The application of time series analysis in modeling and predicting the influenza-like illness consultation rate in Huangpu District of Shanghai

Objective To explore the feasibility for modeling and forecasting outpatient consultationrate of influenza-like illness (ILI) in Huangpu District of Shanghai. Methods ILI consultation rates from the first week of 2011 to the 52th week of 2014 were collected through the National Influenza Surveillance Information System. SAS 9.3 was used to establish an optimal ARIMA model of ILI data. The forecasting ability of the ARIMA model was afterwards evaluated by using data from the first to the sixth week in 2015. Results ARIMA(1,0,0) was identified as the final model in fitting outpatient vising rate of ILI with stable and white noise residual. Model validation showed that the real outpatient-visiting rate of ILI all fell in the 95% confidence interval of the predictive values. Conclusion ARIMA (1,0,0) could be used in the forecast of ILI consultation rates in Huangpu District of Shanghai.