Structural identifiability and observability of compartmental models of the COVID-19 pandemic

The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of...

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Autores: Massonis, Gemma, Banga, Julio R., Villaverde, A. F.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/226706
Acceso en línea:http://hdl.handle.net/10261/226706
Access Level:acceso abierto
Palabra clave:Identifiability
Observability
Dynamic modelling
Epidemiology
COVID-19
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spelling Structural identifiability and observability of compartmental models of the COVID-19 pandemicMassonis, GemmaBanga, Julio R.Villaverde, A. F.IdentifiabilityObservabilityDynamic modellingEpidemiologyCOVID-19The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights – as well as the possibility of controlling the system – may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.This research has received funding from the Spanish Ministry of Science, Innovation and Universities and the European Union FEDER under project grant SYNBIOCONTROL (DPI2017-82896-C2-2-R) and the CSIC, Spain intramural project grant MOEBIUS (PIE 202070E062). TPeer reviewedElsevier BVMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202120212021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/226706reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-82896-C2-2-RDPI2017-82896-C2-2-R/AEI/10.13039/501100011033https://doi.org/10.1016/j.arcontrol.2020.12.001Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2267062026-05-22T06:33:51Z
dc.title.none.fl_str_mv Structural identifiability and observability of compartmental models of the COVID-19 pandemic
title Structural identifiability and observability of compartmental models of the COVID-19 pandemic
spellingShingle Structural identifiability and observability of compartmental models of the COVID-19 pandemic
Massonis, Gemma
Identifiability
Observability
Dynamic modelling
Epidemiology
COVID-19
title_short Structural identifiability and observability of compartmental models of the COVID-19 pandemic
title_full Structural identifiability and observability of compartmental models of the COVID-19 pandemic
title_fullStr Structural identifiability and observability of compartmental models of the COVID-19 pandemic
title_full_unstemmed Structural identifiability and observability of compartmental models of the COVID-19 pandemic
title_sort Structural identifiability and observability of compartmental models of the COVID-19 pandemic
dc.creator.none.fl_str_mv Massonis, Gemma
Banga, Julio R.
Villaverde, A. F.
author Massonis, Gemma
author_facet Massonis, Gemma
Banga, Julio R.
Villaverde, A. F.
author_role author
author2 Banga, Julio R.
Villaverde, A. F.
author2_role author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Identifiability
Observability
Dynamic modelling
Epidemiology
COVID-19
topic Identifiability
Observability
Dynamic modelling
Epidemiology
COVID-19
description The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights – as well as the possibility of controlling the system – may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/226706
url http://hdl.handle.net/10261/226706
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-82896-C2-2-R
DPI2017-82896-C2-2-R/AEI/10.13039/501100011033
https://doi.org/10.1016/j.arcontrol.2020.12.001
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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