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A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network

We describe in this paper an approach for predicting the COVID-19 time series in the world using a hybrid ensemble modular neural network, which combines nonlinear autoregressive neural networks. At the level of the modular neural network, which is formed with several modules (ensembles in this case...

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Detalles Bibliográficos
Autores principales: Melin, Patricia, Monica, Julio Cesar, Sanchez, Daniela, Castillo, Oscar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7675021/
https://www.ncbi.nlm.nih.gov/pubmed/33230389
http://dx.doi.org/10.1007/s00500-020-05452-z
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author Melin, Patricia
Monica, Julio Cesar
Sanchez, Daniela
Castillo, Oscar
author_facet Melin, Patricia
Monica, Julio Cesar
Sanchez, Daniela
Castillo, Oscar
author_sort Melin, Patricia
collection PubMed
description We describe in this paper an approach for predicting the COVID-19 time series in the world using a hybrid ensemble modular neural network, which combines nonlinear autoregressive neural networks. At the level of the modular neural network, which is formed with several modules (ensembles in this case), the modules are designed to be efficient predictors for each country. In this case, an integrator is used to combine the outputs of the modules, in this way achieving the goal of predicting a set of countries. At the level of the ensembles, forming a part of the modular network, these are constituted by a set of modules, which are nonlinear autoregressive neural networks that are designed to be efficient predictors under particular conditions for each country. In each ensemble, the results of the modules are combined with an aggregator to achieve a better and improved result for the ensemble. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained that could be helpful in deciding the best strategies in dealing with this virus for countries in their fight against the coronavirus pandemic. In addition, the proposed approach could be helpful in proposing strategies for similar countries.
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spelling pubmed-76750212020-11-19 A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network Melin, Patricia Monica, Julio Cesar Sanchez, Daniela Castillo, Oscar Soft comput Focus We describe in this paper an approach for predicting the COVID-19 time series in the world using a hybrid ensemble modular neural network, which combines nonlinear autoregressive neural networks. At the level of the modular neural network, which is formed with several modules (ensembles in this case), the modules are designed to be efficient predictors for each country. In this case, an integrator is used to combine the outputs of the modules, in this way achieving the goal of predicting a set of countries. At the level of the ensembles, forming a part of the modular network, these are constituted by a set of modules, which are nonlinear autoregressive neural networks that are designed to be efficient predictors under particular conditions for each country. In each ensemble, the results of the modules are combined with an aggregator to achieve a better and improved result for the ensemble. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained that could be helpful in deciding the best strategies in dealing with this virus for countries in their fight against the coronavirus pandemic. In addition, the proposed approach could be helpful in proposing strategies for similar countries. Springer Berlin Heidelberg 2020-11-19 2023 /pmc/articles/PMC7675021/ /pubmed/33230389 http://dx.doi.org/10.1007/s00500-020-05452-z Text en © Springer-Verlag GmbH Germany, part of Springer Nature 2020 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 Focus
Melin, Patricia
Monica, Julio Cesar
Sanchez, Daniela
Castillo, Oscar
A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network
title A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network
title_full A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network
title_fullStr A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network
title_full_unstemmed A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network
title_short A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network
title_sort new prediction approach of the covid-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network
topic Focus
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7675021/
https://www.ncbi.nlm.nih.gov/pubmed/33230389
http://dx.doi.org/10.1007/s00500-020-05452-z
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