Inferring Viral Transmission Pathways from Within-Host Variation

Abstract

Genome sequencing can offer critical insight into pathogen spread in viral outbreaks, but existing transmission inference methods use simplistic evolutionary models and only incorporate a portion of available genetic data. Here, we develop a robust evolutionary model for transmission reconstruction that tracks the genetic composition of within-host viral populations over time and the lineages transmitted between hosts. We confirm that our model reliably describes within-host variant frequencies in a dataset of 134,682 SARS-CoV-2 deep-sequenced genomes from Massachusetts, USA. We then demonstrate that our reconstruction approach infers transmissions more accurately than two leading methods on synthetic data, as well as in a controlled outbreak of bovine respiratory syncytial virus and an epidemiologically-investigated SARS-CoV-2 outbreak in South Africa. Finally, we apply our transmission reconstruction tool to 5,692 outbreaks among the 134,682 Massachusetts genomes. Our methods and results demonstrate the utility of within-host variation for transmission inference of SARS-CoV-2 and other pathogens, and provide an adaptable mathematical framework for tracking within-host evolution.

Integrating Transmission Dynamics and Pathogen Evolution Through a Bayesian Approach

“…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.…”