Application of ARIMA model in the prediction of syphilis incidence in Anhui Province
10.3969/j.issn.1006-2483.2020.06.005
- VernacularTitle:ARIMA模型在安徽省梅毒发病预测中的应用
- Author:
Lanlan FANG
1
;
Guixia PAN
1
Author Information
1. Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032,China
- Publication Type:Journal Article
- Keywords:
Syphilis;
ARIMA model;
R software;
Prediction
- From:
Journal of Public Health and Preventive Medicine
2020;31(6):19-23
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the application of time series autoregressive integrated moving average (ARIMA) based on seasonal difference to predict the number of syphilis cases in Anhui Province, and to provide a reference for early warning and control of syphilis. Methods Using R 3.6.2 software, the number of syphilis cases in Anhui Province from January 2004 to December 2016 was used for model fitting, and the resulting model was used to predict the incidence from January to December 2017. The difference between the predicted value and actual observed value was compared to evaluate the prediction effect of this model fitting. Results The incidence of syphilis in Anhui Province was on the rise with obvious periodicity. ARIMA(1,1,1)(0,1,2)12 was the optimal model, with the AIC being -264.81 and the BIC being -249.99. Box-Pierce test showed that λ2 value was 1.444(P=0.963), 10.459(P=0.576), and the difference was not statistically significant (P>0.05), indicating that the residual sequence was white noise. The model accuracy effect evaluation showed that the MAE was 0.06, the RMSE was 0.09, and the MAPE was 1.00%, indicating that the model fitting effect was good. The 2017 data was used to test the effect of the model extrapolation, and the results showed MAPE=6.09%, indicating that the model extrapolation effect was good. The actual value fell within 95% confidence interval of the predicted value, and the model prediction effect was relatively ideal. Conclusion The ARIMA(1,1,1)(0,1,2)12 model could better fit and predict the number of syphilis cases in Anhui Province, which may provide a theoretical basis for early warning, prevention and control of syphilis.
