Abstract：Objective To evaluate the feasibility of autoregressive integrated moving average with explanatory variables ( ARIMAX ) model including meteorological factors on the prediction of influenza-like illness ( ILI ), so as to provide a basis for the monitoring and early warning of influenza. Methods The ILI data reported by four sentinel hospitals in Yuhang District of Hangzhou from the 1st week of 2014 to the 26th week of 2018 was collected, as well as the meteorological data during the same period. The ARIMAX model was established using the percentage of ILI cases in total outpatients ( ILI% ) data from the 1st week of 2014 to the 52nd week of 2017 and the meteorological factors selected by Lasso regression model. The ILI% from the 1st to 26th week of 2018 was predicted and compared with the actual values to verify the ARIMAX model. Results From the 1st week of 2014 to the 26th week of 2018, a total of 60 419 cases of ILI were reported by the four sentinel hospitals of Yuhang District, with ILI% of 1.29%. Lasso regression analysis showed that there was a positive correlation between weekly average absolute humidity and ILI% ( r=27.769 ), and a negative correlation between weekly average temperature and ILI% ( r=-0.117 ). The ARIMAX (1, 0, 0) ( 1, 0, 0 )12 with weekly average temperature and absolute humidity was selected as the optimal model, with the Bayesian information criterion (BIC) value of 81.30 and the mean absolute percentage error (MAPE) value of 15.77%. The MAPE value of the ARIMAX model predicting the ILI% from 1st to 26th week of 2018 were 43.75%. Conclusion The ARIMAX model including meteorological factors can be used to predict the prevalence of ILI, but the accuracy needs to be promoted.
