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

Nicola De Maio, Chieh-Hsi Wu and Daniel J Wilson

PLOS Computational Biology, 2016, vol. 12, issue 9, 1-23

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.Author Summary: We present a new tool, SCOTTI, to efficiently reconstruct transmission events within outbreaks. Our approach combines genetic information from infection samples with epidemiological information of patient exposure to infection. 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 sequencing technologies. However, many complications, if unaccounted for, can affect the accuracy with which the transmission history is reconstructed. SCOTTI efficiently accounts for several complications, in particular within-patient genetic variation of the infectious organism, and non-sampled patients (such as asymptomatic patients). Thanks to these features, SCOTTI provides accurate reconstructions of transmission in complex scenarios, which will be important in finding and limiting the sources and routes of transmission, preventing the spread of infectious disease.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005130

DOI: 10.1371/journal.pcbi.1005130

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