Bayesian inference of infectious disease transmission from whole-genome sequence data

Abstract

Genomics is increasingly being used to investigate disease outbreaks, but an important question remains unanswered--how well do genomic data capture known transmission events, particularly for pathogens with long carriage periods or large within-host population sizes? Here we present a novel Bayesian approach to reconstruct densely sampled outbreaks from genomic data while considering within-host diversity. We infer a time-labeled phylogeny using Bayesian evolutionary analysis by sampling trees (BEAST), and then infer a transmission network via a Monte Carlo Markov chain. We find that under a realistic model of within-host evolution, reconstructions of simulated outbreaks contain substantial uncertainty even when genomic data reflect a high substitution rate. Reconstruction of a real-world tuberculosis outbreak displayed similar uncertainty, although the correct source case and several clusters of epidemiologically linked cases were identified. We conclude that genomics cannot wholly replace traditional epidemiology but that Bayesian reconstructions derived from sequence data may form a useful starting point for a genomic epidemiology investigation.

Fig. 1.

The colored genealogical tree (left). Each host isolate corresponds to a unique color. A lineage is colored according to the host it was in at the corresponding time. When a lineage changes from color c_i to c_j (forward in time), this represents i infecting j. Each color may not persist in the tree after the time of the corresponding tip, because this is the recovery time of the host. The subtree restricted to a single color (right) is the part of the tree inside the corresponding host; lineages are taken from this tree at the recovery time and the times when the host infected others.

Fig. 2.

(A) Transmission tree simulated with N = 100, , and . The numbers in square brackets represent the time of infection and removal of each individual, respectively. (B) Genealogical tree simulated conditional on the transmission tree in (A) and with parameter . Transmission events are indicated by red stars and a change in branch color. (C) Same as in (B) but with parameter .

Fig. 3.

(A) Network representation of the posterior distribution of transmission trees for the simulated data set shown in figure 2C. Nodes represent hosts, and the numbers within square brackets to mean inferred infection time and the known removal time. Edges represent a posterior probability of transmission of at least 10%, with a darker edge indicating a higher probability. (B) Point estimate of the transmission tree obtained by taking the optimal branching tree in the network shown in part (A).

Fig. 4.

Application to a real-world tuberculosis outbreak. (A) Phylogenetic tree inferred by BEAST. (B) SNVs differentiating the isolates. (C) Transmission network inferred without epidemiological modification. (D) Transmission network inferred with epidemiological modification. In (C) and (D), edges are shown with width and shading proportional to their posterior probability, except edges with low probability that are omitted. In (A), the phylogenetic tree is colored according to the consensus transmission tree from (D) and using the same unique colors for each host as in (C) and (D).