【摘要】：目的探討差分自回歸移動(dòng)平均(ARIMA)與廣義回歸神經(jīng)網(wǎng)絡(luò)(GRNN)組合模型在丙型肝炎月發(fā)病率中預(yù)測(cè)建模效果及應(yīng)用前景,為疫情預(yù)測(cè)提供依據(jù)。方法 2015年5月—2016年5月,選取山東省疾病預(yù)防控制中心法定傳染病直報(bào)系統(tǒng)2004—2014年丙型肝炎月度發(fā)病率數(shù)據(jù)及山東省統(tǒng)計(jì)局發(fā)布的同期人口資料。對(duì)2004—2014年山東省丙型肝炎月發(fā)病率數(shù)據(jù)構(gòu)建ARIMA模型,驗(yàn)證擬合精度并外推預(yù)測(cè);將ARIMA模型擬合值作為GRNN模型的輸入,實(shí)際值作為GRNN模型的輸出,對(duì)樣本進(jìn)行訓(xùn)練和預(yù)測(cè)。比較單純ARIMA模型和ARIMA-GRNN組合模型在丙型肝炎月發(fā)病率中的預(yù)測(cè)效果。結(jié)果 2004—2014年山東省丙型肝炎年均發(fā)病率為17.28/10萬(wàn),并隨著時(shí)間的推移呈上升趨勢(shì)(Z=29.05,P0.01)。ARIMA(1,2,1)模型預(yù)測(cè)2014年山東省丙型肝炎發(fā)病率與實(shí)際發(fā)病率基本一致,落在95%置信區(qū)間內(nèi),擬合效果較好。以ARIMA(1,2,1)模型擬合值作為GRNN模型的輸入,丙型肝炎月發(fā)病率實(shí)際值作為GRNN模型的輸出,取最優(yōu)光滑因子0.12訓(xùn)練模型,ARIMA-GRNN組合模型預(yù)測(cè)的擬合值與實(shí)際值基本吻合。ARIMA模型和ARIMA-GRNN組合模型的平均誤差率(MER)分別為16.87%、15.30%;決定系數(shù)(R~2)分別為0.53、0.60;平均絕對(duì)誤差(MAE)分別為0.17、0.09;平均絕對(duì)百分誤差(MAPE)分別為1.18、0.35。結(jié)論 ARIMA-GRNN組合模型對(duì)山東省丙型肝炎月發(fā)病率擬合及預(yù)測(cè)效果優(yōu)于單純ARIMA模型,具有較高的擬合精度,有較為廣闊的應(yīng)用前景,對(duì)于疫情預(yù)測(cè)工作有一定的實(shí)用性意義。
[Abstract]:Objective to explore the effect and application prospect of differential autoregressive moving average (ARIMA) model combined with generalized regression neural network (GRNN) model in predicting the monthly incidence of hepatitis C. Methods from May 2015 to May 2016, the monthly incidence data of hepatitis C from 2004 to 2014 and the population data of the same period published by Shandong Bureau of Statistics were selected. Based on the monthly incidence data of hepatitis C in Shandong Province from 2004 to 2014, the ARIMA model was constructed to verify the fitting accuracy and extrapolate prediction, and the fitting value of ARIMA model was taken as the input of GRNN model and the actual value was taken as the output of GRNN model to train and predict the samples. To compare the predictive effect of simple ARIMA model and ARIMA-GRNN combination model in the monthly incidence of hepatitis C. Results the average annual incidence of hepatitis C in Shandong Province from 2004 to 2014 was 17.28 / 100 000, and showed an increasing trend with the passage of time. The results are as follows: 1) the incidence of hepatitis C in Shandong Province in 2014 is basically consistent with the actual incidence, falling within 95% confidence interval, and the fitting effect is good. The fitting value of ARIMA model was taken as the input of GRNN model, the actual value of monthly incidence of hepatitis C was taken as the output of GRNN model, and the optimal smoothing factor 0.12 training model was selected. The average error rate of ARIMA model and ARIMA-GRNN combination model is 16.8715.30, respectively. The average absolute error (MAE) is 0.170.09, and the average absolute percent error (MAPE) is 1.18 ~ 0.35. The determination coefficient (R _ (2) is 0.53 ~ (0.60), the average absolute error (MAE) is 0.17 ~ (0.09), respectively. Conclusion the ARIMA-GRNN combination model is superior to the simple ARIMA model in fitting and predicting the incidence of hepatitis C in Shandong province. It has a higher fitting precision and has a broad application prospect. It has some practical significance for the prediction of the epidemic situation.
【作者單位】： 濰坊醫(yī)學(xué)院公共衛(wèi)生與管理學(xué)院衛(wèi)生統(tǒng)計(jì)學(xué)教研室;
【基金】：“健康山東”重大社會(huì)風(fēng)險(xiǎn)預(yù)測(cè)與治理協(xié)同創(chuàng)新中心資助課題(XT-1402001)
【分類號(hào)】：R512.63

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