Abstract
Genomic data are increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, the phylogenetic trees typically used to represent this variation are not directly informative about who infected whom—a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by coloring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. We also demonstrate how the branch coloring approach can incorporate a variable number of unique colors to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte–Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.
Highlights
Infectious disease epidemiology is increasingly incorporating genomic data into routine public health practice, using genome sequencing for diagnosis, resistance typing, surveillance, and outbreak reconstruction
We originally used a simple susceptible-infected-recovered (SIR) model to evaluate the probability of the transmission tree, and we recently showed we can extend our approach to incorporate other types of epidemiological models (Hatherell et al, 2016)
Let T be the transmission tree, P be the timed phylogenetic tree, θ be the parameters of the transmission and sampling model, and Ne g the within-host effective population size
Summary
Infectious disease epidemiology is increasingly incorporating genomic data into routine public health practice, using genome sequencing for diagnosis, resistance typing, surveillance, and outbreak reconstruction. In the latter use case, we can draw inferences about the order and direction of transmission based on the presence of mutations common to multiple pathogen isolates (Gilchrist et al, 2015; Croucher and Didelot, 2015). While early works in this area assumed that pathogen genomes from a transmission pair should be identical or near-identical, a number of genomic outbreak investigations revealed the complicating factor of within-host evolution (Ypma et al, 2013; Romero-Severson et al, 2014; Worby et al, 2014). Simulation studies have shown that if within-host diversity is ignored, incorrect inferences can be drawn about the transmission events that occurred within an outbreak (Romero-Severson et al, 2014; Worby et al, 2014; Worby and Read, 2015)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
