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用于广义HIV-1流行的系统发育工具:PANGEA-HIV方法比较的结果

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

作者信息

Ratmann Oliver, Hodcroft Emma B, Pickles Michael, Cori Anne, Hall Matthew, Lycett Samantha, Colijn Caroline, Dearlove Bethany, Didelot Xavier, Frost Simon, Hossain A S Md Mukarram, Joy Jeffrey B, Kendall Michelle, Kühnert Denise, Leventhal Gabriel E, Liang Richard, Plazzotta Giacomo, Poon Art F Y, Rasmussen David A, Stadler Tanja, Volz Erik, Weis Caroline, Leigh Brown Andrew J, Fraser Christophe

机构信息

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.

出版信息

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

Abstract

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.

摘要

病毒系统发育方法有助于理解艾滋病毒在人群中的传播方式,从而有助于指导预防干预措施的设计。到目前为止,大多数分析都应用于富裕国家中采样良好的集中性艾滋病毒-1流行情况。为了将系统发育工具应用于艾滋病毒-1影响最大的地区,非洲广义艾滋病毒流行的系统发育与网络(PANGEA-HIV)联盟从撒哈拉以南非洲各地生成了全基因组病毒序列。分析这些数据带来了新的挑战,因为这些流行主要由异性传播驱动,且采样的病例比例较小。在这里,我们表明病毒系统发育工具可以进行调整并用于估计对撒哈拉以南非洲艾滋病毒-1预防至关重要的流行病学数量。我们在模拟数据上进行了一次全社区范围的方法比较练习,参与者对他们所推断的真实动态不知情。两次不同的模拟捕捉了大规模社区层面干预降低感染水平之前和之后的广义艾滋病毒-1流行情况。五个研究小组参与了此次练习。结构化合并建模方法最为成功:艾滋病毒-1发病率、发病率降低情况以及感染后前三个月内个体传播比例的系统发育估计值与真实值相关(皮尔逊相关系数>90%),偏差较小。然而,在某些模拟中,真实值明显超出了报告的置信区间或可信区间。这次不知情比较揭示了在具有挑战性的环境中使用艾滋病毒系统发育方法的当前局限性和优势,为未来方法的发展提供了基准,并支持使用最新一代的系统发育工具来推进艾滋病毒监测和预防工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4f5/5854118/0838173482ba/msw217f1p.jpg

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