TY - JOUR AU - Dunne, Michael AU - Mohammadi, Hossein AU - Challenor, Peter AU - Borgo, Rita AU - Porphyre, Thibaud AU - Vernon, Ian AU - Firat, Elif E. AU - Turkay, Cagatay AU - Torsney-Weir, Thomas AU - Goldstein, Michael AU - Reeve, Richard AU - Fang, Hui AU - Swallow, Ben PY - 2022 DA - June TI - Complex model calibration through emulation, a worked example for a stochastic epidemic model JO - Epidemics VL - 39 DO - http://dx.doi.org/10.1016/j.epidem.2022.100574 AB - 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. PB - Elsevier UR - http://eprints.gla.ac.uk/271989/ KW - Coronavirus (COVID-19) ER