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The dynamics of entropy in the COVID-19 outbreaks

With the unfolding of the COVID-19 pandemic, mathematical modelling of epidemics has been perceived and used as a central element in understanding, predicting, and governing the pandemic event. However, soon it became clear that long-term predictions were extremely challenging to address. In additio...

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Autores principales: Wang, Ziqi, Broccardo, Marco, Mignan, Arnaud, Sornette, Didier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480665/
https://www.ncbi.nlm.nih.gov/pubmed/32929304
http://dx.doi.org/10.1007/s11071-020-05871-5
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author Wang, Ziqi
Broccardo, Marco
Mignan, Arnaud
Sornette, Didier
author_facet Wang, Ziqi
Broccardo, Marco
Mignan, Arnaud
Sornette, Didier
author_sort Wang, Ziqi
collection PubMed
description With the unfolding of the COVID-19 pandemic, mathematical modelling of epidemics has been perceived and used as a central element in understanding, predicting, and governing the pandemic event. However, soon it became clear that long-term predictions were extremely challenging to address. In addition, it is still unclear which metric shall be used for a global description of the evolution of the outbreaks. Yet a robust modelling of pandemic dynamics and a consistent choice of the transmission metric is crucial for an in-depth understanding of the macroscopic phenomenology and better-informed mitigation strategies. In this study, we propose a Markovian stochastic framework designed for describing the evolution of entropy during the COVID-19 pandemic together with the instantaneous reproductive ratio. Then, we introduce and use entropy-based metrics of global transmission to measure the impact and the temporal evolution of a pandemic event. In the formulation of the model, the temporal evolution of the outbreak is modelled by an equation governing the probability distribution that describes a nonlinear Markov process of a statistically averaged individual, leading to a clear physical interpretation. The time-dependent parameters are formulated by adaptive basis functions, leading to a parsimonious representation. In addition, we provide a full Bayesian inversion scheme for calibration together with a coherent strategy to address data unreliability. The time evolution of the entropy rate, the absolute change in the system entropy, and the instantaneous reproductive ratio are natural and transparent outputs of this framework. The framework has the appealing property of being applicable to any compartmental epidemic model. As an illustration, we apply the proposed approach to a simple modification of the susceptible–exposed–infected–removed model. Applying the model to the Hubei region, South Korean, Italian, Spanish, German, and French COVID-19 datasets, we discover significant difference in the absolute change of entropy but highly regular trends for both the entropy evolution and the instantaneous reproductive ratio.
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spelling pubmed-74806652020-09-10 The dynamics of entropy in the COVID-19 outbreaks Wang, Ziqi Broccardo, Marco Mignan, Arnaud Sornette, Didier Nonlinear Dyn Original Paper With the unfolding of the COVID-19 pandemic, mathematical modelling of epidemics has been perceived and used as a central element in understanding, predicting, and governing the pandemic event. However, soon it became clear that long-term predictions were extremely challenging to address. In addition, it is still unclear which metric shall be used for a global description of the evolution of the outbreaks. Yet a robust modelling of pandemic dynamics and a consistent choice of the transmission metric is crucial for an in-depth understanding of the macroscopic phenomenology and better-informed mitigation strategies. In this study, we propose a Markovian stochastic framework designed for describing the evolution of entropy during the COVID-19 pandemic together with the instantaneous reproductive ratio. Then, we introduce and use entropy-based metrics of global transmission to measure the impact and the temporal evolution of a pandemic event. In the formulation of the model, the temporal evolution of the outbreak is modelled by an equation governing the probability distribution that describes a nonlinear Markov process of a statistically averaged individual, leading to a clear physical interpretation. The time-dependent parameters are formulated by adaptive basis functions, leading to a parsimonious representation. In addition, we provide a full Bayesian inversion scheme for calibration together with a coherent strategy to address data unreliability. The time evolution of the entropy rate, the absolute change in the system entropy, and the instantaneous reproductive ratio are natural and transparent outputs of this framework. The framework has the appealing property of being applicable to any compartmental epidemic model. As an illustration, we apply the proposed approach to a simple modification of the susceptible–exposed–infected–removed model. Applying the model to the Hubei region, South Korean, Italian, Spanish, German, and French COVID-19 datasets, we discover significant difference in the absolute change of entropy but highly regular trends for both the entropy evolution and the instantaneous reproductive ratio. Springer Netherlands 2020-09-09 2020 /pmc/articles/PMC7480665/ /pubmed/32929304 http://dx.doi.org/10.1007/s11071-020-05871-5 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Paper
Wang, Ziqi
Broccardo, Marco
Mignan, Arnaud
Sornette, Didier
The dynamics of entropy in the COVID-19 outbreaks
title The dynamics of entropy in the COVID-19 outbreaks
title_full The dynamics of entropy in the COVID-19 outbreaks
title_fullStr The dynamics of entropy in the COVID-19 outbreaks
title_full_unstemmed The dynamics of entropy in the COVID-19 outbreaks
title_short The dynamics of entropy in the COVID-19 outbreaks
title_sort dynamics of entropy in the covid-19 outbreaks
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480665/
https://www.ncbi.nlm.nih.gov/pubmed/32929304
http://dx.doi.org/10.1007/s11071-020-05871-5
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