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

Epidemics. 2022 Jun:39:100574. doi: 10.1016/j.epidem.2022.100574. Epub 2022 May 16.

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.

Keywords: Calibration; History matching; SEIR; Stochastic epidemic model; Uncertainty quantification.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19* / epidemiology
  • Calibration
  • Epidemics*
  • Humans
  • SARS-CoV-2
  • Uncertainty