It is important not only to collect epidemiologic data on
HIV but to also fully utilize such information to understand the epidemic
over time and to help inform and monitor the impact of policies
and interventions. We describe and apply a novel method to estimate
the size and characteristics of HIV-positive populations. The method
was applied to data on men who have sex with men living in the UK
and to a pseudo dataset to assess performance for different data availability.
The individual-based simulation model was calibrated using an approximate Bayesian computation-based approach. In 2013,
48,310 (90% plausibility range: 39,900–45,560) men who have sex
with men were estimated to be living with HIV in the UK, of whom
10,400 (6,160–17,350) were undiagnosed. There were an estimated
3,210 (1,730–5,350) infections per year on average between 2010
and 2013. Sixty-two percent of the total HIV-positive population are
thought to have viral load <500 copies/ml. In the pseudo-epidemic
example, HIV estimates have narrower plausibility ranges and are
closer to the true number, the greater the data availability to calibrate
the model. We demonstrate that our method can be applied to settings
with less data, however plausibility ranges for estimates will be wider
to reflect greater uncertainty of the data used to fit the model.