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目的探索流行性腮腺炎月发病数的最佳预测模型,为流腮发病的预测和预警提供理论依据。方法使用SPSS18.0软件,分别采用单纯自回归移动平均模型(ARIMA模型)、自回归移动平均-多层感知器神经网络模型(ARIMA-MLP组合模型)及自回归移动平均-径向基函数神经网络模型(ARIMA-RBF组合模型)对陕西省2009—2014年流腮月发病数进行拟合,找出最佳预测模型。结果单纯ARIMA模型拟合优度R~2值为0.831,平均绝对误差(MAE)值为267.49;ARIMA-MLP组合模型的R~2值为0.881,MAE值为208.01;ARIMA-RBF组合模型的R~2值为0.898,MAE值为205.82。结论 ARIMARBF组合模型对陕西省流腮月发病数预测效果最佳,可以为流腮发病的预测、预警提供理论依据。
Objective To explore the best prediction model of monthly incidence of mumps and provide a theoretical basis for the prediction and early warning of the incidence of mumps. Methods Using SPSS18.0 software, the ARIMA model, the autoregressive moving average-multilayer perceptron neural network model (ARIMA-MLP combination model) and the autoregressive moving average-radial basis function neural Network model (ARIMA-RBF combination model) to simulate the incidence of gonorrhea in Shaanxi Province from 2009 to 2014 to find the best prediction model. Results The excellent R2 of ARIMA model was 0.831, the mean absolute error (MAE) was 267.49. The R ~ 2 value of ARIMA-MLP combined model was 0.881 and the MAE value was 208.01. The ARIMA-RBF combined model’s R The ~ 2 value was 0.898 and the MAE value was 205.82. Conclusions ARIMARBF combination model is the best one to predict the number of morbidity in Shaanxi Province, which can provide a theoretical basis for prediction and early warning of morbidity.