Journal article
BI-FIDELITY STOCHASTIC COLLOCATION METHODS FOR EPIDEMIC TRANSPORT MODELS WITH UNCERTAINTIES
Networks and heterogeneous media, Vol.17(3), pp.401-425
03/01/2022
DOI: 10.3934/nhm.2022013
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 low fidelity 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.
Details
- Title: Subtitle
- BI-FIDELITY STOCHASTIC COLLOCATION METHODS FOR EPIDEMIC TRANSPORT MODELS WITH UNCERTAINTIES
- Creators
- Giulia Bertaglia - Ist Nazl Alta Matemat Francesco Severr INdAM, I-00185 Rome, ItalyLiu Liu - Chinese Univ Hong Kong, Dept Math, Shatin, Hong Kong, Peoples R ChinaLorenzo Pareschi - Univ Ferrara, Dept Math & Comp Sci, I-44121 Ferrara, ItalyXueyu Zhu - Univ Iowa, Dept Math, Iowa City, IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- Networks and heterogeneous media, Vol.17(3), pp.401-425
- Publisher
- Amer Inst Mathematical Sciences-Aims
- DOI
- 10.3934/nhm.2022013
- ISSN
- 1556-1801
- eISSN
- 1556-181X
- Number of pages
- 25
- Grant note
- 504054 / Simons Foundation 24301021 / Research Grants Council of Hong Kong; Hong Kong Research Grants Council Italian National Institute of High Mathematics, INdAM (GNCS) 2017KKJP4X / MIUR PRIN 2017; Ministry of Education, Universities and Research (MIUR) 24301021 / Chinese University of Hong Kong; Chinese University of Hong Kong
- Language
- English
- Date published
- 03/01/2022
- Academic Unit
- Mathematics
- Record Identifier
- 9984244029002771
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