PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial
Author(s)
Type
Journal Article
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.
Date Issued
2021-09-01
Date Acceptance
2021-07-22
Citation
PLOS COMPUTATIONAL BIOLOGY, 2021, 17 (9)
ISSN
1553-734X
Publisher
PUBLIC LIBRARY SCIENCE
Journal / Book Title
PLOS COMPUTATIONAL BIOLOGY
Volume
17
Issue
9
Copyright Statement
© 2021 Pickles et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Sponsor
Medical Research Council (MRC)
Medical Research Council (MRC)
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000724165700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
PII: PCOMPBIOL-D-20-01554
Grant Number
MR/K010174/1B
MR/R015600/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
APPROXIMATE BAYESIAN COMPUTATION
CONCURRENT PARTNERSHIPS
VIRAL LOAD
AFRICA
SEX
INFECTION
DYNAMICS
AGE
Publication Status
Published
Coverage Spatial
United States
Article Number
ARTN e1009301