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Bayesian Emulation and History Matching of JUNE
[article]
2022
medRxiv
pre-print
Preserved Fulltext
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 the JUNE model and to perform a global parameter search, hence identifying regions of
doi:10.1101/2022.02.21.22271249
fatcat:ebwi3qwecvdxxklwlkhmid4ucm