BI-FIDELITY STOCHASTIC COLLOCATION METHODS FOR EPIDEMIC TRANSPORT MODELS WITH UNCERTAINTIES
Networks and Heterogeneous Media
; 17(3):401-425, 2022.
Article
in English
| Scopus | ID: covidwho-1875877
ABSTRACT
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. © 2021 Giulia Bertaglia, Liu Liu, Lorenzo Pareschi and Xueyu Zhu.
asymptotic-preserving schemes; Bi-fidelity methods; diffusion limit; epidemic models; kinetic transport equations; uncertainty quantification; Stochastic models; Stochastic systems; Uncertainty analysis; Asymptotic preserving schemes; Bi-fidelity method; Diffusion limits; Epidemic modeling; Fidelity methods; Stochastic collocation method; Transport modelling; Uncertainty; Uncertainty quantifications; Epidemiology
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Networks and Heterogeneous Media
Year:
2022
Document Type:
Article
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