Imperial College London
Alternate

DrOliverRatmann

Faculty of Natural Sciences, Department of Mathematics

Reader in Statistics and Machine Learning for Public Good
 
 
 
//

Contact

 

oliver.ratmann05 Website

 
 
//

Location

 

525Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Metzig:2019:10.1371/journal.pcbi.1006761,
author = {Metzig, C and Ratmann, O and Bezemer, D and Colijn, C},
doi = {10.1371/journal.pcbi.1006761},
journal = {PLoS Computational Biology},
title = {Phylogenies from dynamic networks},
url = {http://dx.doi.org/10.1371/journal.pcbi.1006761},
volume = {15},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 Erds-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 est
AU  - Metzig,C
AU  - Ratmann,O
AU  - Bezemer,D
AU  - Colijn,C
DO  - 10.1371/journal.pcbi.1006761
PY  - 2019///
SN  - 1553-734X
TI  - Phylogenies from dynamic networks
T2  - PLoS Computational Biology
UR  - http://dx.doi.org/10.1371/journal.pcbi.1006761
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000460276500025&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/68783
VL  - 15
ER  -
Download