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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, Vol 17, Iss 9, p e1009301 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 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 />Author summary In this paper we present PopART-IBM, an individual-based model used to simulate HIV transmission in communities in high prevalence settings. We show that PopART-IBM can simulate transmission over a span of decades in a large community in less than a minute. This computational efficiency allows us to calibrate the model within an inference framework, and we show an illustrative example of calibration using an adaptive population Monte Carlo Approximate Bayesian Computation algorithm for a community in Zambia that was part of the HPTN-071 (PopART) trial. We compare the detailed model output to real-world data collected during the trial from this community. Finally, we project how the HIV epidemic would have changed over time in this community if no intervention from the trial had occurred.
- Subjects :
- RNA viruses
Male
Epidemiology
Computer science
Human immunodeficiency virus (HIV)
HIV Infections
Pathology and Laboratory Medicine
medicine.disease_cause
Geographical Locations
Immunodeficiency Viruses
Antiretroviral Therapy, Highly Active
Prevalence
Econometrics
Public and Occupational Health
Viral suppression
Biology (General)
Hiv transmission
Virus Testing
Ecology
Simulation and Modeling
Incidence
virus diseases
Middle Aged
Vaccination and Immunization
Computational Theory and Mathematics
HIV epidemiology
Medical Microbiology
Viral Pathogens
Modeling and Simulation
Viruses
Disease Progression
Female
Pathogens
Algorithms
Research Article
Adult
Sexual network
Adolescent
Bioinformatics
QH301-705.5
Immunology
Antiretroviral Therapy
Zambia
Context (language use)
Research and Analysis Methods
HPTN 071 (PopART) Study Team
Bayesian inference
Microbiology
Young Adult
Cellular and Molecular Neuroscience
Individual based
Antiviral Therapy
Diagnostic Medicine
Retroviruses
Genetics
medicine
Humans
Computer Simulation
IBM
Microbial Pathogens
Molecular Biology
01 Mathematical Sciences
Ecology, Evolution, Behavior and Systematics
Aged
Medicine and health sciences
Stochastic Processes
Models, Statistical
Lentivirus
Organisms
Hiv epidemiology
Biology and Life Sciences
HIV
Reproducibility of Results
06 Biological Sciences
Age Groups
Medical Risk Factors
People and Places
Africa
Population Groupings
Preventive Medicine
08 Information and Computing Sciences
Subjects
Details
- Language :
- English
- ISSN :
- 15537358 and 1553734X
- Volume :
- 17
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....546bc695bb33a6a505d56dd6677fd345