Evidence map›Paper›PMID 40813968›Full record

ArticleBMC bioinformatics2025

HIV-phyloTSI: subtype-independent estimation of time since HIV-1 infection for cross-sectional measures of population incidence using deep sequence data.

Tanya Golubchik, Lucie Abeler-Dörner, Matthew Hall, Chris Wymant, David Bonsall, George Macintyre-Cockett, Laura Thomson, Jared M Baeten, Connie L Celum, Ronald M Galiwango and 40 more

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Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Trial
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

50 authors.

Tanya GolubchikPandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK. tanya.golubchik@sydney.edu.au.
Lucie Abeler-DörnerPandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Matthew HallPandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Chris WymantPandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
David BonsallPandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
George Macintyre-CockettPandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Laura ThomsonPandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Jared M BaetenDepartment of Global Health, University of Washington, Seattle, WA, USA.
Connie L CelumDepartment of Global Health, University of Washington, Seattle, WA, USA.
Ronald M GaliwangoRakai Health Sciences Program, Kalisizo, Uganda.
Barry KosloffLondon School of Hygiene and Tropical Medicine, London, UK.
Mohammed LimbadaLondon School of Hygiene and Tropical Medicine, London, UK.
Andrew MujugiraInfectious Diseases Institute, Makerere University, Kampala, Uganda.
Nelly R MugoDepartment of Global Health, University of Washington, Seattle, WA, USA.
Astrid GallEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
François BlanquartCentre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL Research University, Paris, France.
Margreet BakkerMedical Microbiology and Infection Prevention, Amsterdam UMC, Location AMC, Amsterdam, Netherlands.
Daniela BezemerStichting HIV Monitoring, Amsterdam UMC, Location AMC, Amsterdam, Netherlands.
Swee Hoe OngWellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
Jan AlbertDepartment of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
Norbert BannertDivision for HIV and Other Retroviruses, Robert Koch Institute, Berlin, Germany.
Jacques FellaySchool of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Barbara Gunsenheimer-BartmeyerDepartment of Infectious Disease Epidemiology, Robert Koch-Institute, Berlin, Germany.
Huldrych F GünthardDivision of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.
Pia KiveläDepartment of Infectious Diseases, Helsinki University Hospital, Helsinki, Finland.
Roger D KouyosDivision of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.
Laurence MeyerINSERM CESP U1018, APHP, Service de Santé Publique, Hôpital de Bicêtre, Université Paris Saclay, Le Kremlin-Bicêtre, France.
Kholoud PorterInstitute for Global Health, University College London, London, UK.
Ard van SighemStichting HIV Monitoring, Amsterdam UMC, Location AMC, Amsterdam, Netherlands.
Mark van der ValkAmsterdam UMC Location Meibergdreef, Amsterdam, The Netherlands.
Ben BerkhoutMedical Microbiology and Infection Prevention, Amsterdam UMC, Location AMC, Amsterdam, Netherlands.
Paul KellamDepartment of Infectious Diseases, Department of Medicine, Imperial College London, London, UK.
Marion CornelissenMedical Microbiology and Infection Prevention, Amsterdam UMC, Location AMC, Amsterdam, Netherlands.
Peter ReissStichting HIV Monitoring, Amsterdam UMC, Location AMC, Amsterdam, Netherlands.
Helen AylesLondon School of Hygiene and Tropical Medicine, London, UK.
David N BurnsDivision of AIDS, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA.
Sarah FidlerDepartment of Infectious Disease Epidemiology, Imperial College, London, UK.
Mary Kate GrabowskiDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Richard HayesLondon School of Hygiene and Tropical Medicine, London, UK.
Joshua T HerbeckInstitute for Disease Modeling, Bellevue, WA, USA.
Joseph KagaayiRakai Health Sciences Program, Kalisizo, Uganda.
Pontiano KaleebuMedical Research Council (MRC), Uganda Virus Research Institute (UVRI), Entebbe, Uganda.
Jairam R LingappaDepartment of Global Health, University of Washington, Seattle, WA, USA.
Deogratius SsemwangaMedical Research Council (MRC), Uganda Virus Research Institute (UVRI), Entebbe, Uganda.
Susan H EshlemanDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Myron S CohenDepartment of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Oliver RatmannDepartment of Mathematics and Imperial-X, Imperial College, London, UK.
Oliver LaeyendeckerDivision of Intramural Research, National Institute of Allergy and Infectious Disease, National Institutes of Medicine, Baltimore, MD, USA.
Christophe FraserPandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
HPTN 071 (PopART) Phylogenetics protocol team, the BEEHIVE consortium and the PANGEA consortium

Funding

SDMC: HPTN 084 Pregnancy SupplementUM1AI068617 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Elizabeth Renata Brown, Deborah J Donnell · 2011 to 2026
$204.9M
LC: HIV Prevention Trials Network - Laboratory Support for the SARS-CoV-2 Seroprevalence Study (CoVPN 5002)UM1AI068613 · NIAID · JOHNS HOPKINS UNIVERSITY · PI SUSAN H ESHLEMAN, Mark A Marzinke · 2011 to 2026
$100.2M
Bill and Melinda Gates Foundation OPP1175094European Research Council PBDR-339251National Health and Medical Research Council GNT2025445National Institute of Allergy and Infectious Diseases UM1-AI068619, UM1-AI068617, UM1-AI068613NIAID NIH HHS UM1 AI068613NIAID NIH HHS UM1 AI068617
6 · The paper itself

Abstract

backgroundEstimating the time since HIV infection (TSI) at population level is essential for tracking changes in the global HIV epidemic. Most methods for determining TSI give a binary classification of infections as recent or non-recent within a window of several months, and cannot assess the cumulative impact of an intervention.

resultsWe developed a Random Forest Regression model, HIV-phyloTSI, which combines measures of within-host diversity and divergence to generate continuous TSI estimates directly from viral deep-sequencing data, with no need for additional variables. HIV-phyloTSI provides a continuous measure of TSI up to 9 years, with a mean absolute error of less than 12 months overall and less than 5 months for infections with a TSI of up to a year. It performs equally well for all major HIV subtypes based on data from African and European cohorts.

conclusionsWe demonstrate how HIV-phyloTSI can be used for incidence estimates on a population level.

Indexed as

High-Throughput Nucleotide SequencingHIV-1HIV InfectionsCross-Sectional StudiesHumansIncidencePhylogenyHIVNext-generation sequencingRandom forestRecency of infectionTime since infection

Identifiers

PMID40813968
PMCPMC12351810

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.