Abstract
Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not directly informative about transmission events between individuals. TransPhylo is a software tool implemented as an R package designed to bridge the gap between pathogen phylogenies and transmission trees. TransPhylo is based on a combined model of transmission between hosts and pathogen evolution within each host. It can simulate both phylogenies and transmission trees jointly under this combined model. TransPhylo can also reconstruct a transmission tree based on a dated phylogeny, by exploring the space of transmission trees compatible with the phylogeny. A transmission tree can be represented as a coloring of a phylogeny where each color represents a different host of the pathogen, and TransPhylo provides convenient ways to plot these colorings and explore the results. This article presents the basic protocols that can be used to make the most of TransPhylo. © 2021 The Authors. Basic Protocol 1: First steps with TransPhylo Basic Protocol 2: Simulation of outbreak data Basic Protocol 3: Inference of transmission Basic Protocol 4: Exploring the results of inference
Highlights
Pathogen genomics has great potential to help us understand how infectious diseases spread between hosts
We explore the results of the inference performed in the previous protocol, taking our starting point as r=inferTTree(...) as above and assuming that the convergence and mixing are satisfactory
The advantage of this approach is that it returns a single transmission tree, which can be shown either as a coloring of the dated phylogeny (Fig. 7A) or as a separate transmission tree (Fig. 7B), using exactly the same visualization techniques as we described earlier for simulated transmission trees (Fig. 3A)
Summary
Pathogen genomics has great potential to help us understand how infectious diseases spread between hosts. This representation only shows the transmission tree, but the simulation contains the phylogenetic tree of relationships between sampled genomes With a basic reproduction number equal to 3, the outbreak grows exponentially, which is not clear in the example above, but becomes obvious if we consider a longer time frame of, for example, 7 years with sampling of pi=0.1 of cases: set.seed(0) s=simulateOutbreak(off.r=3,dateStartOutbreak=2020, pi=0.1,dateT=2027,w.shape=10,w.scale=0.1) plot(extractTTree(s),showLabels=F). The simulation used a generation time distribution with parameters w.shape=5 and w.scale=0.3, a basic reproduction number of Figure 5 Dated phylogeny used as input for inference. A run ten times longer than the one above with parameters mcmcIterations=1e6,thinning=10 takes approximately 20 min and gives all ESS values above 500
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