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 d
ecisions 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.
The question of the stability of the four dimensional Gross-Perry-Sorkin Kaluza-Klein magnetic monopole solution is investigated within the framework of a N=2, D=5 supergravity theory. We show that this solution does not support a spin structure of t
he Killing type and is therefore, contrary to previous expectations, not necessarily stable.
