VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants.
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Deep Learning
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
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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