Abstract
AbstractWe 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 the June model and to perform a global parameter search, hence identifying regions of parameter space that produce acceptable matches to observed data.
Publisher
Cold Spring Harbor Laboratory