The construction and application of ARIMA model for predicting the hospital-visiting percentage of adult influenza-like illness in Jing-an District of Shanghai

ObjectiveTo explore the feasibility of constructing and applying the autoregressive integrated moving average(ARIMA)model for predicting the hospital-visiting percentage of adult influenza-like illness (ILI) in Jing-an District, Shanghai.

MethodsAn optimal ARIMA model for predicting the hospital-visiting percentage of adult ILI was established based on the monthly hospital-visiting percentage of adult ILI in Jing-an District of Shanghai from 2011 to 2014. The parameters of the model were determined through non-conditional least square method, the structure thereof was determined according to the concision principle and residual non-relevance principle, and the goodness of fit thereof was determined in accordance with Schwarz Bayesian Criterion(BSC) and Akaike Information Criterion (AIC). This model was applied to predict the monthly hospital-visiting percentage of adult ILI in Jing-an District from January to October of 2015 and to calculate the relative error between the actual value and the predicted one; it was also used to predict the monthly hospital-visiting percentage of adult ILI in Jing-an District in 2016.

ResultsThe ARIMA model (0, 2, 1)(1, 1, 0)₁₂ (without constants) could well fit the time series of the hospital-visiting percentage of adult ILI while both the moving average coefficient (MA1=0.944) and the seasonal autoregressive coefficient (SAR1=-0.542) had statistical significance(P < 0.001) and the residual error reached white noise(P>0.05). The mathematic expression of the model was (1+0.542B) (1-B)² (1-B¹²)Z_t=(1-0.944B)μ_t. The predicted value for the hospital-visiting percentage of adult ILI from Jan., 2015 to Oct., 2015 was in conformity with the change trend of the actual value and the minimal relative error was only 4.45%.

ConclusionThe ARIMA model can well fit the time-change trend of the hospital-visiting percentage of adult ILI of Jing-an District and can be used to forecast the hospital-visiting percentage of adult ILI while ensuring relatively high accuracy of short-term forecasts.