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• W3048608718 abstract "We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al. in Comput Methods Appl Mech Eng 356:44–74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods." @default.
• W3048608718 created "2020-08-18" @default.
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• W3048608718 date "2020-08-12" @default.
• W3048608718 modified "2023-10-12" @default.
• W3048608718 title "System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19" @default.
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• W3048608718 doi "https://doi.org/10.1007/s00466-020-01894-2" @default.
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