Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control

This special issue contains 35 regular articles on the analysis and dynamics of COVID-19 with several applications. Some analyses are on the construction of mathematical models representing the dynamics of COVID-19, and some are on the estimations and predictions of the disease, a few with possible...

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Autor principal: Banerjee, Santo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Editorial
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9716540/
https://www.ncbi.nlm.nih.gov/pubmed/36475056
http://dx.doi.org/10.1140/epjs/s11734-022-00724-1
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author Banerjee, Santo
author_facet Banerjee, Santo
author_sort Banerjee, Santo
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description This special issue contains 35 regular articles on the analysis and dynamics of COVID-19 with several applications. Some analyses are on the construction of mathematical models representing the dynamics of COVID-19, and some are on the estimations and predictions of the disease, a few with possible applications. The various contributions report important, timely, and promising results, such as the effects of several waves, deep learning-based COVID-19 classifications, and multivariate time series with applications.
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spelling pubmed-97165402022-12-02 Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control Banerjee, Santo Eur Phys J Spec Top Editorial This special issue contains 35 regular articles on the analysis and dynamics of COVID-19 with several applications. Some analyses are on the construction of mathematical models representing the dynamics of COVID-19, and some are on the estimations and predictions of the disease, a few with possible applications. The various contributions report important, timely, and promising results, such as the effects of several waves, deep learning-based COVID-19 classifications, and multivariate time series with applications. Springer Berlin Heidelberg 2022-12-02 2022 /pmc/articles/PMC9716540/ /pubmed/36475056 http://dx.doi.org/10.1140/epjs/s11734-022-00724-1 Text en © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022 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 Editorial
Banerjee, Santo
Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control
title Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control
title_full Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control
title_fullStr Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control
title_full_unstemmed Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control
title_short Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control
title_sort dynamics of the covid-19 pandemic: nonlinear approaches on the modelling, prediction and control
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9716540/
https://www.ncbi.nlm.nih.gov/pubmed/36475056
http://dx.doi.org/10.1140/epjs/s11734-022-00724-1
work_keys_str_mv AT banerjeesanto dynamicsofthecovid19pandemicnonlinearapproachesonthemodellingpredictionandcontrol

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