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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DOI:
10.1016/j.epidem.2022.100574
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发表时间:
2022-06
期刊:
影响因子:
3.8
通讯作者:
Swallow, Ben
中科院分区:
文献类型:
--
作者:
Dunne, Michael;Mohammadi, Hossein;Challenor, Peter;Borgo, Rita;Porphyre, Thibaud;Vernon, Ian;Firat, Elif E.;Turkay, Cagatay;Torsney-Weir, Thomas;Goldstein, Michael;Reeve, Richard;Fang, Hui;Swallow, Ben
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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