Epidemiological characteristics and prediction of incidence trend of hepatitis E in Huai'an City in 2015-2024
10.3969/j.issn.1006-2483.2026.04.006
- VernacularTitle:2015—2024年淮安市戊肝流行病学特征分析及发病趋势预测
- Author:
Wenling XIA
1
;
Hongbo ZHANG
2
;
Yang LI
3
;
Ben CAI
1
;
Wenyi ZHANG
1
;
Chunyu WAN
1
;
Qiang GAO
1
Author Information
1. Huai'an Center for Disease Control and Prevention, Huai'an, Jiangsu 223001, China
2. Lianshui County Center for Disease Control and Prevention, Lianshui, Jiangsu 223400, China
3. Xuyi County Center for Disease Control and Prevention, Xuyi, Jiangsu 223400, China
- Publication Type:Journal Article
- Keywords:
Hepatitis E;
Epidemiological characteristics;
Incidence trend;
SARIMA model
- From:
Journal of Public Health and Preventive Medicine
2026;37(4):26-30
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the epidemiological characteristics of hepatitis E (HE) in Huai'an City from 2015 to 2024, and construct and validate the prediction effect of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, and to provide a scientific basis for HE prevention and control. Methods Data of reported HE cases in Huai'an City from January 2015 to December 2024 were extracted from the National Disease Control and Prevention Information System. Descriptive epidemiological methods were used to analyze the temporal, regional and population distribution characteristics of HE incidence. After data preprocessing, stationarity test and randomness test, the SARIMA model was constructed. The optimal model was selected by minimizing the Akaike Information Criterion (AIC). The model performance was evaluated by adjusted R-squared (Adj R2) and root mean square error (RMSE). The monthly incidence rate of HE in Huai'an City in 2025 was predicted and compared with the actual incidence data to validate the model. Results A total of 1 713 HE cases were reported in Huai'an City from 2015 to 2024, showing a fluctuating upward trend. Huaiyin District (339 cases) and Lianshui County (334 cases) were high-incidence areas. The cases were mainly concentrated in people aged 45 and above (83.3%), males (gender ratio 2.37:1) and farmers (70.8%). The optimal model was SARIMA (1,1,1)×(2,0,1)12, with residual as white noise sequence. There was no statistical difference between the actual incidence and predicted value from January to August 2025 (P>0.05). Conclusion HE incidence in Huai'an has obvious aggregation and temporal fluctuation characteristics. The SARIMA model has a good prediction effect, which can provide reference for formulating targeted prevention and control strategies.
