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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 predicting the incidence of tuberculosis by the ARIMA model and provide a scientific basis for the development of targeted prevention and control policies. Methods Based on the incidence data of tuberculosis surveillance from January 2005 to December 2013 in Jiangxi Province, the ARIMA prediction model was established. The prediction results of tuberculosis incidence in 2014 and 2015 were selected. Results The incidence of pulmonary tuberculosis in Jiangxi Province from January 2005 to December 2013 showed a seasonal effect with annual cycle and a long-term decreasing trend. The fitting sparse coefficient ARIMA (0, (1,12), (2,3 )) Model can better interpret the history of tuberculosis incidence data, and the monthly incidence of tuberculosis in 2014 and 2015 forecast the situation is similar to the actual situation. Conclusion The ARIMA model of sparse coefficient can effectively explain the incidence of tuberculosis in Jiangxi province and predict the trend of onset.