VIRUS PROPAGATION PREDICTION BASED ON LSTM-SELF-ATTENTION

COVID-19 presents different development trends due to different climate, government policies and vaccination population in different countries, which leads to the instability of COVID-19 data. The traditional mechanism model cannot make accurate prediction based on historical time series data. Therefore, this paper proposes an improved model with self-attention mechanism in the framework of deep learning LSTM network. Through simulation experiments, the existing data of COVID-19 in China, Britain and Italy were predicted, and the prediction results were compared with those of SIS model, LSTM model and ConvLSTM model with nonlinear infection rate. Experiments show that LSTM Self-Attention model has higher prediction accuracy than the other three models.