Phylogenies from dynamic networks

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Title: Phylogenies from dynamic networks
Authors: Metzig, C
Ratmann, O
Bezemer, D
Colijn, C
Item Type: Journal Article
Abstract: The relationship between the underlying contact network over which a pathogen spreads and the pathogen phylogenetic trees that are obtained presents an opportunity to use sequence data to learn about contact networks that are difficult to study empirically. However, this relationship is not explicitly known and is usually studied in simulations, often with the simplifying assumption that the contact network is static in time, though human contact networks are dynamic. We simulate pathogen phylogenetic trees on dynamic Erdős-Renyi random networks and on two dynamic networks with skewed degree distribution, of which one is additionally clustered. We use tree shape features to explore how adding dynamics changes the relationships between the overall network structure and phylogenies. Our tree features include the number of small substructures (cherries, pitchforks) in the trees, measures of tree imbalance (Sackin index, Colless index), features derived from network science (diameter, closeness), as well as features using the internal branch lengths from the tip to the root. Using principal component analysis we find that the network dynamics influence the shapes of phylogenies, as does the network type. We also compare dynamic and time-integrated static networks. We find, in particular, that static network models like the widely used Barabasi-Albert model can be poor approximations for dynamic networks. We explore the effects of mis-specifying the network on the performance of classifiers trained identify the transmission rate (using supervised learning methods). We find that both mis-specification of the underlying network and its parameters (mean degree, turnover rate) have a strong adverse effect on the ability to estimate the transmission parameter. We illustrate these results by classifying HIV trees with a classifier that we trained on simulated trees from different networks, infection rates and turnover rates. Our results point to the importance of correctly estimating and modelling contact networks with dynamics when using phylodynamic tools to estimate epidemiological parameters.
Issue Date: 26-Feb-2019
Date of Acceptance: 7-Jan-2019
ISSN: 1553-734X
Publisher: Public Library of Science (PLoS)
Journal / Book Title: PLoS Computational Biology
Volume: 15
Issue: 2
Copyright Statement: © 2019 Metzig et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/K026003/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
06 Biological Sciences
08 Information and Computing Sciences
01 Mathematical Sciences
Publication Status: Published
Open Access location:
Article Number: e1006761
Online Publication Date: 2019-02-26
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics

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