Bayesian emulation and history matching of JUNE. (3rd October 2022)

• Record Type: Journal Article
• Authors: Vernon, I.; Owen, J.; Aylett-Bullock, J.; Cuesta-Lazaro, C.; Frawley, J.; Quera-Bofarull, A.; Sedgewick, A.; Shi, D.; Truong, H.; Turner, M.; Walker, J.; Caulfield, T.; Fong, K.; Krauss, F.
• Is Part Of: Philosophical transactions. Volume 380:Number 2233(2022)
• Publication Date: 2022-10-03
• Subjects: disease models -- Bayes linear -- emulation -- calibration -- history matching; Physical sciences -- Periodicals; Engineering -- Periodicals; Mathematics -- Periodicals; 500
• DOI: 10.1098/rsta.2022.0039
• Languages: English
• ISSNs: 1364-503X

Abstract

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'.