Bayesian Emulation and History Matching of JUNE

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