Time series analysis and prediction model of percentage of influenza-like illness （ILI） cases in Shanghai

Objective To predict the incidence trend of influenza-like illness proportion （ILI%） in Shanghai using the seasonal autoregressive integrated moving average model （SARIMA）， and to provide an important reference for timely prevention and control measures.

Methods Time series analysis was performed on ILI% surveillance data of Shanghai Municipal Center for Disease Control and Prevention from the 15^th week of 2015 to the 52^nd week of 2019， and a prediction model was established. Seasonal autoregressive integrated moving average （SARIMA） model was established using data from the foregoing 212 weeks， and prediction effect of the model was evaluated using data from the latter 36 weeks.

Results From the 15^th week of 2015 to the 52^nd week of 2019， the average ILI% in Shanghai was 1.494%， showing an obvious epidemic peak. SARIMA（1，0，0） （2，0，0） ₅₂ was finally modeled. The residual of the model was white noise sequence， and the true values were all within the 95% confidence interval of the predicted values.

Conclusion SARIMA（1，0，0） （2，0，0）₅₂ can be used for the medium term prediction of ILI% in Shanghai， and can play an early warning role for the epidemic and outbreak of influenza in Shanghai.