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Journal Articles Epidemics Year : 2022

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

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.
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Dates and versions

hal-03747498 , version 1 (14-06-2024)

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Michael Dunne, Hossein Mohammadi, Peter Challenor, Rita Borgo, Thibaud Porphyre, et al.. Complex model calibration through emulation, a worked example for a stochastic epidemic model. Epidemics, 2022, 39, pp.100574. ⟨10.1016/j.epidem.2022.100574⟩. ⟨hal-03747498⟩
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