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System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19.
Wang, Z; Zhang, X; Teichert, G H; Carrasco-Teja, M; Garikipati, K.
  • Wang Z; Mechanical Engineering, Mathematics and the Michigan Institute for Computational, Discovery and Engineering, University of Michigan, Ann Arbor, USA.
  • Zhang X; Mechanical Engineering, Mathematics and the Michigan Institute for Computational, Discovery and Engineering, University of Michigan, Ann Arbor, USA.
  • Teichert GH; Mechanical Engineering, Mathematics and the Michigan Institute for Computational, Discovery and Engineering, University of Michigan, Ann Arbor, USA.
  • Carrasco-Teja M; Mechanical Engineering, Mathematics and the Michigan Institute for Computational, Discovery and Engineering, University of Michigan, Ann Arbor, USA.
  • Garikipati K; Mechanical Engineering, Mathematics and the Michigan Institute for Computational, Discovery and Engineering, University of Michigan, Ann Arbor, USA.
Comput Mech ; 66(5): 1153-1176, 2020.
Article in English | MEDLINE | ID: covidwho-716301
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 35644-74, 2019; arXiv2001.04816 [cs.CE]) as well as Bayesian machine learning methods.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Comput Mech Year: 2020 Document Type: Article Affiliation country: S00466-020-01894-2

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Comput Mech Year: 2020 Document Type: Article Affiliation country: S00466-020-01894-2