Population density prediction of Adeds albopictus in Guangzhou based on autoregressive integrated moving average model

doi:10.11853/j.issn.1003.8280.2018.06.001

Chines Journal of Vector Biology and Control ›› 2018, Vol. 29 ›› Issue (6): 545-549.DOI: 10.11853/j.issn.1003.8280.2018.06.001

Population density prediction of Adeds albopictus in Guangzhou based on autoregressive integrated moving average model

PAN Yan-yu, WU Hai-xia, GUO Jia, LIU Qi-yong

State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, Beijing 102206, China

Received:2018-06-22 Online:2018-12-20 Published:2018-12-20
Supported by:
Supported by the National Key Research and Development Plan (No. 2016YFC1200802) and the National Basic Research Program of China (No. 2012CB955504)

基于R语言自回归积分移动平均模型的广州市白纹伊蚊密度预测研究

潘衍宇, 吴海霞, 国佳, 刘起勇

中国疾病预防控制中心传染病预防控制所, 传染病预防控制国家重点实验室, 感染性疾病诊治协同创新中心, 世界卫生组织媒介生物监测与管理合作中心, 北京 102206

通讯作者: 刘起勇,Email:liuqiyong@icdc.cn
作者简介:潘衍宇,男,在读硕士,从事分子流行病学研究,Email:934955946@qq.com
基金资助:
国家重点研发计划（2016YFC1200802）；国家重大科学研究计划（2012CB955504）

Abstract

Abstract: Objective To construct the autoregressive integrated moving average (ARIMA) model to predict by summarizing the density data of Aedes albopictus in Guangzhou. Methods Through the R programming language 3.4.4, the model was constituted by density of Ae. albopictus from January 2009 to June 2017, proceeded significance test of model and parameter, and evaluated the model by overall data. the predicted value and the real value from July to December 2017 were compared to evaluate the extrapolation effect. Results ARIMA (0, 1, 1) (0, 1, 1)₁₂ has been constituted with AIC=-268.83 and R²=0.427. Residual sequence was proved white noise (P>0.05) and homoscedasticity. The predicted value and the real value from July to December 2017 are approximately in agreement, showing the Root Mean Square Error (RMSE)=0.087 4 and the Mean Absolute Error (MAE)=0.028 3. Good data fit was demonstrated. Conclusion The model can well predict the density data of Ae. albopictus in Guangzhou.

Key words: Time series analysis, Autoregressive integrated moving average model, Aedes albopictus, Prediction

摘要： 目的构建广州市白纹伊蚊密度自回归积分移动平均模型（ARIMA）并进行预测。方法应用R语言3.4.4将2009年1月至2017年5月的白纹伊蚊月密度数据构建ARIMA模型，进行整体回代评价拟合效果，比较2017年6-12月预测值与真实值，评价外推效果，对2018年白纹伊蚊密度进行预测。结果白纹伊蚊密度监测数据构建ARIMA（0，1，1）（0，1，1）₁₂模型，赤池信息准则（AIC）=-268.83，平稳R²=0.427；残差序列为白噪声（P>0.05），且方差齐性，证明模型有效；2017年6-12月预测值与实际值基本一致，均方根误差（RMSE）=0.087 4，平均绝对误差（MAE）=0.028 3，模型外推良好。结论 ARIMA模型能够较好地预测广州市白纹伊蚊密度消长趋势。

关键词: 时间序列, 自回归积分移动平均模型, 白纹伊蚊, 预测

CLC Number:

R384.1

PAN Yan-yu, WU Hai-xia, GUO Jia, LIU Qi-yong. Population density prediction of Adeds albopictus in Guangzhou based on autoregressive integrated moving average model[J]. Chines Journal of Vector Biology and Control, 2018, 29(6): 545-549.

潘衍宇, 吴海霞, 国佳, 刘起勇. 基于R语言自回归积分移动平均模型的广州市白纹伊蚊密度预测研究[J]. 中国媒介生物学及控制杂志, 2018, 29(6): 545-549.

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Population density prediction of Adeds albopictus in Guangzhou based on autoregressive integrated moving average model

基于R语言自回归积分移动平均模型的广州市白纹伊蚊密度预测研究

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