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

Michael Dunne1, Hossein Mohammadi1, Peter Challenor1

  • 1College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.

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Summary
This summary is machine-generated.

This tutorial introduces uncertainty quantification (UQ) for stochastic epidemic models, crucial for understanding complex systems. It offers a workflow and visualizations to aid practitioners in applying UQ to epidemiological modeling and other fields.

Keywords:
CalibrationHistory matchingSEIRStochastic epidemic modelUncertainty quantification

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Area of Science:

  • Epidemiology
  • Computational Statistics

Background:

  • Uncertainty quantification (UQ) is vital for complex systems modeling but underutilized in stochastic epidemiology.
  • Existing UQ methods offer a framework for assessing model uncertainties.

Purpose of the Study:

  • To provide a tutorial on UQ for stochastic epidemic models.
  • To facilitate the adoption of UQ by practitioners working with complex stochastic simulators.
  • To present novel visualization techniques for sensitivity analysis and UQ outputs.

Main Methods:

  • A formal workflow for UQ is presented, detailing key decisions and considerations.
  • Methods are illustrated using a stochastic SARS-CoV-2 transmission and patient outcome model for the UK.
  • New visualization approaches for high-dimensional UQ and sensitivity analysis outputs are introduced.

Main Results:

  • The tutorial demonstrates a practical application of UQ in an epidemiological context.
  • The study provides a clear framework for implementing UQ in stochastic epidemic models.
  • Novel visualization methods enhance the interpretability of UQ and sensitivity analysis results.

Conclusions:

  • UQ is a valuable, yet underused, tool for stochastic epidemic modeling.
  • The provided workflow and visualizations can significantly aid practitioners in applying UQ.
  • This work promotes broader adoption of UQ across various complex systems modeling applications.