Bayesian emulation and history matching of JUNE
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 descr...
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Veröffentlicht in: | Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences physical, and engineering sciences, 2022-10, Vol.380 (2233), p.20220039-20220039 |
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Hauptverfasser: | , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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Zusammenfassung: | 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’. |
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ISSN: | 1364-503X 1471-2962 |
DOI: | 10.1098/rsta.2022.0039 |