四川省艾滋病发病人数模型及预测

Model and Prediction of AIDS Incidence in Sichuan Province

摘要: 为探究四川省艾滋病发病人数的趋势，本文利用残差修正GM(1,1)模型和BP (Back Propagation)神经网络模型对发病人数进行预测并对预测效果进行比较。根据四川省2005年第1季度至2016年第4季度艾滋病发病人数建立的残差修正GM(1,1)模型和BP神经网络模型，对2017年第1季度至第4季度发病人数进行预测。残差修正GM(1,1)模型预测出2017年四川省艾滋病发病人数的平均绝对误差(MAE)、平均绝对百分比误差(MAPE)分别为1019和0.4023；BP神经网络模型预测出2017年四川省艾滋病发病人数的MAE、MAPE分别为236和0.0697。BP神经网络模型相较于残差修正GM(1,1)模型能更好地拟合四川省艾滋病的发病趋势，因此更适用于四川省艾滋病发病人数的短期预测。

Abstract: In order to explore the trend of the number of AIDS cases in Sichuan province, this paper uses the residual correction GM(1,1) model and the BP (Back Propagation) neural network model to predict the number of cases and compare the prediction results. Based on the data on the number of AIDS cases in Sichuan Province from the first quarter of 2005 to the fourth quarter of 2016, a residual correction GM(1,1) model and a BP neural network model were established to predict the number of cases from the first quarter to the fourth quarter of 2017. The residual correction GM(1,1) model predicted that the average absolute error (MAE) and average absolute percentage error (MAPE) of the number of AIDS cases in Sichuan Province in 2017 were 1019 and 0.4023, respectively; the BP neural network model predicted that MAE and MAPE of the number of AIDS cases in Sichuan Province in 2017 were 236 and 0.0697, respectively. Compared with the residual correction GM(1,1) model, the BP neural network model can better fit the incidence trend of AIDS in Sichuan Province, therefore, it is more suitable for short-term prediction of AIDS incidence in Sichuan Province.