差分自回归移动平均乘积季节模型预测广州市肺结核发病趋势

刘伟; 刘远; 胡文穗; 董智强; 侯建荣; 王德东; 杨智聪

doi:10.16462/j.cnki.zhjbkz.2021.02.023

差分自回归移动平均乘积季节模型预测广州市肺结核发病趋势

doi: 10.16462/j.cnki.zhjbkz.2021.02.023

510440 广州，广州市疾病预防控制中心业务管理部

基金项目:

广州市科技计划项目 201904010156

详细信息

通讯作者:
杨智聪，E-mail：yangzc@gzcdc.org.cn

中图分类号: R181.2
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- 被引次数: 0
出版历程
- 收稿日期: 2020-03-24
- 修回日期: 2020-08-15
- 刊出日期: 2021-02-10

Application of multiple seasonal ARIMA model for predicting the incidence trend of tuberculosis in Guangzhou City

Operations Management Department, Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China

Funds:

Guangzhou Science and Technology Program Project 201904010156

More Information

Corresponding author: YANG Zhi-cong, E-mail: yangzc@gzcdc.org.cn

摘要

摘要: 目的探讨应用差分自回归移动平均(autoregressive intergrated moving average, ARIMA)乘积季节模型预测广州市肺结核月发病数的可行性，为制定防控措施提供参考依据。方法利用2010年1月至2019年6月广州市肺结核月发病数据建立ARIMA模型，并以2019年7-12月数据对模型的预测效果进行验证。结果 2010-2019年广州市共报告肺结核124 311例，总体呈下降趋势。2月发病数最少，3-4月发病数最多。拟合出的最佳模型ARIMA (0, 1, 1) (0, 1, 1)₁₂对广州市2019年7-12月肺结核月发病数预测结果显示实际值和预测值相对误差范围介于0.08%~11.33%，平均相对误差为1.46%。结论 ARIMA (0, 1, 1) (0, 1, 1)₁₂模型可用于广州市肺结核月发病数的短期预测。
- 肺结核 /
- 差分自回归移动平均模型 /
- 时间序列 /
- 预测
Abstract: Objective To explore the feasibility of applying the multiple seasonal autoregressive intergrated moving average (ARIMA) model to predict the monthly incidence of tuberculosis in Guangzhou, and to provide evidence for developing prevention and control measures. Methods The ARIMA model was established based on the monthly incidence of tuberculosis in Guangzhou from January 2010 to June 2019, and the prediction effect of the model was verified with the data from July to December 2019. Results A total of 124 311 tuberculosis cases were reported during 2010-2019 in Guangzhou, showing an overall decreasing trend, with the lowest incidence in February and the hightest in March to April. Using the best fitted model ARIMA (0, 1, 1) (0, 1, 1)₁₂ to predict the monthly incidence of tuberculosis in Guangzhou from July to December 2019, the results showed that the relative error between the actual value and predicted value ranged from 0.08% to 11.33%, and the average relative error was 1.46%. Conclusions The ARIMA (0, 1, 1) (0, 1, 1)₁₂ model can be used for short-term prediction of the monthly incidence of tuberculosis in Guangzhou.
- Tuberculosis /
- ARIMA /
- Time series /
- Prediction

HTML全文

图 1 广州市2010-2019年肺结核月发病数时间序列分解图

Figure 1. Time series decomposition plot of the monthly number of tuberculosis cases in Guangzhou City from 2010 to 2019

下载: 全尺寸图片幻灯片

图 2 广州市2010-2019年肺结核月发病数差分后时间序列、自相关函数和偏相关函数图

Figure 2. Time series, ACF and PACF plots of the monthly number of tuberculosis cases after differencing in Guangzhou from 2010 to 2019

下载: 全尺寸图片幻灯片

表 1 ARIMA(0, 1, 1) (0, 1, 1)₁₂模型参数估计

Table 1. Parameters estimation of the ARIMA (0, 1, 1) (0, 1, 1)₁₂ model

指标	参数估计		Ljung-Box残差检验		赤池信息准则值
指标	β值	s_x(β)值	统计量	P值	赤池信息准则值
MA1	-0.656	0.085	19.514	0.552	1 178.540
SMA1	-0.692	0.100

下载: 导出CSV

表 2 2019年7-12月广州市肺结核发病数实际值与预测值比较

Table 2. Comparison between actual value and predicted value of the monthly number of tuberculosis cases in Guangzhou from July to December 2019

月份(月)	实际值(人)	预测值(95% CI值)	相对误差(%)
7	864	849.64(697.08~1002.19)	1.66
8	792	806.71(645.36~968.06)	1.86
9	734	744.50(574.82~914.19)	1.43
10	726	730.57(552.94~908.21)	0.63
11	698	698.59(513.35~883.83)	0.08
12	725	642.83(450.29~835.37)	11.33

下载: 导出CSV

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