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Complex model calibration through emulation, a worked example for a stochastic epidemic model

Dunne, Michael; Mohammadi, Hossein; Challenor, Peter; Borgo, Rita; Porphyre, Thibaud; Vernon, Ian; Firat, Elif E.; Turkay, Cagatay; Torsney-Weir, Thomas; Goldstein, Michael; Reeve, Richard; Fang, Hui; Swallow, Ben

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Authors

Michael Dunne

Hossein Mohammadi

Peter Challenor

Rita Borgo

Thibaud Porphyre

Elif E. Firat

Cagatay Turkay

Thomas Torsney-Weir

Richard Reeve

Hui Fang

Ben Swallow



Abstract

Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties 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., …Swallow, B. (2022). Complex model calibration through emulation, a worked example for a stochastic epidemic model. Epidemics, 39, Article 100574. https://doi.org/10.1016/j.epidem.2022.100574

Journal Article Type Article
Acceptance Date Apr 29, 2022
Online Publication Date May 16, 2022
Publication Date 2022-06
Deposit Date May 18, 2022
Publicly Available Date Mar 29, 2024
Journal Epidemics
Print ISSN 1755-4365
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 39
Article Number 100574
DOI https://doi.org/10.1016/j.epidem.2022.100574

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Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright Statement
This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.




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