TY - JOUR

T1 - A Method to Estimate the Size and Characteristics of HIV-positive Populations Using an Individual-based Stochastic Simulation Model

AU - Nakagawa, Fumiyo

AU - van Sighem, Ard

AU - Thiebaut, Rodolphe

AU - Smith, Colette

AU - Ratmann, Oliver

AU - Cambiano, Valentina

AU - Albert, Jan

AU - Amato-Gauci, Andrew

AU - Bezemer, Daniela

AU - Campbell, Colin

AU - Commenges, Daniel

AU - Donoghoe, Martin

AU - Ford, Deborah

AU - Kouyos, Roger

AU - Lodwick, Rebecca

AU - Lundgren, Jens

AU - Pantazis, Nikos

AU - Pharris, Anastasia

AU - Quinten, Chantal

AU - Thorne, Claire

AU - Touloumi, Giota

AU - Delpech, Valerie

AU - Phillips, Andrew

AU - SSOPHIE project working group in EuroCoord

N1 - Erratum In the March 2016 issue of EPIDEMIOLOGY in the article by Nakagawa et al., “A Method to Estimate the Size and Characteristics of HIV-positive Populations Using an Individual-based Stochastic Simulation Model,” the Open Access footnote should have read: “This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.” Epidemiology. 27(3):e24, May 2016.

PY - 2016/3

Y1 - 2016/3

N2 - 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.

AB - 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.

U2 - 10.1097/EDE.0000000000000423

DO - 10.1097/EDE.0000000000000423

M3 - Journal article

C2 - 26605814

VL - 27

SP - 247

EP - 256

JO - Epidemiology

JF - Epidemiology

SN - 1044-3983

IS - 2

ER -