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VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants.
- Source :
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Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2022 Sep; Vol. 224, pp. 106981. Date of Electronic Publication: 2022 Jun 30. - Publication Year :
- 2022
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Abstract
- 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.<br />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.<br />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.<br />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.<br />Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interests.<br /> (Copyright © 2022. Published by Elsevier B.V.)
- Subjects :
- Forecasting
Humans
India
Pandemics
COVID-19 diagnosis
Deep Learning
Subjects
Details
- Language :
- English
- ISSN :
- 1872-7565
- Volume :
- 224
- Database :
- MEDLINE
- Journal :
- Computer methods and programs in biomedicine
- Publication Type :
- Academic Journal
- Accession number :
- 35863125
- Full Text :
- https://doi.org/10.1016/j.cmpb.2022.106981