Back to overview

A Method to Estimate the Size and Characteristics of HIV-positive Populations Using an Individual-based Stochastic Simulation Model.

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Nakagawa Fumiyo, van Sighem Ard, Thiebaut Rodolphe, Smith Colette, Ratmann Oliver, Cambiano Valentina, Albert Jan, Amato-Gauci Andrew, Bezemer Daniela, Campbell Colin, Commenges Daniel, Donoghoe Martin, Ford Deborah, Kouyos Roger, Lodwick Rebecca, Lundgren Jens, Pantazis Nikos, Pharris Anastasia, Quinten Chantal, Thorne Claire, Touloumi Giota, Delpech Valerie, Phillips Andrew, SSOPHIE project working group in EuroCoord,
Project Swiss HIV Cohort Study (SHCS)
Show all

Original article (peer-reviewed)

Journal Epidemiology (Cambridge, Mass.)
Volume (Issue) 27(2)
Page(s) 247 - 56
Title of proceedings Epidemiology (Cambridge, Mass.)
DOI 10.1097/ede.0000000000000423

Open Access

Type of Open Access Repository (Green Open Access)


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.