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PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial.
- Source :
-
PLoS computational biology [PLoS Comput Biol] 2021 Sep 02; Vol. 17 (9), pp. e1009301. Date of Electronic Publication: 2021 Sep 02 (Print Publication: 2021). - Publication Year :
- 2021
-
Abstract
- Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention.<br />Competing Interests: The authors have declared that no competing interests exist.
- Subjects :
- Adolescent
Adult
Aged
Algorithms
Antiretroviral Therapy, Highly Active
Disease Progression
Female
HIV Infections drug therapy
HIV Infections transmission
Humans
Incidence
Male
Middle Aged
Prevalence
Reproducibility of Results
Young Adult
Zambia epidemiology
Computer Simulation
HIV Infections epidemiology
Models, Statistical
Stochastic Processes
Subjects
Details
- Language :
- English
- ISSN :
- 1553-7358
- Volume :
- 17
- Issue :
- 9
- Database :
- MEDLINE
- Journal :
- PLoS computational biology
- Publication Type :
- Academic Journal
- Accession number :
- 34473700
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1009301