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. 2021 Feb;1(2):e60.
doi: 10.1002/cpz1.60.

Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo

Xavier Didelot  1 , Michelle Kendall  1 , Yuanwei Xu  2 , Peter J White  3   4   5   6 , Noel McCarthy  7
Affiliations

Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo

Xavier Didelot et al. Curr Protoc. 2021 Feb.

Erratum in

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.

Keywords: genomic epidemiology; infectious disease outbreak; phylogenetics; transmission analysis.

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Figures

Figure 1
Figure 1
Illustration of how a given dated phylogeny (A) can be compatible with multiple transmission trees (B‐D). In parts (B‐D), the red, green and blue boxes correspond to evolution of the pathogen in hosts A, B, and C, respectively.
Figure 2
Figure 2
Dated phylogeny (A), transmission tree (B), and colored phylogeny (C) for a small simulated outbreak with ten sampled cases and five unsampled cases.
Figure 3
Figure 3
Detailed transmission tree (A) and colored phylogeny (B) for a small simulated outbreak.
Figure 4
Figure 4
Transmission tree for a large simulated outbreak.
Figure 5
Figure 5
Dated phylogeny used as input for inference.
Figure 6
Figure 6
Traces of the Markov Chain Monte‐Carlo.
Figure 7
Figure 7
Medoid inferred transmission events shown as a colored phylogeny (A) and as a detailed transmission tree (B).
Figure 8
Figure 8
Matrix of transmission probabilities between cases (A) and matrix of distance between cases in the transmission tree (B).
Figure 9
Figure 9
Infection date for a selected individual (A) and number of secondary cases caused by that same individual (B).
Figure 10
Figure 10
Number of cases in the transmission tree (A), realized generation time distribution (B), and realized sampling time distribution (C).
See this image and copyright information in PMC

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Internet Resources
    1. https://CRAN.R‐project.org/package=TransPhylo
    1. https://github.com/xavierdidelot/TransPhylo
    1. http://xavierdidelot.github.io/TransPhylo/