Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks

Abstract

Whole-genome sequencing of pathogens from host samples becomes more and more routine during infectious disease outbreaks. These data provide information on possible transmission events which can be used for further epidemiologic analyses, such as identification of risk factors for infectivity and transmission. However, the relationship between transmission events and sequence data is obscured by uncertainty arising from four largely unobserved processes: transmission, case observation, within-host pathogen dynamics and mutation. To properly resolve transmission events, these processes need to be taken into account. Recent years have seen much progress in theory and method development, but existing applications make simplifying assumptions that often break up the dependency between the four processes, or are tailored to specific datasets with matching model assumptions and code. To obtain a method with wider applicability, we have developed a novel approach to reconstruct transmission trees with sequence data. Our approach combines elementary models for transmission, case observation, within-host pathogen dynamics, and mutation, under the assumption that the outbreak is over and all cases have been observed. We use Bayesian inference with MCMC for which we have designed novel proposal steps to efficiently traverse the posterior distribution, taking account of all unobserved processes at once. This allows for efficient sampling of transmission trees from the posterior distribution, and robust estimation of consensus transmission trees. We implemented the proposed method in a new R package phybreak. The method performs well in tests of both new and published simulated data. We apply the model to five datasets on densely sampled infectious disease outbreaks, covering a wide range of epidemiological settings. Using only sampling times and sequences as data, our analyses confirmed the original results or improved on them: the more realistic infection times place more confidence in the inferred transmission trees.

outbreaker2: a modular platform for outbreak reconstruction

“…We compared the performance of o2mod.TransPhylo and TransPhylo by reconstructing simulated outbreaks, using the simulator in the phybreak package described described by Klinkenberg et al [14]. We used epidemiological and evolutionary parameters of Ebola virus as a plausible use case (Table 2), and assumed a linearly growing within-host pathogen population size.…”

“…A large number of studies have addressed this problem in recent years (Table 1) [6][7][8][9][10][11][12][13][14][15]. These approaches differ in multiple ways, including in their underlying epidemiological models (e.g.…”

“…SIR [8,11], SEIR [9,12] or branching process models [7,14,15]) and genetic models (e.g. non-phylogenetic [7,10,12,16] or phylogenetic models [11,[13][14][15]), as well as their ability to account for unobserved cases and multiple infectious introductions. This methodological diversity is beneficial, providing various theoretical frameworks for outbreak reconstruction in different epidemic scenarios.…”

“…[7]). This ranges from simple implementation issues, such as being limited to specific distributions for priors [14,15], to more fundamental restrictions on the inferential process itself, in that the underlying epidemiological and genetic models are hardcoded and not customisable by the user.…”

“…Most consider the same data, namely WGS and some form of temporal data (i.e. dates of symptom onset and assumptions on the distribution of incubation and infectious periods [7,9,12,14,15], or explicitly defined exposure intervals [10,13]). The majority are also implemented in a Bayesian framework, and therefore describe prior distributions on parameters and likelihood functions that evaluate the plausibility of a given parameter sets under specific transmission and evolutionary models.…”