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肺气肿发病率是政府和相关医学工作者预防与监测肺气肿的重要依据之一。因此,选择合理的肺气肿发病率预测模型显得尤为重要。文章提出了一种基于时间序列法的分时段传递函数模型来预测短期肺气肿发病率,该模型考虑了平均气温因素对肺气肿发病率的影响,同时利用累积式自回归滑动平均模型(ARIMA)对肺气肿发病率序列和平均气温序列的非平稳性进行处理,并且对2009年12个时段分别建立了预测模型。最后采用青海海西州地区的肺气肿发病率历史数据进行算例研究。结果表明,利用本文模型进行肺气肿发病率预测能够提高预测的准确性。对肺气肿发病率历史数据进行时间序列分析是用于肺气肿监测的一个重要的内容。该研究所建立的传递函数预测模型适用于青海海西州地区肺气肿发病率预测与监测。
The incidence of emphysema is one of the important bases for the prevention and monitoring of emphysema by the government and related medical workers. Therefore, the choice of a reasonable prediction of the incidence of emphysema appears to be particularly important. In this paper, we propose a time-series transfer function model to predict the incidence of short-term emphysema. The model considers the influence of mean temperature on the incidence of emphysema, and uses the cumulative autoregressive moving average model ARIMA) to deal with the non-stationary series of the incidence of emphysema and the average temperature series, and the prediction model was established respectively for 12 periods in 2009. Finally, the historical data of incidence of emphysema in Haixi Prefecture of Qinghai Province were used to carry out the case study. The results show that using this model to predict the incidence of emphysema can improve the prediction accuracy. Time series analysis of historical data on the incidence of emphysema is an important element for the monitoring of emphysema. The transfer function prediction model established in this study is suitable for the prediction and monitoring of the incidence of emphysema in Haixi Prefecture of Qinghai Province.