Vernon, I, Owen, J, Aylett-Bullock, J et al. (11 more authors) (2022) Bayesian emulation and history matching of JUNE. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380 (2233). 20220039. ISSN 1364-503X
Abstract
We analyze JUNE: a detailed model of COVID-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. JUNE requires substantial computational resources to evaluate, making model calibration and general uncertainty analysis extremely challenging. We describe and employ the uncertainty quantification approaches of Bayes linear emulation and history matching to mimic JUNE and to perform a global parameter search, hence identifying regions of parameter space that produce acceptable matches to observed data, and demonstrating the capability of such methods.
This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | disease models, Bayes linear, emulation, calibration, history matching |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst for Climate & Atmos Science (ICAS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Sep 2022 13:20 |
Last Modified: | 20 Dec 2022 16:40 |
Status: | Published |
Publisher: | The Royal Society |
Identification Number: | 10.1098/rsta.2022.0039 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190903 |