目的 探讨疏系数求和自回归移动平均(ARIMA) 模型预测结核病发病率的可行性,为制定有针对性的防制政策提供科学依据。方法 根据江西省2005年1月～2013年12月结核病监测发病资料进行疏系数ARIMA 预测模型的建立,选择2014和2015年肺结核发病资料评价预测效果。结果 江西省2005年1月～2013年12月肺结核的发病率呈现以年为周期的季节效应,并且出现长期递减的趋势;拟合疏系数ARIMA(0,(1,12),(2,3))模型可以较好地诠释肺结核历史发病数据,且对2014和2015年肺结核的月发病率预测情况与实际情况基本相近。结论 疏系数ARIMA模型能有效阐明江西省肺结核的发病时间规律和预测发病趋势。

Objective To explore the feasibility of Sparse coefficient autoregressive integrated moving average( ARIMA) model for predicting tuberculosis incidence and provide a scientific evidence for the targeted prevention and control policy of TB. Methods Sparse coefficient ARIMA model was built using tuberculosis surveillance data from January 2005 to December 2013 in Jiangxi province, and the predictive effect was evaluated by using the data from 2014 to 2015. Results The annual seasonal effect in the incidence of tuberculosis was observed from January 2005 to December 2013 in the province, and the long-term descending trend in the incidence of tuberculosis was also observed. Sparse coefficient ARIMA (0,(1,12),(2,3)) model could better fit the incidence of tuberculosis over the period, and forecast situation and actual situation were basically similar. Conclusion Sparse coefficient ARIMA model could effectively clarify the regularity of the incidence of tuberculosis in Jiangxi province and predict the trend of the incidence.