System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19

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...

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Detalles Bibliográficos
Autores principales: Wang, Z., Zhang, X., Teichert, G. H., Carrasco-Teja, M., Garikipati, K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8824376/
https://www.ncbi.nlm.nih.gov/pubmed/35194281
http://dx.doi.org/10.1007/s00466-020-01894-2
Descripción
Sumario: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.

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