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

Dunne, M. et al. (2022) Complex model calibration through emulation, a worked example for a stochastic epidemic model. Epidemics, 39, 100574. (doi: 10.1016/j.epidem.2022.100574) (PMID:35617882) (PMCID:PMC9109972)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Reeve, Professor Richard and Swallow, Dr Ben
Authors: 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., and Swallow, B.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
Journal Name:Epidemics
ISSN (Online):1878-0067
Published Online:16 May 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Epidemics 39: 100574
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
311856Open Epidemiology for COVID-19: a transparent, traceable, open source pipeline for reproducible scienceRichard ReeveScience and Technology Facilities Council (STFC)ST/V006126/1Institute of Biodiversity, Animal Health and Comparative Medicine