Chinese Medical Sciences Journal ›› 2017, Vol. 32 ›› Issue (3): 152-160.doi: 10.24920/J1001-9294.2017.036

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构建预测陕西省咸阳市日本脑炎疫情的季节性时间序列模型

张荣强1,2, 李凤英3, 刘军礼3, 刘美宁3, 罗文瑞3, 马婷3, 马波3, 张志刚1,*   

  1. 1 陕西中医药大学公共卫生学院,陕西,咸阳 712046
    2 西安交通大学医学部公共卫生学院地方病研究所,国家卫生和计划生育委员会微量元素与地方病重点实验室,西安 710061
    3 陕西咸阳市疾病预防控制中心免疫规划科,陕西,咸阳 712046
  • 收稿日期:2016-12-21 出版日期:2017-09-26 发布日期:2017-09-27
  • 通讯作者: 张志刚

Time Series Models for Short Term Prediction of the Incidence of Japanese Encephalitis in Xianyang City, P R China

Zhang Rong-qiang1,2, Li Feng-ying3, Liu Jun-li3, Liu Mei-ning3, Luo Wen-rui3, Ma Ting3, Ma Bo3, Zhang Zhi-gang1,*   

  1. 1School of Public Health, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi 712046, China
    2Institute of Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi’an 710061, China;
    3Department of Immunology, Center for Disease Control and Prevention of Xianyang City, Xianyang, Shaanxi 712046, China
  • Received:2016-12-21 Online:2017-09-26 Published:2017-09-27
  • Contact: Zhang Zhi-gang

摘要:

目的 通过构建季节性时间序列模型(SARIMA),预测陕西省咸阳市日本脑炎疫情,为日本脑炎的预防控制提供参考。方法 采用理论流行病学研究设计,收集2005年1月至2014年9月陕西省咸阳市日本脑炎的发病率资料,采用Box和Jenkins方法建立最佳的SARIMA模型,并对咸阳市日本脑炎的发病率进行短期预测。结果 研究结果显示:SARIMA(1, 1, 1)(2, 1, 1)12是最佳模型,该模型具有最低的贝叶斯信息标准(BIC),Akaike信息标准(AIC),平均绝对误差(MAE)值及最高的R2和较低的平均绝对百分比误差(MAPE);SARIMA(1, 1, 1)(2, 1, 1)12可对咸阳市日本脑炎发病率进行较为可靠的预测。结论 SARIMA(1, 1, 1)(2, 1, 1)12可为咸阳市早期识别日本脑炎发病率异常升高(≥0.4/100 000)提供预警;咸阳市日本脑炎的发病率将略有下降,从6月至8月达到顶峰。

关键词: 日本脑炎, 时间序列模型, 发病率, 预测

Abstract: Objective

To construct a model of Seasonal Autoregressive Integrated Moving Average (SARIMA) for forecasting the epidemic of Japanese encephalitis (JE) in Xianyang, Shaanxi, China, and provide valuable reference information for JE control and prevention.

Methods

Theoretically epidemiologic study was employed in the research process. Monthly incidence data on JE for the period from Jan 2005 to Sep 2014 were obtained from a passive surveillance system at the Center for Diseases Prevention and Control in Xianyang, Shaanxi province. An optimal SARIMA model was developed for JE incidence from 2005 to 2013 with the Box and Jenkins approach. This SARIMA model could predict JE incidence for the year 2014 and 2015.

Results

SARIMA (1, 1, 1) (2, 1, 1)12 was considered to be the best model with the lowest Bayesian information criterion, Akaike information criterion, Mean Absolute Error values, the highest R2, and a lower Mean Absolute Percent Error. SARIMA (1, 1, 1) (2, 1, 1)12 was stationary and accurate for predicting JE incidence in Xianyang. The predicted incidence, around 0.3/100 000 from June to August in 2014 with low errors, was higher compared with the actual incidence. Therefore, SARIMA (1, 1, 1) (2, 1, 1)12 appeared to be reliable and accurate and could be applied to incidence prediction.

Conclusions

The proposed prediction model could provide clues to early identification of the JE incidence that is increased abnormally (≥0.4/100 000). According to the predicted results in 2014, the JE incidence in Xianyang will decline slightly and reach its peak from June to August.

Key words: Japanese encephalitis, time series models, incidence, prediction

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