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

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Název: Bi-fidelity stochastic collocation methods for epidemic transport models with uncertainties
Autoři: Bertaglia, Giulia, Liu, Liu, Pareschi, Lorenzo, Zhu, Xueyu
Informace o vydavateli: 2021-10-27
Druh dokumentu: Electronic Resource
Abstrakt: 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 Bertaglia, Boscheri, Dimarco & Pareschi, Math. Biosci. Eng. (2021) and Boscheri, Dimarco & Pareschi, Math. Mod. Meth. App. Scie. (2021) 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.
Témata: Mathematics - Numerical Analysis, Quantitative Biology - Populations and Evolution, text
DOI: 10.3934.nhm.2022013
URL: http://arxiv.org/abs/2110.14579
Dostupnost: Open access content. Open access content
Other Numbers: COO oai:arXiv.org:2110.14579
Netw. Heterog. Media 17 (2022) 401-425
doi:10.3934/nhm.2022013
1333728944
Přispívající zdroj: CORNELL UNIV
From OAIster®, provided by the OCLC Cooperative.
Přístupové číslo: edsoai.on1333728944
Databáze: OAIster
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