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Mol Biol Evol. 2017 Jan;34(1):185-203. doi: 10.1093/molbev/msw217. Epub 2016 Oct 7.

Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison.

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Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom
School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.
Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom.
Nuffield Department of Medicine, Li Ka Shing Centre for Health Information and Discovery, Oxford Big Data Institute, University of Oxford, Oxford, United Kingdom.
The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom.
Department of Mathematics, Imperial College London, London, United Kingdom.
Department of Veterinary Medicine, Cambridge Veterinary School, Cambridge, United Kingdom.
Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada.
Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.
Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA.
Department of Pathology & Laboratory Medicine, Western University, Ontario, Canada.


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.


HIV transmission and prevention; molecular epidemiology of infectious diseases; viral phylogenetic methods validation

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