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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.
  • Liao Z; School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Song Y; School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Ren S; School of Computer Science and Engineering, Central South University, Changsha 410083, China. Electronic address: Rsb@csu.edu.cn.
  • Song X; School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Fan X; Hunan University of Finance and Economics, Changsha 410083, China.
  • Liao Z; Nuffield Health Research Group, Nuffield Health, Ashley Avenue, Epsom, Surrey KT18 5AL, UK. Electronic address: zhining.liao@nuffieldhealth.com.
Comput Methods Programs Biomed ; 224: 106981, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1914265
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.

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Diagnostic study / Prognostic study / Risk factors Topics: Long Covid / Variants Limits: Humans Country/Region as subject: Asia Language: English Journal: Comput Methods Programs Biomed Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: J.cmpb.2022.106981

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Diagnostic study / Prognostic study / Risk factors Topics: Long Covid / Variants Limits: Humans Country/Region as subject: Asia Language: English Journal: Comput Methods Programs Biomed Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: J.cmpb.2022.106981