Prediction of Incidence Trend of Influenza-Like Illness in Wuhan Based on ARIMA Model

OBJECTIVE: The autoregressive integrated moving average (ARIMA) model has been widely used to predict the trend of infectious diseases. This paper is aimed at analyzing the application of the ARIMA model in the prediction of the incidence trend of influenza-like illness (ILI) in Wuhan and providing a scientific basis for the prediction and prevention of influenza. METHODS: The weekly ILI data of two influenza surveillance sentinel hospitals in Wuhan City published on the website of the National Influenza Center of China were collected, and the ARIMA model was used to model the data from 2014 to 2020, to predict and verify the ILI data in 2021. RESULTS: The optimal model for the incidence trend of ILI in Wuhan was ARIMA (1, 1, 1), the residuals were in line with the white noise sequence (0.018 < Ljung‐Box Q < 30.695, P > 0.05), and the relative error between the predicted value and the actual value was small, which all proved the model was practical. CONCLUSION: ARIMA (1, 1, 1) can effectively simulate the short-term incidence trend of ILI in Wuhan.