  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.
