PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial.
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 t...
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-09-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1009301 |
| _version_ | 1798002643680886784 |
|---|---|
| author | Michael Pickles Anne Cori William J M Probert Rafael Sauter Robert Hinch Sarah Fidler Helen Ayles Peter Bock Deborah Donnell Ethan Wilson Estelle Piwowar-Manning Sian Floyd Richard J Hayes Christophe Fraser HPTN 071 (PopART) Study Team |
| author_facet | Michael Pickles Anne Cori William J M Probert Rafael Sauter Robert Hinch Sarah Fidler Helen Ayles Peter Bock Deborah Donnell Ethan Wilson Estelle Piwowar-Manning Sian Floyd Richard J Hayes Christophe Fraser HPTN 071 (PopART) Study Team |
| author_sort | Michael Pickles |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T11:55:25Z |
| format | Article |
| id | doaj.art-affef60ff94443cda079aebd0c49e70c |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T11:55:25Z |
| publishDate | 2021-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-affef60ff94443cda079aebd0c49e70c2022-12-22T04:25:10ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-09-01179e100930110.1371/journal.pcbi.1009301PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial.Michael PicklesAnne CoriWilliam J M ProbertRafael SauterRobert HinchSarah FidlerHelen AylesPeter BockDeborah DonnellEthan WilsonEstelle Piwowar-ManningSian FloydRichard J HayesChristophe FraserHPTN 071 (PopART) Study TeamMathematical 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.https://doi.org/10.1371/journal.pcbi.1009301 |
| spellingShingle | Michael Pickles Anne Cori William J M Probert Rafael Sauter Robert Hinch Sarah Fidler Helen Ayles Peter Bock Deborah Donnell Ethan Wilson Estelle Piwowar-Manning Sian Floyd Richard J Hayes Christophe Fraser HPTN 071 (PopART) Study Team PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial. PLoS Computational Biology |
| title | PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial. |
| title_full | PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial. |
| title_fullStr | PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial. |
| title_full_unstemmed | PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial. |
| title_short | PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial. |
| title_sort | popart ibm a highly efficient stochastic individual based simulation model of generalised hiv epidemics developed in the context of the hptn 071 popart trial |
| url | https://doi.org/10.1371/journal.pcbi.1009301 |
| work_keys_str_mv | AT michaelpickles popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT annecori popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT williamjmprobert popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT rafaelsauter popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT roberthinch popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT sarahfidler popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT helenayles popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT peterbock popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT deborahdonnell popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT ethanwilson popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT estellepiwowarmanning popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT sianfloyd popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT richardjhayes popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT christophefraser popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial AT hptn071popartstudyteam popartibmahighlyefficientstochasticindividualbasedsimulationmodelofgeneralisedhivepidemicsdevelopedinthecontextofthehptn071poparttrial |