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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7752088/ https://www.ncbi.nlm.nih.gov/pubmed/33362427 http://dx.doi.org/10.1016/j.arcontrol.2020.12.001 |
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author | Massonis, Gemma Banga, Julio R. Villaverde, Alejandro F. |
author_facet | Massonis, Gemma Banga, Julio R. Villaverde, Alejandro F. |
author_sort | Massonis, Gemma |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-7752088 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77520882020-12-22 Structural identifiability and observability of compartmental models of the COVID-19 pandemic() Massonis, Gemma Banga, Julio R. Villaverde, Alejandro F. Annu Rev Control Tutorial Article 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. Elsevier Ltd. 2021 2020-12-21 /pmc/articles/PMC7752088/ /pubmed/33362427 http://dx.doi.org/10.1016/j.arcontrol.2020.12.001 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Tutorial Article Massonis, Gemma Banga, Julio R. Villaverde, Alejandro F. 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() |
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_short | 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() |
topic | Tutorial Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7752088/ https://www.ncbi.nlm.nih.gov/pubmed/33362427 http://dx.doi.org/10.1016/j.arcontrol.2020.12.001 |
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