Imperial College London

DrErikVolz

Faculty of MedicineSchool of Public Health

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 1933e.volz Website

 
 
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Location

 

UG10Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ratmann:2017:molbev/msw217,
author = {Ratmann, O and Hodcroft, EB and Pickles, M and Cori, A and Hall, M and Lycett, S and Colijn, C and Dearlove, B and Didelot, X and Frost, S and Hossain, M and Joy, JB and Kendall, M and Kühnert, D and Leventhal, GE and Liang, R and Plazzotta, G and Poon, A and Rasmussen, DA and Stadler, T and Volz, E and Weis, C and Leigh, Brown AJ and Fraser, C},
doi = {molbev/msw217},
journal = {Molecular Biology and Evolution},
pages = {185--203},
title = {Phylogenetic tools for generalized HIV-1 epidemics: findings from the PANGEA-HIV methods comparison},
url = {http://dx.doi.org/10.1093/molbev/msw217},
volume = {34},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation > 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods’ development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention.
AU - Ratmann,O
AU - Hodcroft,EB
AU - Pickles,M
AU - Cori,A
AU - Hall,M
AU - Lycett,S
AU - Colijn,C
AU - Dearlove,B
AU - Didelot,X
AU - Frost,S
AU - Hossain,M
AU - Joy,JB
AU - Kendall,M
AU - Kühnert,D
AU - Leventhal,GE
AU - Liang,R
AU - Plazzotta,G
AU - Poon,A
AU - Rasmussen,DA
AU - Stadler,T
AU - Volz,E
AU - Weis,C
AU - Leigh,Brown AJ
AU - Fraser,C
DO - molbev/msw217
EP - 203
PY - 2017///
SN - 1537-1719
SP - 185
TI - Phylogenetic tools for generalized HIV-1 epidemics: findings from the PANGEA-HIV methods comparison
T2 - Molecular Biology and Evolution
UR - http://dx.doi.org/10.1093/molbev/msw217
UR - http://hdl.handle.net/10044/1/42696
VL - 34
ER -