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

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

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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 Interior Dome and Courtesy Lighting 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 Tortilla Bowl Makers 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.