Imperial College London

DrCarolineColijn

Faculty of Natural SciencesDepartment of Mathematics

Visiting Professor
 
 
 
//

Contact

 

+44 (0)20 7594 2647c.colijn Website

 
 
//

Location

 

626Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Didelot:2017:molbev/msw275,
author = {Didelot, X and Fraser, C and Gardy, J and Colijn, C},
doi = {molbev/msw275},
journal = {Molecular Biology and Evolution},
pages = {997--1007},
title = {Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks},
url = {http://dx.doi.org/10.1093/molbev/msw275},
volume = {34},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Genomic data is increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, thephylogenetic trees typically used to represent this variation are not directly informative about who infected whom { a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by colouring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. Wealso demonstrate how the branch colouring approach can incorporate a variable number of unique colours to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte-Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo
AU - Didelot,X
AU - Fraser,C
AU - Gardy,J
AU - Colijn,C
DO - molbev/msw275
EP - 1007
PY - 2017///
SN - 1537-1719
SP - 997
TI - Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks
T2 - Molecular Biology and Evolution
UR - http://dx.doi.org/10.1093/molbev/msw275
UR - http://hdl.handle.net/10044/1/42711
VL - 34
ER -