| 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 |
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| 引用本文： | 刘伟,刘远,胡文穗,董智强,侯建荣,王德东,杨智聪. 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[J]. 中华疾病控制杂志, 2021, 25(2): 240-243,248. DOI: 10.16462/j.cnki.zhjbkz.2021.02.023 |
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| 作者姓名： | 刘伟 刘远 胡文穗 董智强 侯建荣 王德东 杨智聪 |
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| 作者单位： | 510440广州,广州市疾病预防控制中心业务管理部 |
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| 基金项目： | 广州市科技计划项目(201904010156)。 |
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| 摘 要： | 目的 探讨应用差分自回归移动平均(autoregressive intergrated moving average,ARI-MA)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...
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| 关 键 词： | 肺结核 差分自回归移动平均模型 时间序列 预测 |
| 收稿时间： | 2020-03-24 |
Application of multiple seasonal ARIMA model for predicting the incidence trend of tuberculosis in Guangzhou City |
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| LIU Wei,LIU Yuan,HU Wen-sui,DONG Zhi-qiang,HOU Jian-rong,WANG De-dong,YANG Zhi-cong. Application of multiple seasonal ARIMA model for predicting the incidence trend of tuberculosis in Guangzhou City[J]. Chinese Journal of Disease Control & Prevention, 2021, 25(2): 240-243,248. DOI: 10.16462/j.cnki.zhjbkz.2021.02.023 |
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| Authors: | LIU Wei LIU Yuan HU Wen-sui DONG Zhi-qiang HOU Jian-rong WANG De-dong YANG Zhi-cong |
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| Affiliation: | Operations Management Department, Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China |
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| 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%. Conclusion 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. |
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| Keywords: | Tuberculosis ARIMA Time series Prediction |
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