Application of seasonal ARIMA model in predicting the monthly incidence of foodborne diseases
10.3969/j.issn.1006-2483.2024.05.002
- VernacularTitle:ARIMA乘积季节模型在食源性疾病月发病数预测中的应用
- Author:
Xuepei ZHANG
1
,
2
;
Lin ZHOU
3
;
Min LIU
1
;
Aiying TENG
1
;
Yanhua LI
1
;
Wei MA
2
Author Information
1. Provincial Hospital Affiliated to Shandong First Medical University , Jinan , Shandong 250012 , China
2. School of Public Health , Shandong University , Jinan , Shandong 250012 ,China
3. Jinan Center for Disease Control and Prevention , Jinan , Shandong 250021 ,China
- Publication Type:Journal Article
- Keywords:
Foodborne diseases;
Seasonal ARIMA;
Prediction
- From:
Journal of Public Health and Preventive Medicine
2024;35(5):6-9
- CountryChina
- Language:Chinese
-
Abstract:
Objectives To explore the trend characteristics of foodborne diseases in Jinan City and apply the seasonal autoregressive integrated moving average model (SARIMA) for prediction. Methods The incidence data of foodborne diseases from two active monitoring sentinel hospitals in Jinan City from 2014 to 2020 were collected to establish a time series. The SARIMA model was used to fit the incidence situation. The numbers of cases in 2021 were compared with the predicted values to validate the model and evaluate the predictive effect. Results The SARIMA (2, 0, 1) (0, 1, 1)12 model was established and fitted the time series of food borne diseases in Jinan well, with AIC=687.22. Using Ljung Box function, P=0.499 was obtained, indicating that the residual error belonged to the white noise series. The data in 2021 was used to test the model extrapolation effect, and the actual values fell within the 95% confidence interval of the predicted value. The model prediction effect was relatively ideal. Conclusion SARIMA (2, 0, 1) (0, 1, 1)12 model can better fit the temporal change of foodborne diseases, and therefore can be used to fit and predict the monthly incidence of foodborne diseases.
