Bayesian emulation and history matching of JUNE

被引：8
|
作者：
Vernon, I. [1 ,2 ]
Owen, J. [1 ,2 ]
Aylett-Bullock, J. [1 ,3 ]
Cuesta-Lazaro, C. [1 ,4 ]
Frawley, J. [1 ,5 ]
Quera-Bofarull, A. [1 ,4 ]
Sedgewick, A. [1 ,6 ]
Shi, D. [1 ,4 ]
Truong, H. [1 ,3 ]
Turner, M. [1 ,5 ]
Walker, J. [1 ,3 ]
Caulfield, T. [7 ]
Fong, K. [8 ,9 ]
Krauss, F. [1 ,3 ]
机构：
[1] Univ Durham, Inst Data Sci, Durham DH13LE, England
[2] Univ Durham, Dept Math Sci, Durham DH13LE, England
[3] Univ Durham, Inst Particle Phys Phenomenol, Durham DH13LE, England
[4] Univ Durham, Inst Computat Cosmol, Durham DH13LE, England
[5] Univ Durham, Adv Res Comp, Durham DH13LE, England
[6] Univ Durham, Ctr Extragalact Astron, Durham DH13LE, England
[7] Univ Durham, Dept Comp Sci, Durham DH13LE, England
[8] UCL, Dept Sci Technol Engn & Publ Policy, London WC1E6BT, England
[9] Univ Coll London Hosp, Dept Anaesthesia, London NW12BU, England
来源：
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES | 2022年 / 380卷 / 2233期
基金：
英国惠康基金;
关键词：
disease models; Bayes linear; emulation; calibration; history matching; GALAXY FORMATION; COMPUTER CODE; INFERENCE; PREDICTION; DESIGN;
D O I：
10.1098/rsta.2022.0039
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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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页数：20
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