A method to estimate the size and characteristics of HIV-positive populations using an individual-based stochastic simulation model
Fumiyo Nakagawa
(1)
,
Ard van Sighem
(2)
,
Rodolphe Thiébaut
(3, 4)
,
Colette Smith
(1)
,
Oliver Ratmann
(5)
,
Valentina Cambiano
(1)
,
Jan Albert
(6)
,
Andrew Amato-Gauci
(7)
,
Daniela Bezemer
(2)
,
Colin Campbell
(8)
,
Daniel Commenges
(4)
,
Martin Donoghoe
(9)
,
Deborah Ford
(10)
,
Roger Kouyos
(11)
,
Rebecca Lodwick
(12)
,
Jens Lundgren
(13)
,
Nikos Pantazis
(14)
,
Anastasia Pharris
(7)
,
Chantal Quinten
(7)
,
Claire Thorne
(15)
,
Giota Touloumi
(14)
,
Valerie Delpech
(16)
,
Andrew Phillips
(1)
1
Research Department of Infection and Population Health [London]
2 Stichting HIV Monitoring [Amsterdam]
3 UB - Université de Bordeaux
4 SISTM - Statistics In System biology and Translational Medicine
5 DIDE - Department of Infectious Disease Epidemiology [London]
6 Service de rhumatologie [Rennes] = Rheumatology [Rennes]
7 ECDC - European Centre for Disease Prevention and Control [Stockholm, Sweden]
8 CEEISCAT - Centre d'Estudis Epidemiològics sobre les Infeccions de Transmissió Sexual i Sida de Catalunya
9 WHO Regional Office for Europe [Copenhagen]
10 ICTM - Institute of Clinical Trials and Methodology [London]
11 Department of Infectious Diseases and Hospital Epidemiology [Zurich]
12 PCPH - Department of Primary Care and Population Health [London]
13 UCPH - University of Copenhagen = Københavns Universitet
14 Department of Hygiene, Epidemiology and Medical Statistics [Athens]
15 UGA UFRP - Université Grenoble Alpes - UFR Pharmacie
16 Keele University [Keele]
2 Stichting HIV Monitoring [Amsterdam]
3 UB - Université de Bordeaux
4 SISTM - Statistics In System biology and Translational Medicine
5 DIDE - Department of Infectious Disease Epidemiology [London]
6 Service de rhumatologie [Rennes] = Rheumatology [Rennes]
7 ECDC - European Centre for Disease Prevention and Control [Stockholm, Sweden]
8 CEEISCAT - Centre d'Estudis Epidemiològics sobre les Infeccions de Transmissió Sexual i Sida de Catalunya
9 WHO Regional Office for Europe [Copenhagen]
10 ICTM - Institute of Clinical Trials and Methodology [London]
11 Department of Infectious Diseases and Hospital Epidemiology [Zurich]
12 PCPH - Department of Primary Care and Population Health [London]
13 UCPH - University of Copenhagen = Københavns Universitet
14 Department of Hygiene, Epidemiology and Medical Statistics [Athens]
15 UGA UFRP - Université Grenoble Alpes - UFR Pharmacie
16 Keele University [Keele]
Rodolphe Thiébaut
- Function : Author
- PersonId : 175125
- IdHAL : rodolphe-thiebaut
- ORCID : 0000-0002-5235-3962
- IdRef : 071580735
Oliver Ratmann
- Function : Author
- PersonId : 777331
- ORCID : 0000-0001-8667-4118
Anastasia Pharris
- Function : Author
- PersonId : 777332
- ORCID : 0000-0002-7196-657X
Claire Thorne
- Function : Author
- PersonId : 777333
- ORCID : 0000-0003-0389-1956
Abstract
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.