2025/08/14 by Tanya Golubchik, Lucie Abeler‐Dörner, Matthew Hall +46 · 1 voice
Immunology and Microbiology · Medicine · #HIV Research and Treatment #HIV-related health complications and treatments #HIV/AIDS Research and Interventions
paper · pdf · doi:10.1186/s12859-025-06189-y
openalex publication_date 2025/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.