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About the Authors:
Mathieu Maheu-Giroux
* E-mail: [email protected]
Affiliation: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Québec, Canada
ORCID http://orcid.org/0000-0002-8363-4388
Juan F. Vesga
Affiliation: Department of Infectious Disease Epidemiology, Imperial College London, St Mary’s Hospital, London, United Kingdom
ORCID http://orcid.org/0000-0003-1103-9587
Souleymane Diabaté
Affiliations Centre de recherche du CHU de Québec - Université Laval, Québec, Canada, Département d’infectiologie et santé publique, Université Alassane Ouattara, Bouaké, Côte d’Ivoire
Michel Alary
Affiliations Centre de recherche du CHU de Québec - Université Laval, Québec, Canada, Département de médecine sociale et préventive, Université Laval, Québec, Canada, Institut national de santé publique du Québec, Québec, Canada
Stefan Baral
Affiliation: Key Populations Program, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
ORCID http://orcid.org/0000-0002-5482-2419
Daouda Diouf
Affiliation: Enda Santé, Dakar, Sénégal
Kouamé Abo
Affiliation: Programme National de Lutte contre le SIDA, Ministère de la Santé et de l’Hygiène Publique, Abidjan, Côte d’Ivoire
Marie-Claude Boily
Affiliation: Department of Infectious Disease Epidemiology, Imperial College London, St Mary’s Hospital, London, United KingdomAbstract
Background
National responses will need to be markedly accelerated to achieve the ambitious target of the Joint United Nations Programme on HIV/AIDS (UNAIDS). This target aims for 90% of HIV-positive individuals to be aware of their status, for 90% of those aware to receive antiretroviral therapy (ART), and for 90% of those on treatment to have a suppressed viral load by 2020, with each individual target reaching 95% by 2030. We aimed to estimate the impact of various treatment-as-prevention scenarios in Côte d’Ivoire, one of the countries with the highest HIV incidence in West Africa, with unmet HIV prevention and treatment needs, and where key populations are important to the broader HIV epidemic.
Methods and findings
An age-stratified dynamic model was developed and calibrated to epidemiological and programmatic data using a Bayesian framework. The model represents sexual and vertical HIV transmission in the general population, female sex workers (FSW), and men who have sex with men (MSM). We estimated the impact of scaling up interventions to reach the UNAIDS targets, as well as the impact of 8 other scenarios, on HIV transmission in adults and children, compared to our baseline scenario that maintains 2015 rates of testing, ART initiation, ART discontinuation, treatment failure,...