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
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author Wang, Z.
Zhang, X.
Teichert, G. H.
Carrasco-Teja, M.
Garikipati, K.
author_facet Wang, Z.
Zhang, X.
Teichert, G. H.
Carrasco-Teja, M.
Garikipati, K.
author_sort Wang, Z.
collection PubMed
description 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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spelling pubmed-88243762022-02-18 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. Comput Mech Original Paper 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. Springer Berlin Heidelberg 2020-08-12 2020 /pmc/articles/PMC8824376/ /pubmed/35194281 http://dx.doi.org/10.1007/s00466-020-01894-2 Text en © Springer-Verlag GmbH Germany, part of Springer Nature 2020, corrected publication 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Paper
Wang, Z.
Zhang, X.
Teichert, G. H.
Carrasco-Teja, M.
Garikipati, K.
System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
title System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
title_full System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
title_fullStr System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
title_full_unstemmed System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
title_short System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
title_sort system inference for the spatio-temporal evolution of infectious diseases: michigan in the time of covid-19
topic Original Paper
url 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
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