1. Complex model calibration through emulation, a worked example for a stochastic epidemic model
- Author
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Michael Dunne, Hossein Mohammadi, Peter Challenor, Rita Borgo, Thibaud Porphyre, Ian Vernon, Elif E. Firat, Cagatay Turkay, Thomas Torsney-Weir, Michael Goldstein, Richard Reeve, Hui Fang, and Ben Swallow
- Subjects
Uncertainty quantification ,History matching ,Stochastic epidemic model ,SEIR ,Calibration ,Infectious and parasitic diseases ,RC109-216 - 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.
- Published
- 2022
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