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.

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., Sedgewick, A., Shi, D., Truong, H., Turner, M., Walker, J., Caulfield, T., Fong, K., & 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