A new grey quadratic polynomial model and its application in the COVID-19 in China

This paper develops a new grey prediction model with quadratic polynomial term. Analytical expressions of the time response function and the restored values of the new model are derived by using grey model technique and mathematical tools. With observations of the confirmed cases, the death cases an...

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Autores principales: Zhang, Jianbo, Jiang, Zeyou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206087/
https://www.ncbi.nlm.nih.gov/pubmed/34131231
http://dx.doi.org/10.1038/s41598-021-91970-1
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author Zhang, Jianbo
Jiang, Zeyou
author_facet Zhang, Jianbo
Jiang, Zeyou
author_sort Zhang, Jianbo
collection PubMed
description This paper develops a new grey prediction model with quadratic polynomial term. Analytical expressions of the time response function and the restored values of the new model are derived by using grey model technique and mathematical tools. With observations of the confirmed cases, the death cases and the recovered cases from COVID-19 in China at the early stage, the proposed forecasting model is developed. The computational results demonstrate that the new model has higher precision than the other existing prediction models, which show the grey model has high accuracy in the forecasting of COVID-19.
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spelling pubmed-82060872021-06-16 A new grey quadratic polynomial model and its application in the COVID-19 in China Zhang, Jianbo Jiang, Zeyou Sci Rep Article This paper develops a new grey prediction model with quadratic polynomial term. Analytical expressions of the time response function and the restored values of the new model are derived by using grey model technique and mathematical tools. With observations of the confirmed cases, the death cases and the recovered cases from COVID-19 in China at the early stage, the proposed forecasting model is developed. The computational results demonstrate that the new model has higher precision than the other existing prediction models, which show the grey model has high accuracy in the forecasting of COVID-19. Nature Publishing Group UK 2021-06-15 /pmc/articles/PMC8206087/ /pubmed/34131231 http://dx.doi.org/10.1038/s41598-021-91970-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Article
Zhang, Jianbo
Jiang, Zeyou
A new grey quadratic polynomial model and its application in the COVID-19 in China
title A new grey quadratic polynomial model and its application in the COVID-19 in China
title_full A new grey quadratic polynomial model and its application in the COVID-19 in China
title_fullStr A new grey quadratic polynomial model and its application in the COVID-19 in China
title_full_unstemmed A new grey quadratic polynomial model and its application in the COVID-19 in China
title_short A new grey quadratic polynomial model and its application in the COVID-19 in China
title_sort new grey quadratic polynomial model and its application in the covid-19 in china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206087/
https://www.ncbi.nlm.nih.gov/pubmed/34131231
http://dx.doi.org/10.1038/s41598-021-91970-1
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