@article{Dunne_M-2022_86733,
title = {Complex model calibration through emulation, a worked example for a stochastic epidemic model},
author = {Dunne, M. and Mohammadi, H. and Challenor, P. and Borgo, R. and Porphyre, T. and Vernon, I. and Firat, E. and Turkay, C. and Torsney-Weir, T. and Goldstein, M. and Reeve, R. and Fang, H. and Swallow, B.},
month = {jun},
year = {2022},
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.},
volume = {39},
journal = {Epidemics},
publisher = {Elsevier},
url = {http://dx.doi.org/10.1016/j.epidem.2022.100574},
}
