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COVID-19 Epidemic Prediction Based on Deep Learning
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KeyWord:COVID-19, deep learning, time series forecasting, gated recurrent unit neural network
Author NameAffiliation
Rui Li School of Economics and Management, Tongji University, Shanghai 200092, China 
Zhihan Zhang Minhang Crosspoint Academy at Shanghai Wenqi Middle School, Shanghai 200240, China 
Peng Liu School of Economics and Management, Tongji University, Shanghai 200092, China 
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      In this paper, a multi-layer gated recurrent unit neural network (multi-head GRU) model is proposed to predict the confirmed cases of the new crown epidemic (COVID-19). We extract the time series relationship in the data, and the rolling prediction method is adopted to ensure the simple structure of the model and achieve higher precision and interpretability. The prediction results of this model are compared with the LSTM model, the Transformer model and the infectious disease model (SIR). The results show that the proposed model has higher prediction accuracy. The mean absolute error (MAE) of epidemic prediction in most countries (the United States, Brazil, India, the United Kingdom and Russia) is respectively 197.52, 68.02, 200.67, 24.78 and 123.50, which is much smaller than the prediction error of the SIR model, LSTM model and Transformer model. For the spread of the COVID-19 epidemic, traditional infectious disease models and machine learning models cannot achieve more accurate predictions. In this paper, we use a GRU model to predict the real-time spread of COVID-19, which has fewer parameters and reduces the risk of overfitting to train faster. Meanwhile, it can make up for the shortcoming of the transformer model to capture local features.