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Vernon I, Owen J, Aylett-Bullock J, Cuesta-Lazaro C, Frawley J, Quera-Bofarull A, Sedgewick A, Shi D, Truong H, Turner M, Walker J, Caulfield T, Fong K, Krauss F. Bayesian emulation and history matching of JUNE. Philos Trans A Math Phys Eng Sci 2022; 380:20220039. [PMID: 35965471 PMCID: PMC9376712 DOI: 10.1098/rsta.2022.0039] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/07/2022] [Indexed: 05/21/2023]
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'.
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Affiliation(s)
- I. Vernon
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Department of Mathematical Sciences, Durham University, Durham DH13LE, UK
| | - J. Owen
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Department of Mathematical Sciences, Durham University, Durham DH13LE, UK
| | - J. Aylett-Bullock
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - C. Cuesta-Lazaro
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - J. Frawley
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Advanced Research Computing, Durham University, Durham DH13LE, UK
| | - A. Quera-Bofarull
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - A. Sedgewick
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Centre for Extragalactic Astronomy, Durham University, Durham DH13LE, UK
| | - D. Shi
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - H. Truong
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - M. Turner
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Advanced Research Computing, Durham University, Durham DH13LE, UK
| | - J. Walker
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - T. Caulfield
- Department of Computer Science, Durham University, Durham DH13LE, UK
| | - K. Fong
- Department of Science, Technology, Engineering and Public Policy, University College London, London WC1E6BT, UK
- Department of Anaesthesia, University College London Hospital, London NW12BU, UK
| | - F. Krauss
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
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Vernon I, Owen J, Aylett-Bullock J, Cuesta-Lazaro C, Frawley J, Quera-Bofarull A, Sedgewick A, Shi D, Truong H, Turner M, Walker J, Caulfield T, Fong K, Krauss F. Bayesian emulation and history matching of JUNE. Philos Trans A Math Phys Eng Sci 2022; 380:20210039. [PMID: 35965471 DOI: 10.1098/rsta.2021.0039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 12/06/2021] [Indexed: 05/21/2023]
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'.
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Affiliation(s)
- I Vernon
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Department of Mathematical Sciences, Durham University, Durham DH13LE, UK
| | - J Owen
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Department of Mathematical Sciences, Durham University, Durham DH13LE, UK
| | - J Aylett-Bullock
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - C Cuesta-Lazaro
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - J Frawley
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Advanced Research Computing, Durham University, Durham DH13LE, UK
| | - A Quera-Bofarull
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - A Sedgewick
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Centre for Extragalactic Astronomy, Durham University, Durham DH13LE, UK
| | - D Shi
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Computational Cosmology, Durham University, Durham DH13LE, UK
| | - H Truong
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - M Turner
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Advanced Research Computing, Durham University, Durham DH13LE, UK
| | - J Walker
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
| | - T Caulfield
- Department of Computer Science, Durham University, Durham DH13LE, UK
| | - K Fong
- Department of Science, Technology, Engineering and Public Policy, University College London, London WC1E6BT, UK
- Department of Anaesthesia, University College London Hospital, London NW12BU, UK
| | - F Krauss
- Institute for Data Science, Durham University, Durham DH13LE, UK
- Institute for Particle Physics Phenomenology, Durham University, Durham DH13LE, UK
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