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

Journal article

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.

Authors: Golubchik, T; Abeler-Dörner, L; Hall, M; Wymant, C; Bonsall, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: BMC Bioinformatics
• Volume: 26
• Issue: 1
• Article number: 212
• Publication date: 2025-08-14
• Acceptance date: 2025-06-12
• DOI: 10.1186/s12859-025-06189-y
• EISSN: 1471-2105
• ISSN: 1471-2105
• Language: English
• Keywords: Recency of infection; HIV; Next-generation sequencing; Time since infection; Random forest