within-host genomic variants are valuable for understanding disease transmission dynamics.