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

Blog Article

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 Puffer Jackets 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 Medical Thermometers quantification more generally in high input and/or output dimensions.