SCOTTI: Efficient Reconstruction of Transmission within Outbreaks with the Structured Coalescent

TL;DR

SCOTTI is a new computational method that models each host as a separate population to accurately infer transmission events within outbreaks, accounting for within-host evolution and non-sampled hosts, using pathogen genomic data.

Contribution

The paper introduces SCOTTI, a novel statistical framework and software implementation that improves transmission inference by modeling hosts as populations and incorporating within-host evolution and unsampled hosts.

Findings

SCOTTI accurately infers transmission even with high within-host variation.

It accounts for uncertainty due to non-sampled hosts.

It efficiently integrates multiple samples from the same host.

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.