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)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• 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
• Publisher: Elsevier
• ISSN: 1755-4365
• 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

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
311856		Open Epidemiology for COVID-19: a transparent, traceable, open source pipeline for reproducible science	Richard Reeve	Science and Technology Facilities Council (STFC)	ST/V006126/1	Institute of Biodiversity, Animal Health and Comparative Medicine