# Quantifying the recency of HIV infection using multiple longitudinal biomarkers

## TL;DR

This paper introduces a Bayesian mixed effects modeling approach to quantify the recency of HIV infection using multiple biomarkers, improving estimation accuracy by joint modeling of correlated biomarkers.

## Contribution

It develops a novel Bayesian framework for characterizing biomarker growth patterns and estimating infection recency at an individual level, incorporating multiple biomarkers for better accuracy.

## Key findings

- Joint biomarker models outperform univariate models in recency estimation.
- Biomarker growth rate significantly influences estimation accuracy.
- Simulation results demonstrate improved predictive performance with combined biomarkers.

## Abstract

Knowledge of the time at which an HIV-infected individual seroconverts, when the immune system starts responding to HIV infection, plays a vital role in the design and implementation of interventions to reduce the impact of the HIV epidemic. A number of biomarkers have been developed to distinguish between recent and long-term HIV infection, based on the antibody response to HIV. To quantify the recency of infection at an individual level, we propose characterising the growth of such biomarkers from observations from a panel of individuals with known seroconversion time, using Bayesian mixed effect models. We combine this knowledge of the growth patterns with observations from a newly diagnosed individual, to estimate the probability seroconversion occurred in the X months prior to diagnosis. We explore, through a simulation study, the characteristics of different biomarkers that affect our ability to estimate recency, such as the growth rate. In particular, we find that predictive ability is improved by using joint models of two biomarkers, accounting for their correlation, rather than univariate models of single biomarkers.