Bayesian Emulation and History Matching of JUNE

We analyse <monospace> J <sc>une</sc> </monospace> : a detailed model of Covid-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. <monospace> J <sc>une</sc> </monospace> 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 the <monospace> J <sc>une</sc> </monospace> model and to perform a global parameter search, hence identifying regions of parameter space that produce acceptable matches to observed data.