Relating Phylogenetic Trees to Transmission Trees of Infectious Disease Outbreaks

Abstract

Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.E STIMATING who infected whom for an outbreak of an infectious disease can provide valuable insights. Estimated transmission trees have been used to evaluate effectiveness of intervention measures (Ferguson et al. 2001;Keeling et al. 2003;Wallinga and Teunis 2004;Heijne et al. 2009), to quantify superspreading (Lloyd-Smith et al. 2005), to estimate key parameters (Haydon et al. 2003;Heijne et al. 2012;Hens et al. 2012), and to identify mechanisms of transmission (Spada et al. 2004;Ypma et al. 2013). Transmission trees can be statistically reconstructed using epidemiological data from outbreak investigations, such as time of symptom onset, geographical location, and social ties; these data generally must be very detailed to allow for accurate reconstructions.For many pathogens, in particular RNA viruses, evolutionary processes occur on the same timescale as epidemiological processes (Holmes et al. 1995;Pybus and Rambaut 2009). This makes it possible to draw conclusions about epidemiology from genetic analysis. The field that infers epidemiological characteristics from genetic sequences by simultaneously considering host dynamics and pathogen genetics has been dubbed "phylodynamics" (Grenfell et al. 2004). In practical applications researchers have considered a specific epidemiological model dependent on the phylogenetic tree inferred from sequence data, simultaneously estimating mutational and epidemiological parame...

The role of pathogen genomics in assessing disease transmission

“…[117][118][119] These studies highlight important differences in the interpretation and characteristics of phylogenetic and transmission trees. For example, the timing of nodes (see Glossary) in the transmission tree corresponds to the point of transmission, whereas the timing of nodes in phylogenetic trees of genomic data reflect branching events (see Glossary) that may have taken place before transmission.…”

“…Hence, intra-host and inter-host evolution change the association between transmission and phylogenetic trees. 120 Although Bayesian methods have been developed that jointly estimate transmission and phylogenetic trees and capture within host pathogen dynamics, [117][118][119] in many cases there may be too much uncertainty to resolve person to person transmission networks, even with WGS data. new capacity to provide (near) real time data on the origin and transmission dynamics of pathogens could provide a major public health benefit.…”

“…121 A growing body of statistical methods is aimed at inferring transmission networks and contact structures from pathogen genomic data, with and without contextual epidemiological information. [116][117][118][119][120] These methodological developments provide new opportunities for proactive laboratory surveillance and a better understanding of the epidemiology of high burden infectious d iseases. 3 12 65 122 123 For example, examination of the genomic diversity of Salmonella typhimurium in co-located human and animal populations in the UK showed that a large proportion of transmissions occurred between humans, challenging the current belief that human to human transmission is uncommon in the developed world.…”

Reconstructing contact network parameters from viral phylogenies

“…Leitner et al 1996], and is frequently used in practice [e.g. Stadler and Bonhoeffer 2013], the topologies of a viral phylogeny and transmission tree can differ significantly (Ypma, Marijn van Ballegooijen, and Wallinga 2013) due to within-host evolution and the sampling process (Giardina 2016). The ABC-SMC algorithm is computationally intensive, taking about a day when run on 20 cores in parallel with the settings we described in the methods.…”

Whole-Genome Sequencing for Rapid and Accurate Identification of Bacterial Transmission Pathways

“…However, these approaches, like the method developed by Jombart et al (2011), associated gene sequences with the host, rather than a bacterium infecting the host. Ypma, van Ballegooijen, and Wallinga (2013) showed that this assumption could lead to erroneous reconstruction of the transmission tree, and described a method for joint estimation of the phylogenetic tree of the infecting pathogen and the transmission tree of hosts to overcome this issue.…”