Abstract: Novel coronavirus pneumonia, referred to as COVID-19, is an acute infectious pneumonia caused by novel coronavirus, which is of highly infectious and generally susceptible to the population. Therefore, the prediction of the number of novel coronavirus pneumonia infections is not only beneficial for the country to make scientific decisions in the face of the epidemic, but also facilitates the timely integration of epidemic prevention resources. In this paper, a hybrid model SEIR-ARIMA constructed by the model SEIR based on the traditional infectious disease dynamics and the differential integrated moving average autoregressive model ARIMA is proposed to make prediction and analysis of the novel coronavirus pneumonia epidemic in different time periods and locations. From the experimental results, the prediction based on the SEIR-ARIMA hybrid model has better prediction effect than the common logistic regression Logistic, long short-term memory artificial neural network LSTM, SEIR model, and ARIMA model used for COVID-19 prediction. In order to truly reflect whether the improvement of the experimental effect originates from the advantage of combining SEIR and ARIMA models, this paper also implements the SEIR-Logistic hybrid model and SEIR-LSTM hybrid model, and compares the analysis with SEIR-ARIMA to conclude that both SEIR-ARIMA predictions achieve better prediction results. Therefore, the analysis of the development trend of COVID-19 based on the SEIR-ARIMA hybrid model is relatively reliable, which is conducive to the scientific decision-making of the country in the face of the epidemic and has good application value for the prevention of other types of infectious diseases in China in the future.

Key words: COVID-19, SEIR model, ARIMA model, hybrid model, prediction