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VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants
BACKGROUND AND OBJECTIVE: The ever-mutating COVID-19 has infected billions of people worldwide and seriously affected the stability of human society and the world economic development. Therefore, it is essential to make long-term and short-term forecasts for COVID-19. However, the pandemic situation...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9242688/ https://www.ncbi.nlm.nih.gov/pubmed/35863125 http://dx.doi.org/10.1016/j.cmpb.2022.106981 |
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author | Liao, Zhifang Song, Yucheng Ren, Shengbing Song, Xiaomeng Fan, Xiaoping Liao, Zhining |
author_facet | Liao, Zhifang Song, Yucheng Ren, Shengbing Song, Xiaomeng Fan, Xiaoping Liao, Zhining |
author_sort | Liao, Zhifang |
collection | PubMed |
description | BACKGROUND AND OBJECTIVE: The ever-mutating COVID-19 has infected billions of people worldwide and seriously affected the stability of human society and the world economic development. Therefore, it is essential to make long-term and short-term forecasts for COVID-19. However, the pandemic situation in different countries and regions may be dominated by different virus variants, and the transmission capacity of different virus variants diversifies. Therefore, there is a need to develop a predictive model that can incorporate mutational information to make reasonable predictions about the current pandemic situation. METHODS: This paper proposes a deep learning prediction framework, VOC-DL, based on Variants Of Concern (VOC). The framework uses slope feature method to process the time series dataset containing VOC variant information, and uses VOC-LSTM, VOC-GRU and VOC-BILSTM prediction models included in the framework to predict the daily newly confirmed cases. RESULTS: We analyzed daily newly confirmed cases in Italy, South Korea, Russia, Japan and India from April 14th, 2021 to July 3rd, 2021. The experimental results show that all VOC-DL models proposed in this paper can accurately predict the pandemic trend in the medium and long term, and VOC-LSTM model has the best prediction performance, with the highest average determination coefficient R2 of 96.83% in five nations’ datasets. The overall prediction has robustness. CONCLUSIONS: The experimental results show that VOC-LSTM is the best predictor for such a series of data and has higher prediction accuracy in the long run. At the same time, our VOC-DL framework combining VOC variants has reference significance for predicting other variants in the future. |
format | Online Article Text |
id | pubmed-9242688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92426882022-06-30 VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants Liao, Zhifang Song, Yucheng Ren, Shengbing Song, Xiaomeng Fan, Xiaoping Liao, Zhining Comput Methods Programs Biomed Article BACKGROUND AND OBJECTIVE: The ever-mutating COVID-19 has infected billions of people worldwide and seriously affected the stability of human society and the world economic development. Therefore, it is essential to make long-term and short-term forecasts for COVID-19. However, the pandemic situation in different countries and regions may be dominated by different virus variants, and the transmission capacity of different virus variants diversifies. Therefore, there is a need to develop a predictive model that can incorporate mutational information to make reasonable predictions about the current pandemic situation. METHODS: This paper proposes a deep learning prediction framework, VOC-DL, based on Variants Of Concern (VOC). The framework uses slope feature method to process the time series dataset containing VOC variant information, and uses VOC-LSTM, VOC-GRU and VOC-BILSTM prediction models included in the framework to predict the daily newly confirmed cases. RESULTS: We analyzed daily newly confirmed cases in Italy, South Korea, Russia, Japan and India from April 14th, 2021 to July 3rd, 2021. The experimental results show that all VOC-DL models proposed in this paper can accurately predict the pandemic trend in the medium and long term, and VOC-LSTM model has the best prediction performance, with the highest average determination coefficient R2 of 96.83% in five nations’ datasets. The overall prediction has robustness. CONCLUSIONS: The experimental results show that VOC-LSTM is the best predictor for such a series of data and has higher prediction accuracy in the long run. At the same time, our VOC-DL framework combining VOC variants has reference significance for predicting other variants in the future. Published by Elsevier B.V. 2022-09 2022-06-30 /pmc/articles/PMC9242688/ /pubmed/35863125 http://dx.doi.org/10.1016/j.cmpb.2022.106981 Text en © 2022 Published by Elsevier B.V. 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 Liao, Zhifang Song, Yucheng Ren, Shengbing Song, Xiaomeng Fan, Xiaoping Liao, Zhining VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants |
title | VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants |
title_full | VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants |
title_fullStr | VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants |
title_full_unstemmed | VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants |
title_short | VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants |
title_sort | voc-dl: deep learning prediction model for covid-19 based on voc virus variants |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9242688/ https://www.ncbi.nlm.nih.gov/pubmed/35863125 http://dx.doi.org/10.1016/j.cmpb.2022.106981 |
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