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Bayesian Emulation and History Matching of JUNE

Vernon, I.; Owen, J.; Aylett-Bullock, J.; Cuestra-Lazaro, C.; Frawley, J.; Quera-Bofarull, A.; Sedgewick, A.; Shi, D.; Truong, H.; Turner, M.; Walker, J.; Caulfield, T.; Fong, K.; Krauss, F.

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Authors

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Jonathan Owen jonathan.owen@durham.ac.uk
PGR Student Doctor of Philosophy

J. Aylett-Bullock

C. Cuestra-Lazaro

A. Quera-Bofarull

A. Sedgewick

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Dr Difu Shi difu.shi@durham.ac.uk
Science Translation Fellow

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Henry Truong henry.truong@durham.ac.uk
PGR Student Doctor of Philosophy

M. Turner

Joseph Walker j.j.walker@durham.ac.uk
PGR Student Doctor of Philosophy

T. Caulfield

K. Fong



Abstract

We analyse 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.

Citation

Vernon, I., Owen, J., Aylett-Bullock, J., Cuestra-Lazaro, C., Frawley, J., Quera-Bofarull, A., …Krauss, F. (2022). Bayesian Emulation and History Matching of JUNE. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2233), Article 20220039. https://doi.org/10.1098/rsta.2022.0039

Journal Article Type Article
Acceptance Date Jun 7, 2022
Online Publication Date Aug 15, 2022
Publication Date Oct 3, 2022
Deposit Date Jun 8, 2022
Publicly Available Date Jun 8, 2022
Journal Philosophical Transactions A
Print ISSN 1364-503X
Electronic ISSN 1471-2962
Publisher The Royal Society
Peer Reviewed Peer Reviewed
Volume 380
Issue 2233
Article Number 20220039
DOI https://doi.org/10.1098/rsta.2022.0039
Public URL https://durham-repository.worktribe.com/output/1202890

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Copyright Statement
© 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.







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