COVID-19 Epidemic Prediction Based on Deep Learning (Journal of Nonlinear Modeling and Analysis, 2024)
In this paper, a multi-layer gated recurrent unit neural network(multi-head GRU) model is proposed to predict the confirmed cases of thenew crown epidemic (COVID-19). We extract the time series relationship inthe data, and the rolling prediction method is adopted to ensure the simplestructure of the model and achieve higher precision and interpretability. Theprediction results of this model are compared with the LSTM model, theTransformer model and the infectious disease model (SIR). The results showthat the proposed model has higher prediction accuracy. The mean absoluteerror (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 errorof the SIR model, LSTM model and Transformer model. For the spread ofthe COVID-19 epidemic, traditional infectious disease models and machinelearning models cannot achieve more accurate predictions. In this paper, weuse a GRU model to predict the real-time spread of COVID-19, which hasfewer parameters so that it can reduce the risk of overfitting to train faster.Meanwhile, it can compensate for the transformer model’s shortcomings tocapture local features.