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

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, Ben Swallow*

*Corresponding author for this work

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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.

Original languageEnglish
Article number100574
Number of pages13
Early online date23 May 2022
Publication statusPublished - 1 Jun 2022


  • Uncertainty quantification
  • History matching
  • Stochastic epidemic model
  • SEIR
  • Calibration
  • Covid-19


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