乌鲁木齐市流感样病例与气象因素的ARIMAX模型预测分析

目的分析气象因素对乌鲁木齐市流感样病例(influenza-like illness,ILI)例数的影响,建立ARIMAX(autoregressive integrated moving average model-X)模型对ILI例数进行短期预测,为乌鲁木齐市流感的预防与控制提供理论依据。方法利用乌鲁木齐市2015年1月至2017年9月的ILI例数和同期气象数据,建立ARIMAX模型,预测乌鲁木齐市2017年10月至2018年3月的ILI病例数,并与实际ILI例数进行比较。结果2015年1月—2017年9月发病数建立了ARIMA(0,1,1)(1,1,0)12模型,AIC=200.09;通过残差序列互相关函数(CCF)得出月平均相对湿度与ILI例数之间存在正相关关系,月日照时数与ILI例数之间存在负相关关系。将月平均相对湿度和月日照时数作为影响变量,建立ARIMAX模型,其中,纳入滞后0阶月日照时数的ARIMAX模型的AIC最小(AIC=197.63),且模型各参数差异均具有统计学意义。与一元时间序列ARIMA模型相比,拟合的平均绝对百分误差(MAPE)降低1.3687%,预测的MAPE降低5.25%,预测精度提高。结论本研究建立的带有气象因素的ARIMAX模型能较好预测短时间内ILI病例数发病趋势,为流感监测和预防控制提供依据。

Prediction and analysis of influenza-like illness and meteorological factors by ARIMAX model in Urumqi

Objective To analyze the influence of meteorological factors on the number of influenza-like illness(ILI)cases in Urumqi,and establish an ARIMAX(autoregressive integrated moving average model-X)model to make short-term prediction of the number of ILI cases,so as to provide theoretical basis for the prevention and control of influenza in Urumqi.Methods The number of ILI cases in Urumqi from January 2015 to September 2017 and meteorological data of the same period were used to establish ARIMAX model and predict the number of ILI cases in Urumqi from October 2017 to March 2018.Results The ARIMA(0,1,1)(1,1,0)12 model was established from January 2015 to September 2017,AIC=200.09.According to residual correlation function(CCF),there was a positive correlation between monthly average relative humidity and ILI cases,and a negative correlation between monthly sunshine hours and ILI cases.The average monthly relative humidity and monthly sunshine hours were taken as influencing variables to establish the ARIMAX model.Among them,the ARIMAX model incorporating the lagging order of 0 of monthly sunshine hours had the smallest AIC(AIC=197.63),and all parameters of the model were statistically significant.Compared with the univariate time series ARIMA model,the mean absolute percentage error(MAPE)of fitting was reduced by 1.3687%,the predicted MAPE was reduced by 5.25%,and the prediction accuracy was improved.Conclusion The ARIMAX model with meteorological factors established in this study can better predict the incidence trend of ILI cases in a short time,providing evidence for influenza surveillance and prevention and control.