TY - JOUR
AU - Bertaglia, Giulia
AU - Liu, Liu
AU - Pareschi, Lorenzo
AU - Zhu, Xueyu
PY - 2022
DA - June
TI - Bi-fidelity stochastic collocation methods for epidemic transport models with uncertainties
JO - Networks and Heterogeneous Media
EP - 425
VL - 17
IS - 3
DO - https://doi.org/10.3934/nhm.2022013
AB - Uncertainty in data is certainly one of the main problems in epidemiology, as shown by the recent COVID-19 pandemic. The need for efficient methods capable of quantifying uncertainty in the mathematical model is essential in order to produce realistic scenarios of the spread of infection. In this paper, we introduce a bi-fidelity approach to quantify uncertainty in spatially dependent epidemic models. The approach is based on evaluating a high-fidelity model on a small number of samples properly selected from a large number of evaluations of a low-fidelity model. In particular, we will consider the class of multiscale transport models recently introduced in [13, 7] as the high-fidelity reference and use simple two-velocity discrete models for lowfidelity evaluations. Both models share the same diffusive behavior and are solved with ad-hoc asymptotic-preserving numerical discretizations. A series of numerical experiments confirm the validity of the approach.
PB - American Institute of Mathematical Sciences
UR - https://researchportal.hw.ac.uk/en/publications/c1984403-f0bc-4cce-9469-8a5c046f908a
KW - Coronavirus (COVID-19)
ER