Abstract: In order to improve the prediction accuracy of the epidemic trend of new infectious diseases， this paper improves the traditional SEIR model and proposes the Logistic-GF-SEIR model. Firstly， based on historical data， the Logistic model is used to fit the cumulative rehabilitation， and the daily rehabilitation rate， infection rate and contact rate are inverted. Secondly， Gaussian model and Logistic model are used to fit the optimal time-varying parameters. Finally， the initial value of the model is initialized to predict the trend of epidemic population. Taking the epidemic development trend of Wuhan and Japan in the early stage of COVID-19 outbreak as an example， the simulation test is carried out and compared with Logistic， SEIR， ARIMA， BP neural network and other prediction models. The results show that the fitting and prediction performance of the Logistic-GF-SEIR model is better than other models in the prediction of the epidemic situation in Wuhan， and the root mean square error is better than other models in the prediction of the epidemic situation in Japan， which verifies the feasibility， effectiveness and robustness of the proposed model. It can provide a basis for China to formulate prevention and control policies for similar infectious diseases.

Key words: forecast of epidemic trend, Logistic model, Gaussian model, SEIR model, time-varying parameters