Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases

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Title: Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases
Authors: Le Vu, SOK
Ratmann, O
Delpech, V
Brown, AE
Gill, ON
Tostevin, A
Fraser, C
Volz, EM
Item Type: Journal Article
Abstract: Phylogenetic clustering of HIV sequences from a random sample of patients can reveal epidemiological transmission patterns, but interpretation is hampered by limited theoretical support and statistical properties of clustering analysis remain poorly understood. Alternatively, source attribution methods allow fitting of HIV transmission models and thereby quantify aspects of disease transmission. A simulation study was conducted to assess error rates of clustering methods for detecting transmission risk factors. We modeled HIV epidemics among men having sex with men and generated phylogenies comparable to those that can be obtained from HIV surveillance data in the UK. Clustering and source attribution approaches were applied to evaluate their ability to identify patient attributes as transmission risk factors. We find that commonly used methods show a misleading association between cluster size or odds of clustering and covariates that are correlated with time since infection, regardless of their influence on transmission. Clustering methods usually have higher error rates and lower sensitivity than source attribution method for identifying transmission risk factors. But neither methods provide robust estimates of transmission risk ratios. Source attribution method can alleviate drawbacks from phylogenetic clustering but formal population genetic modeling may be required to estimate quantitative transmission risk factors.
Issue Date: 1-Jun-2018
Date of Acceptance: 17-Oct-2017
ISSN: 1755-4365
Publisher: Elsevier
Start Page: 1
End Page: 10
Journal / Book Title: Epidemics
Volume: 23
Copyright Statement: © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (
Sponsor/Funder: Medical Research Council (MRC)
National Institute for Health Research
Bill & Melinda Gates Foundation
National Institutes of Health
Funder's Grant Number: MR/K010174/1B
GCAEN 511473
Keywords: Science & Technology
Life Sciences & Biomedicine
Infectious Diseases
Phylogenetic analysis
Cluster analysis
HIV epidemiology
Computer simulation
1103 Clinical Sciences
1117 Public Health And Health Services
Publication Status: Published
Online Publication Date: 2017-10-20
Appears in Collections:Mathematics
Faculty of Medicine
Faculty of Natural Sciences
Epidemiology, Public Health and Primary Care

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