Bi-fidelity stochastic collocation methods for epidemic transport models with uncertainties

Uncertainty in data is certainly one of the main problems epidemiology, as shown by recent COVID-19 pandemic. The need for efficient methods capable quantifying uncertainty mathematical model essential order to produce realistic scenarios spread infection. In this paper, we introduce a bi-fidelity approach quantify spatially dependent epidemic models. based on evaluating high-fidelity small number samples properly selected from large evaluations low-fidelity model. particular, will consider class multiscale transport models recently introduced Bertaglia, Boscheri, Dimarco & Pareschi, Math. Biosci. Eng. (2021) and Mod. Meth. App. Scie. reference use simple two-velocity discrete evaluations. Both share same diffusive behavior are solved with ad-hoc asymptotic-preserving numerical discretizations. A series experiments confirm validity approach.