VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants. (September 2022)

• Record Type: Journal Article
• Authors: Liao, Zhifang; Song, Yucheng; Ren, Shengbing; Song, Xiaomeng; Fan, Xiaoping; Liao, Zhining
• Is Part Of: Computer methods and programs in biomedicine. Volume 224(2022)
• Publication Date: 2022-09
• Subjects: COVID-19 -- VOC-DL model -- Variant -- LSTM -- Prediction -- Time series; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106981
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: There are now various variants of COVID-19 that have erupted around the world, and the ability of the different variants to spread varies. Outbreaks affected by different variants are difficult to predict. Most of the prediction models researchers propose do not combine the influence of variants well. In contrast, our innovatively proposed VOC-DL prediction framework combines the influence of variants well and can better adapt to different countries. Make accurate predictions about the effects of different variants. In order to improve the accuracy of the model's prediction of epidemic trends, we innovatively apply slope feature engineering to the preprocessing stage of variant information. The impact of the fluctuating trend of epidemic information, such as variants, on the epidemic, is accurately analyzed. We also compare with models proposed by other researchers, and the results show that our VOC-DL prediction framework outperforms existing methods on RMSE, MSE, MAPE, and R2 evaluation metrics. Most of the current forecasting models are only suitable for short-term forecasting, and our proposed forecasting framework can be used for long-term forecasting. Through experimental verification, our model is still more than 95% of the R2 evaluation index in predicting the next 28 days, which is better than the previous comparison model. 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.