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Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks
Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
ISA. Published by Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349905/ https://www.ncbi.nlm.nih.gov/pubmed/34412892 http://dx.doi.org/10.1016/j.isatra.2021.08.008 |
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author | Tutsoy, Onder Polat, Adem |
author_facet | Tutsoy, Onder Polat, Adem |
author_sort | Tutsoy, Onder |
collection | PubMed |
description | Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein, to cope with these urgent healthcare demands, the state authorities seek ways to develop policies based on the estimated future casualties. These policies are mainly non-pharmacological policies including the restrictions, curfews, closures, and lockdowns. In this paper, we construct three model structures of the S [Formula: see text] I [Formula: see text] I [Formula: see text] I [Formula: see text] D-N (suspicious S [Formula: see text] , infected I [Formula: see text] , intensive care I [Formula: see text] , intubated I [Formula: see text] , and dead D together with the non-pharmacological policies N) holding two key targets. The first one is to predict the future COVID-19 casualties including the intensive care and intubated ones, which directly determine the need for urgent healthcare facilities, and the second one is to analyse the linear and non-linear dynamics of the COVID-19 pandemic under the non-pharmacological policies. In this respect, we have modified the non-pharmacological policies and incorporated them within the models whose parameters are learned from the available data. The trained models with the data released by the Turkish Health Ministry confirmed that the linear S [Formula: see text] I [Formula: see text] I [Formula: see text] I [Formula: see text] D-N model yields more accurate results under the imposed non-pharmacological policies. It is important to note that the non-pharmacological policies have a damping effect on the pandemic casualties and this can dominate the non-linear dynamics. Herein, a model without pharmacological or non-pharmacological policies might have more dominant non-linear dynamics. In addition, the paper considers two machine learning approaches to optimize the unknown parameters of the constructed models. The results show that the recursive neural network has superior performance for learning nonlinear dynamics. However, the batch least squares outperforms in the presence of linear dynamics and stochastic data. The estimated future pandemic casualties with the linear S [Formula: see text] I [Formula: see text] I [Formula: see text] I [Formula: see text] D-N model confirm that the suspicious, infected, and dead casualties converge to zero from 200000, 1400, 200 casualties, respectively. The convergences occur in 120 days under the current conditions. |
format | Online Article Text |
id | pubmed-8349905 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | ISA. Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83499052021-08-09 Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks Tutsoy, Onder Polat, Adem ISA Trans Article Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein, to cope with these urgent healthcare demands, the state authorities seek ways to develop policies based on the estimated future casualties. These policies are mainly non-pharmacological policies including the restrictions, curfews, closures, and lockdowns. In this paper, we construct three model structures of the S [Formula: see text] I [Formula: see text] I [Formula: see text] I [Formula: see text] D-N (suspicious S [Formula: see text] , infected I [Formula: see text] , intensive care I [Formula: see text] , intubated I [Formula: see text] , and dead D together with the non-pharmacological policies N) holding two key targets. The first one is to predict the future COVID-19 casualties including the intensive care and intubated ones, which directly determine the need for urgent healthcare facilities, and the second one is to analyse the linear and non-linear dynamics of the COVID-19 pandemic under the non-pharmacological policies. In this respect, we have modified the non-pharmacological policies and incorporated them within the models whose parameters are learned from the available data. The trained models with the data released by the Turkish Health Ministry confirmed that the linear S [Formula: see text] I [Formula: see text] I [Formula: see text] I [Formula: see text] D-N model yields more accurate results under the imposed non-pharmacological policies. It is important to note that the non-pharmacological policies have a damping effect on the pandemic casualties and this can dominate the non-linear dynamics. Herein, a model without pharmacological or non-pharmacological policies might have more dominant non-linear dynamics. In addition, the paper considers two machine learning approaches to optimize the unknown parameters of the constructed models. The results show that the recursive neural network has superior performance for learning nonlinear dynamics. However, the batch least squares outperforms in the presence of linear dynamics and stochastic data. The estimated future pandemic casualties with the linear S [Formula: see text] I [Formula: see text] I [Formula: see text] I [Formula: see text] D-N model confirm that the suspicious, infected, and dead casualties converge to zero from 200000, 1400, 200 casualties, respectively. The convergences occur in 120 days under the current conditions. ISA. Published by Elsevier Ltd. 2022-05 2021-08-09 /pmc/articles/PMC8349905/ /pubmed/34412892 http://dx.doi.org/10.1016/j.isatra.2021.08.008 Text en © 2021 ISA. Published by 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 | Article Tutsoy, Onder Polat, Adem Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks |
title | Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks |
title_full | Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks |
title_fullStr | Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks |
title_full_unstemmed | Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks |
title_short | Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks |
title_sort | linear and non-linear dynamics of the epidemics: system identification based parametric prediction models for the pandemic outbreaks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349905/ https://www.ncbi.nlm.nih.gov/pubmed/34412892 http://dx.doi.org/10.1016/j.isatra.2021.08.008 |
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