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
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'.
引用
收藏
页数:20
相关论文
共 50 条
  • [41] CaloDREAM - Detector response emulation via attentive flow matching
    Favaro, Luigi
    Ore, Ayodele
    Schweitzer, Sofia Palacios
    Plehn, Tilman
    SCIPOST PHYSICS, 2025, 18 (03):
  • [42] Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks
    Kungl, Akos F.
    Schmitt, Sebastian
    Klaehn, Johann
    Mueller, Paul
    Baumbach, Andreas
    Dold, Dominik
    Kugele, Alexander
    Mueller, Eric
    Koke, Christoph
    Kleider, Mitja
    Mauch, Christian
    Breitwieser, Oliver
    Leng, Luziwei
    Guertler, Nico
    Guettler, Maurice
    Husmann, Dan
    Husmann, Kai
    Hartel, Andreas
    Karasenko, Vitali
    Gruebl, Andreas
    Schemmel, Johannes
    Meier, Karlheinz
    Petrovici, Mihai A.
    FRONTIERS IN NEUROSCIENCE, 2019, 13
  • [43] Bayesian Emulation for Multi-Step Optimization in Decision Problems
    Irie, Kaoru
    West, Mike
    BAYESIAN ANALYSIS, 2019, 14 (01): : 137 - 160
  • [44] Mining data from hemodynamic simulations via Bayesian emulation
    Kolachalama, Vijaya B.
    Bressloff, Neil W.
    Nair, Prasanth B.
    BIOMEDICAL ENGINEERING ONLINE, 2007, 6 (1) : 47
  • [45] A new methodology for Bayesian history matching using parallel interacting Markov chain Monte Carlo
    Maschio, Celio
    Schiozer, Denis J.
    INVERSE PROBLEMS IN SCIENCE AND ENGINEERING, 2018, 26 (04) : 498 - 529
  • [46] A Simulation Approach to Bayesian Emulation of Complex Dynamic Computer Models
    Bhattacharya, Sourabh
    BAYESIAN ANALYSIS, 2007, 2 (04): : 783 - 815
  • [47] Applications of emulation and Bayesian methods in heavy-ion physics
    Paquet, Jean-Francois
    JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS, 2024, 51 (10)
  • [48] Fitting two human atrial cell models to experimental data using Bayesian history matching
    Coveney, Sam
    Clayton, Richard H.
    PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY, 2018, 139 : 43 - 58
  • [49] FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation
    Bouabid, Shahine
    Sejdinovic, Dino
    Watson-Parris, Duncan
    JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 2024, 16 (06)
  • [50] Mining data from hemodynamic simulations via Bayesian emulation
    Vijaya B Kolachalama
    Neil W Bressloff
    Prasanth B Nair
    BioMedical Engineering OnLine, 6