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

## TL;DR

This paper introduces HIV-phyloTSI, a method to estimate the time since HIV infection using deep sequencing data, enabling more accurate population-level tracking of the HIV epidemic.

## Contribution

HIV-phyloTSI provides a continuous, subtype-independent time-since-infection estimation using within-host diversity and divergence from deep sequencing data.

## Key findings

- HIV-phyloTSI estimates TSI up to 9 years with a mean absolute error of less than 12 months.
- The method performs equally well across all major HIV subtypes in African and European cohorts.
- It achieves less than 5 months error for infections within the first year.

## Abstract

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.

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.

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

The online version contains supplementary material available at 10.1186/s12859-025-06189-y.