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Background <p>Estimating 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.</p> Results <p>We 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&#xa0;years, with a mean absolute error of less than 12&#xa0;months overall and less than 5&#xa0;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. </p> Conclusions <p>We demonstrate how HIV-phyloTSI can be used for incidence estimates on a population level.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月14日

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  • 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,
  • Barry Kosloff,
  • Mohammed Limbada,
  • Andrew Mujugira,
  • Nelly R. Mugo,
  • Astrid Gall,
  • François Blanquart,
  • Margreet Bakker,
  • Daniela Bezemer,
  • Swee Hoe Ong,
  • Jan Albert,
  • Norbert Bannert,
  • Jacques Fellay,
  • Barbara Gunsenheimer-Bartmeyer,
  • Huldrych F. Günthard,
  • Pia Kivelä,
  • Roger D. Kouyos,
  • Laurence Meyer,
  • Kholoud Porter,
  • Ard van Sighem,
  • Mark van der Valk,
  • Ben Berkhout,
  • Paul Kellam,
  • Marion Cornelissen,
  • Peter Reiss,
  • Helen Ayles,
  • David N. Burns,
  • Sarah Fidler,
  • Mary Kate Grabowski,
  • Richard Hayes,
  • Joshua T. Herbeck,
  • Joseph Kagaayi,
  • Pontiano Kaleebu,
  • Jairam R. Lingappa,
  • Deogratius Ssemwanga,
  • Susan H. Eshleman,
  • Myron S. Cohen,
  • Oliver Ratmann,
  • Oliver Laeyendecker,
  • Christophe Fraser

摘要

Background

Estimating 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.

Results

We 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.

Conclusions

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