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目的探索曲线估计在流行性感冒发病预测中的应用并对预测效果进行评价,为该病的防治提供理论依据。方法采用移动平均法对2010—2014年安徽省流行性感冒分月发病数建立基础数据库,运用曲线估计对分月发病数开展模型的拟合并优选最佳模型,通过2015年实际发病数与模型预测结果比对进行拟合效果评价。结果 2010—2015年安徽省各级医疗、疾控机构报告流行性感冒病例38 523例,年发病率为4.35/10万~18.58/10万,各年发病率差异有统计学意义(P<0.01)。病例报告集中于12月和次年的1—3月,报告病例数占全部病例数的47.14%(18 160/38 523);其中8月份占8.58%(3 306/38 523),存在夏季病例报告小高峰。模型拟合结果显示4—7和9月适用三次方曲线模型;1、8和10月适用二次方曲线模型;11和12月适用线性模型;2月适用S型曲线模型;3月适用乘幂次方曲线模型。模型预测结果显示2015年流行性感冒病例数为11 938例,实际发病数为11 300例,预测误差率为5.34%。结论曲线估计作为流行性感冒发病预测方法可行,预测结果较为可靠。
Objective To explore the application of curve estimation in the prediction of the incidence of influenza and to evaluate the prediction results so as to provide a theoretical basis for the prevention and treatment of the disease. Methods The moving average method was used to establish the basic database of monthly incidence of influenza in 2010-2014 in Anhui Province. The curve fitting was used to estimate the monthly morbidity and the best model was selected. The actual incidence and the model The result of the prediction is compared with the result of the evaluation of the fit. Results From 2010 to 2015, 38 523 cases of influenza were reported by medical and disease control institutions at all levels in Anhui Province. The annual incidence rate was 4.35 / 10 ~ 18.58 / 100 000, with significant difference in the incidence rates of each year (P0.01 ). The case reports focused on December and January-March of the following year, accounting for 47.14% (18 160/38 523) of the total number of cases, of which 8.58% (3 306/38 523) in August and summer cases Report a small peak. The fitting results of the model show that the cubic curve model is suitable for 4-7 and 9 months; the quadratic curve model is applicable for 1,8 and 10 months; the linear model is applicable for 11 and 12 months; the S-curve model is applicable for February; Power curve model. The model prediction results showed that the number of cases of influenza in 2015 was 11 938, the actual number of cases was 11 300 and the prediction error rate was 5.34%. Conclusion Curve estimation is feasible as a predictor of influenza morbidity. The prediction results are reliable.