Efficient Uncertainty Quantification and Sensitivity Analysis in Epidemic Modelling using Polynomial Chaos

TL;DR

This paper introduces an efficient polynomial chaos framework for quantifying uncertainty and analyzing sensitivity in epidemic models, demonstrated through COVID-19 case studies with minimal model evaluations.

Contribution

It presents a novel application of generalized Polynomial Chaos for uncertainty quantification in SIR models, enabling accurate insights with few simulations.

Findings

Few model evaluations yield reliable uncertainty estimates.

The method effectively predicts peak times and superspreading events.

Demonstrated on Danish COVID-19 data.

Abstract

In the political decision process and control of COVID-19 (and other epidemic diseases), mathematical models play an important role. It is crucial to understand and quantify the uncertainty in models and their predictions in order to take the right decisions and trustfully communicate results and limitations. We propose to do uncertainty quantification in SIR-type models using the efficient framework of generalized Polynomial Chaos. Through two particular case studies based on Danish data for the spread of Covid-19 we demonstrate the applicability of the technique. The test cases are related to peak time estimation and superspeading and illustrate how very few model evaluations can provide insightful statistics.