Chines Journal of Vector Biology and Control ›› 2017, Vol. 28 ›› Issue (3): 265-268.DOI: 10.11853/j.issn.1003.8280.2017.03.018

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Application of autoregressive integrated moving average (ARIMA) model in information system for rodent surveillance in Hebei province

GAO Wen, HUANG Gang, MA Li-hua, WANG Xi-ming, HAN Xiao-li   

  1. Hebei Center for Disease Control and Prevention, Shijiazhuang 050021, Hebei Province, China
  • Received:2017-02-14 Online:2017-06-20 Published:2017-06-20


高文, 黄钢, 马丽华, 王喜明, 韩晓莉   

  1. 河北省疾病预防控制中心有害生物防制所, 石家庄 050021
  • 通讯作者: 黄钢,
  • 作者简介:高文,女,医师,主要从事病媒生物防制工作,

Abstract: Objective To evaluate the application of the autoregressive integrated moving average (ARIMA) model in the prediction of monthly rodent density. Methods The database of monthly rodent density in Hebei province from 2008 to 2014 was constructed with SPSS 21.0 software. A mathematic model was constructed using ARIMA model of 21.0 and used to predict the situation in 2015. Results The main rodents were Rattus norvegicus and Mus musculus in Hebei province. The rodent density presented seasonal periodicity during 2008 to 2014 in Hebei province and the seasonal distribution of R. norvegicus and M. musculus was the same. ARIMA (3, 1, 0)×(0, 1, 1)12 model best fitted the incidence of rodent density from January 2008 to December 2014. The actual average of rodent density in 2015 fell within the 95% confidence interval of prediction. Conclusion ARIMA model fits well in the prediction of rodent density, and can be applied to the information system of vector surveillance to predict the unusual rodent density.

Key words: Rodent density, Autoregressive integrated moving average model, Prediction, Warning

摘要: 目的 探讨差分自回归移动平均(ARIMA)模型在鼠密度信息系统预测分析中的应用。方法 利用SPSS21.0软件对2008-2014年河北省鼠密度逐月资料进行统计,采用ARIMA模型相关模块进行建模拟合及预测分析。结果 河北省城镇主要鼠种褐家鼠和小家鼠的季节分布差异无统计学意义(χ2=19.601,P=0.051),选用ARIMA模型对鼠类监测信息进行预测,以ARIMA(3,1,0)×(0,1,1)12模型为最优,2015年各月鼠密度实际值均在预测值的95% CI范围内。结论 ARIMA模型能较好地拟合鼠密度变化趋势,可用于鼠密度的预测预警,结合当地鼠传疾病疫情,为传染病防控工作提供依据。

关键词: 鼠密度, 差分自回归移动平均模型, 预测, 预警

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