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Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison
Author(s) -
Oliver Ratmann,
Emma B. Hodcroft,
Michael Pickles,
Anne Cori,
Matthew Hall,
Samantha Lycett,
Caroline Colijn,
Bethany L. Dearlove,
Xavier Didelot,
Simon D. W. Frost,
Azim Hossain,
Jeffrey B. Joy,
Michelle Kendall,
Denise Kühnert,
Gabriel E. Leventhal,
Richard Liang,
Giacomo Plazzotta,
Art F. Y. Poon,
David A. Rasmussen,
Tanja Stadler,
Erik Volz,
Caroline Weis,
Andrew Brown,
Christophe Fraser
Publication year - 2016
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/msw217
Subject(s) - phylogenetic tree , biology , coalescent theory , phylogenetics , human immunodeficiency virus (hiv) , transmission (telecommunications) , viral phylodynamics , psychological intervention , demography , evolutionary biology , incidence (geometry) , virology , genetics , medicine , computer science , mathematics , telecommunications , geometry , psychiatry , sociology , gene
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.

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