本文選題：肺結核　切入點：SARIMA模型　出處：《現代預防醫(yī)學》2017年03期

【摘要】：目的探討季節(jié)性自回歸積分滑動平均模型(Seasonal Autoregressive Integrated Moving Average Model,SARIMA)在分析湖北省肺結核報告病例時間分布特征中的應用,預測其發(fā)病趨勢。方法利用全國"傳染病報告信息管理系統(tǒng)"中2005年1月至2015年12月湖北省肺結核各月病例數,分析肺結核報告病例的時間分布特征,建立SARIMA預測模型進行趨勢預測。結果肺結核報告病例數整體處于下降趨勢且具有明顯的季節(jié)特征,3-6月和12月為年周期的兩個高峰期;在肺結核病例時間分布特征中擬合得到的SARIMA最佳預測模型為(1-B)(1-B12)xt=(1-0.732B)(1-0.409B~(12))μt,模型的顯著性檢驗得到P0.05,顯示擬合模型具有統(tǒng)計學意義。結論建立的SARIMA模型能較好地預測湖北省肺結核報告病例數的時間趨勢,為疫情監(jiān)測和疾病防治提供參考依據。
[Abstract]:Objective to explore the application of seasonal Autoregressive Integrated Moving Average model SARIMA in the analysis of the temporal distribution of reported tuberculosis cases in Hubei Province. Methods using the national infectious disease report information management system, the number of tuberculosis cases from January 2005 to December 2015 in Hubei Province was analyzed, and the time distribution characteristics of the reported cases were analyzed. SARIMA forecasting model was established to forecast the trend. Results the total number of reported cases of pulmonary tuberculosis was in a downward trend and had obvious seasonal characteristics: March to June and December were the two peak periods of the annual cycle. The best prediction model of SARIMA obtained by fitting the time distribution of pulmonary tuberculosis cases is 1-B ~ (12) B ~ (12) B ~ (1) ~ (12) 渭 t ~ (-1), and the significance test of the model is P0.05, which shows that the fitting model has statistical significance. Conclusion the established SARIMA model can better predict the lung of Hubei province. Time trends in the number of TB cases reported, To provide reference for epidemic surveillance and disease control.
【作者單位】： 湖北省疾病預防控制中心;華中科技大學醫(yī)學院;
【基金】：湖北省衛(wèi)生計生科研基金資助(WJ2016JT-002)
【分類號】：R521;R181.3

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