HIV-phyloTSI: subtype-independent estimation of time since HIV-1 infection for cross-sectional measures of population incidence using deep sequence data
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
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.
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.
Date Issued
2025-08-14
Date Acceptance
2025-06-12
Citation
BMC Bioinformatics, 2025, 26
ISSN
1471-2105
Publisher
BMC
Journal / Book Title
BMC Bioinformatics
Volume
26
Copyright Statement
© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40813968
PII: 10.1186/s12859-025-06189-y
Subjects
HIV
Next-generation sequencing
Random forest
Recency of infection
Time since infection
HIV Infections
Humans
HIV-1
Incidence
High-Throughput Nucleotide Sequencing
Cross-Sectional Studies
Phylogeny
Publication Status
Published
Coverage Spatial
England
Article Number
212
Date Publish Online
2025-08-14