Vernon, I. orcid.org/0000-0002-9161-9946, Owen, J. orcid.org/0009-0000-9618-9589, Aylett-Bullock, J. orcid.org/0000-0001-7551-3423 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
Keywords: | Bayes linear; calibration; disease models; emulation; history matching; Bayes Theorem; COVID-19; Humans; Uncertainty |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematical and Physical Sciences |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Nov 2024 08:24 |
Last Modified: | 26 Nov 2024 08:24 |
Status: | Published |
Publisher: | The Royal Society |
Refereed: | Yes |
Identification Number: | 10.1098/rsta.2022.0039 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220115 |
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