Complex model calibration through emulation, a worked example for a stochastic epidemic model
Abstract
Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertain-ties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology, however they have thus far not been widely used in this context. In this paper, we provide a tutorial on uncertainty quantification of stochastic epidemic models, aiming to facilitate the use of the uncertainty quantification paradigm for practitioners with other complex stochastic simulators of applied systems. We provide a formal workflow including the important decisions and considerations that need to be taken, and illustrate the methods over a simple stochastic epidemic model of UK SARS-CoV-2 transmission and patient outcome. We also present new approaches to visualisation of outputs from sensitivity analyses and uncertainty quantification more generally in high input and/or output dimensions.
Citation
Dunne , M , Mohammadi , H , Challenor , P , Borgo , R , Porphyre , T , Vernon , I , Firat , E E , Turkay , C , Torsney-Weir , T , Goldstein , M , Reeve , R , Fang , H & Swallow , B 2022 , ' Complex model calibration through emulation, a worked example for a stochastic epidemic model ' , Epidemics , vol. 39 , 100574 . https://doi.org/10.1016/j.epidem.2022.100574
Publication
Epidemics
Status
Peer reviewed
ISSN
1755-4365Type
Journal article
Rights
Copyright © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Description
Funding: This work was supported by EPSRC, United Kingdom grant no. EP/R014604/1. RR was funded by STFC, United Kingdom grant no ST/V006126/1. IV gratefully acknowledges Wellcome funding (218261/Z/19/Z) and EPSRC funding (EP W011956). TP gratefully acknowledges funding from the Scottish Government Rural and Environment Science and Analytical Services Division, United Kingdom, as part of the Centre of Expertise on Animal Disease Outbreaks (EPIC). TP would also like to thank the French National Research Agency and Boehringer Ingelheim Animal Health France for support through the IDEXLYON project (ANR-16-IDEX-0005) and the Industrial Chair in Veterinary Public Health, as part of the VPH Hub in Lyon.Collections
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