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Predictive Analysis of COVID-19 Based on Bidirectional LSTM Model

DOI: 10.12677/HJDM.2023.131005, PP. 46-54

Keywords: 预测,COVID-19,时间序列,神经网络,Forecast, COVID-19, Time Series, Neural Network

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At the beginning of 2020, the rapid outbreak of the COVID-19 epidemic caused many countries to take many measures to control the spread of the epidemic. At the same time, the emergence of the epidemic had a great impact on the medical systems and economies of various countries. Therefore, the estimation and prediction of epidemic information has important reference value for the government and enterprises to formulate public health prevention and control measures. For this fast-spreading highly pathogenic infectious disease, the delay in information acquisition will lead to more serious consequences. Therefore, it is proposed to apply the ARIMA model and LSTM model to the epidemic data, and count asymptomatic infected patients and symptomatic infected patients as new cases. Based on the data from 2020 to 2022, we predict the number of new confirmed cases in the United States every day in the short term. A bidirectional LSTM was introduced into the model, and the mean square error (MSE) and mean absolute error (MAE) were used to evaluate the prediction accuracy of the model under different parameters. The results show that the predicted disease number obtained by the proposed model and parameters is closer to the actual disease number, and better prediction data are obtained.


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