Bayesian emulation and history matching of <tt>JUNE</tt>-Reference-Cited by-同舟云学术

Bayesian emulation and history matching of JUNE

Published:2022-08-15 Issue:2233 Volume:380 Page:
ISSN:1364-503X
Container-title:Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
language:en
Short-container-title:Phil. Trans. R. Soc. A.

Author:

Vernon I.¹²^ORCID,Owen J.¹²,Aylett-Bullock J.¹³^ORCID,Cuesta-Lazaro C.¹⁴,Frawley J.¹⁵,Quera-Bofarull A.¹⁴^ORCID,Sedgewick A.¹⁶,Shi D.¹⁴,Truong H.¹³^ORCID,Turner M.¹⁵,Walker J.¹³,Caulfield T.⁷,Fong K.⁸⁹,Krauss F.¹³^ORCID

Affiliation:

1. Institute for Data Science, Durham University, Durham DH13LE, UK

2. Department of Mathematical Sciences, Durham University, Durham DH13LE, UK

3. Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK

4. Institute for Computational Cosmology, Durham University, Durham DH13LE, UK

5. Advanced Research Computing, Durham University, Durham DH13LE, UK

6. Centre for Extragalactic Astronomy, Durham University, Durham DH13LE, UK

7. Department of Computer Science, Durham University, Durham DH13LE, UK

8. Department of Science, Technology, Engineering and Public Policy, University College London, London WC1E6BT, UK

9. Department of Anaesthesia, University College London Hospital, London NW12BU, UK

Abstract

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’.

Funder

Science and Technology Facilities Council

Wellcome

UK Research and Innovation

Publisher

The Royal Society

Subject

General Physics and Astronomy,General Engineering,General Mathematics

Link

https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2022.0039

Reference54 articles.

1. J une : open-source individual-based epidemiology simulation