  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.
