| 言晨绮,王瑞白,刘海灿,蒋毅,李马超,尹树鹏,肖彤洋,万康林,让蔚清.ARIMA模型预测2018－2019年我国肺结核发病趋势的应用[J].Chinese journal of Epidemiology,2019,40(6):633-637 |
| ARIMA模型预测2018－2019年我国肺结核发病趋势的应用 |
| Application of ARIMA model in predicting the incidence of tuberculosis in China from 2018 to 2019 |
| Received:November 29, 2018 |
| DOI：10.3760/cma.j.issn.0254-6450.2019.06.006 |
| KeyWord: 肺结核 自回归移动平均模型 预测 |
| English Key Word: Pulmonary tuberculosis Autoregressive integrated moving average model Prediction |
| FundProject: |
| Author Name | Affiliation | E-mail | | Yan Chenqi | School of Public Health, University of South China, Hengyang 421001, China | | | Wang Ruibai | State Key Laboratory for Infectious Diseases Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Liu Haican | State Key Laboratory for Infectious Diseases Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Jiang Yi | State Key Laboratory for Infectious Diseases Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Li Machao | State Key Laboratory for Infectious Diseases Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Yin Shupeng | State Key Laboratory for Infectious Diseases Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Xiao Tongyang | State Key Laboratory for Infectious Diseases Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | | | Wan Kanglin | State Key Laboratory for Infectious Diseases Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China | wankanglin@icdc.cn | | Rang Weiqing | School of Public Health, University of South China, Hengyang 421001, China | nhurwq@126.com |
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| Abstract: |
| 目的 应用自回归移动平均（autoregressive integrated moving average，ARIMA）模型对我国2018－2019年肺结核发病情况进行预测，为肺结核防控工作提供参考依据。方法 收集2005年1月至2017年12月中国肺结核月发病数据，使用R 3.4.4软件基于2005年1月至2017年6月肺结核月发病数据建立ARIMA模型，比较2017年7－12月预测数据和实际数据以进行模型预测性能的检验，并预测2018－2019年肺结核发病数情况。结果 2005－2017年共报告肺结核患者13 022 675例，发病数呈逐年下降趋势，2017年肺结核患者数较2005年下降了33.68%，且季节性明显，每年冬春交界之时发病数较高。根据2005年1月至2017年6月肺结核月发病数据拟合出了ARIMA（0，1，2）（0，1，0）12模型，该模型拟合的2017年7－12月的预测值与实际值的相对误差范围是1.67%～6.80%，预测2018年和2019年发病数分别为789 509例和760 165例。结论 ARIMA（0，1，2）（0，1，0）12模型对我国肺结核发病数的拟合效果较好，可用于我国肺结核的短期预测和动态分析，具有较好的应用价值。 |
| English Abstract: |
| Objective Autoregressive integrated moving average (ARIMA) model was used to predict the incidence of tuberculosis in China from 2018 to 2019, providing references for the prevention and control of pulmonary tuberculosis. Methods The monthly incidence data of tuberculosis in China were collected from January 2005 to December 2017. R 3.4.4 software was used to establish the ARIMA model, based on the monthly incidence data of tuberculosis from January 2005 to June 2017. Both predicted and actual data from July to December 2017 were compared to verify the effectiveness of this model, and the number of tuberculosis cases in 2018-2019 also predicted. Results From 2005 to 2017, a total of 13 022 675 cases of tuberculosis were reported, the number of pulmonary tuberculosis patients in 2017 was 33.68% lower than that in 2005, and the seasonal character was obvious, with the incidence in winter and spring was higher than that in other seasons. According to the incidence data from 2005 to 2017, we established the model of ARIMA (0,1,2)(0,1,0)12. The relative error between the predicted and actual values of July to December 2017 fitted by the model ranged from 1.67% to 6.80%, and the predicted number of patients in 2018 and 2019 were 789 509 and 760 165 respectively. Conclusion The ARIMA (0, 1, 2)(0, 1, 0)12 model well predicted the incidence of tuberculosis, thus can be used for short-term prediction and dynamic analysis of tuberculosis in China, with good application value. |
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