Prediction and analysis of influenza-like illness and meteorological factors by ARIMAX model in Urumqi
10.3969/j.issn.1006-2483.2020.02.002
- VernacularTitle:乌鲁木齐市流感样病例与气象因素的ARIMA模型预测分析
- Author:
Fengyun GONG
1
,
2
;
Kai WANG
1
;
Xucheng FAN
3
;
Jiandong YANG
4
Author Information
1. College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011,China
2. School of Statistical and Data Science, Xinjiang University of Finan-ce and Economics, Urumqi 830012, China
3. Infectious Diseases Control and Prevention Department, Center for Disease Control and Prevention of Urumqi, Urumqi 830026, China
4. Tuberculosis Control and Prevention Department, Center for Disease Control and Prevention of Urumqi, Urumqi 830026, China
- Publication Type:Journal Article
- Keywords:
Influenza-like illness;
Meteorological factors;
ARIMAX model;
Prediction
- From:
Journal of Public Health and Preventive Medicine
2020;31(2):4-8
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the influence of meteorological factors on the number of influenza-like illness (ILI) cases in Urumqi, Xinjiang, 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.
