摘要
目的探讨应用时间序列SARIMA模型预测北京市手足口病发病情况。方法利用中国疾病预防控制信息系统中北京市2010年至2017年手足口病报告发病率资料，使用SPSS19．0统计软件建立SARIMA模型，对2018年的发病率进行外推预测。结果北京市手足口病每年4月至8月为发病高峰，9月至10月为次高峰，2010年至2017年的年均报告发病率为166．83／10万。SARIMA（1，0，1）（0，1，1），：模型能较好地拟合既往时间段手足口病发病率，平稳R2为0．705，MAPE为33．217，BIC为2．862，Ljung—Box检验显示残差为白噪声。应用此模型预计2018年手足口病总发病率为120．19／10万，高于2017年，尤其是4月至8月的发病高峰，较2017年同期有明显升高。结论SARIMA模型能较好地拟合北京市手足口病发病率数据，可为提前制定防控策略提供科学依据。
Objective To discuss the prediction method for the incidence of hand, food, and mouth disease （HFMD） in Beijing by SARIMA model. Methods The SARIMA model was established based on the monthly incidence of HFMD in Beijing from 2010 to 2017 collected from China Information System for Disease Control and Prevention, using SPSS 19.0 software. The SARIMA model was used to predict the incidence of HFMD in 2018. Results The annual incidence peak appeared during April to August, and the second peak was during September to October. The average annual incidence of HFMD from 2010 to 2017 was 166.83/100 000. The Stationary R-squared was 0.705, Mean Absolute Percentage Error （MAPE） was 33.217 and Bayesian Information Criterion （BIC） was 2.862. Through the test of parameters and goodness of fit as well as white-noise residuals, SARIMA （I ,0,1） （0,1,1） 12 was proved to be an optimal model for the observed values. The incidence of HFMD in Beijing 2018 was predicted to be 120.19/100,000, which will exceed the incidence of 2017. The annual incidence peak of HFMD from April to August will have a significant increase than 2017. Conclusions The results show that the SARIMA model provided a better fit to the incidence of HFMD in Beijing. This model can provide scientific basis for developing prevention and control strategies in advance.
作者
贾蕾
王小莉
霍达
杜轶威
李洁
庞星火
Jia Lei;Wang Xiaoli;Huo Da;Du Yiwei;Li Jie;Pang Xinghuo.(Institute for Infectious Disease and Endemic Disease Control Beijing Center for Disease Prevention and Control Beijing Research Center for Preventive Medicine, Beijing 100013, China)
出处
《国际病毒学杂志》
2018年第2期83-86,共4页
International Journal of Virology
基金
北京市自然科学基金（7164240）
北京市优秀人才培养资助青年骨干个人项目（2016000021469G184）
