Abstract
Exploiting pathogen genomes to reconstruct transmission represents a powerful tool in the fight against infectious disease. However, their interpretation rests on a number of simplifying assumptions that regularly ignore important complexities of real data, in particular within-host evolution and non-sampled patients. Here we propose a new approach to transmission inference called SCOTTI (Structured COalescent Transmission Tree Inference). This method is based on a statistical framework that models each host as a distinct population, and transmissions between hosts as migration events. Our computationally efficient implementation of this model enables the inference of host-to-host transmission while accommodating within-host evolution and non-sampled hosts. SCOTTI is distributed as an open source package for the phylogenetic software BEAST2. We show that SCOTTI can generally infer transmission events even in the presence of considerable within-host variation, can account for the uncertainty associated with the possible presence of non-sampled hosts, and can efficiently use data from multiple samples of the same host, although there is some reduction in accuracy when samples are collected very close to the infection time. We illustrate the features of our approach by investigating transmission from genetic and epidemiological data in a Foot and Mouth Disease Virus (FMDV) veterinary outbreak in England and a Klebsiella pneumoniae outbreak in a Nepali neonatal unit. Transmission histories inferred with SCOTTI will be important in devising effective measures to prevent and halt transmission.
Highlights
Understanding the dynamics of transmission is fundamental for devising effective policies and practical measures that limit the spread of infectious diseases
We present a new tool, SCOTTI, to efficiently reconstruct transmission events within outbreaks
While epidemiological information has been traditionally used to understand who infected whom in an outbreak, detailed genetic information is increasingly becoming available with the steady progress of PLOS Computational Biology | DOI:10.1371/journal.pcbi
Summary
Understanding the dynamics of transmission is fundamental for devising effective policies and practical measures that limit the spread of infectious diseases. The introduction of affordable whole genome sequencing has provided unprecedented detail on the relatedness of pathogen samples [1,2,3,4]. As a result, inferring transmission between hosts with accuracy is becoming more and more feasible. This requires robust, and computationally efficient methods to infer past transmission events using genetic information. Many complications, such as within-host pathogen genetic variation and non-sampled hosts, obscure the relationship between pathogen phylogenies and the history of transmission events, affecting the accuracy of such methods. We present a new approach, SCOTTI, that accounts for these complexities in a computationally feasible manner
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