Inference of infectious disease transmission using multiple genomes per host

Abstract

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

Integrating Transmission Dynamics and Pathogen Evolution Through a Bayesian Approach

“…However, it requires user-defined exposure intervals during which a host can be part of an epidemic, and does not allow for transmission bottlenecks. TransPhylo (Didelot et al 2017; Didelot et al 2021; Carson et al 2024) can handle incomplete sampling of infected individuals but requires the input of a pre-estimated phylogenetic tree of the pathogen. The phybreak (Klinkenberg et al 2017) package circumvents this by attempting simultaneous transmission and viral gene tree inference and accounting for within-host variation and transmission bottleneck.…”

“…It does not explicitly model the bottleneck and was tested only on simulations where full sampling is assumed. Finally, there have been recent advancements in highly scalable models for transmission network inference, when taking into account the within host pathogen diversity (Skums et al 2022; Specht et al 2023; Carson et al 2024). However, they either require a time scaled phylogeny as an input (Skums et al 2022; Carson et al 2024), thus potentially limiting the full exploration of phylogenetic uncertainties, or are non-tree based (Specht et al 2023).…”

“…Finally, there have been recent advancements in highly scalable models for transmission network inference, when taking into account the within host pathogen diversity (Skums et al 2022; Specht et al 2023; Carson et al 2024). However, they either require a time scaled phylogeny as an input (Skums et al 2022; Carson et al 2024), thus potentially limiting the full exploration of phylogenetic uncertainties, or are non-tree based (Specht et al 2023). Moreover, a common limitation among these models is their inability to explicitly model unobserved transmission events or to accurately infer the timing of such events.…”