摘要
目的探讨指数平滑法和自回归积分滑动平均模型(auto regressive integrated moving average model,ARIMA)在衡阳市学生肺结核疫情预测中的可行性,比较两种模型的预测效果并确定最佳模型,为学校结核病疫情的早期发现和科学控制提供参考。方法收集衡阳市2010—2020年学生肺结核资料,比较两种模型的拟合情况和预测效果优劣。结果拟合最佳的指数平滑法模型为Holt-Winter加法模型,拟合的R^(2)、平稳R^(2)、均方根误差(root mean square error,RMSE)、平均绝对误差百分比(mean absolute percentage error,MAPE)、平均绝对误差(mean absolute error,MAE)、正态化BIC分别是0.666、0.469、5.716、31.276、3.873、3.606,Ljung-Box Q=20.741,P=0.145,验证2020年1—12月预测的平均相对误差为39.98%;拟合最佳的ARIMA模型为ARIMA(0,1,1)×(0,1,1)_(12)模型,拟合的平稳R^(2)、R^(2)、RMSE、MAPE、MAE、正态化BIC分别是0.500、0.603、6.532、34.623、4.443、3.885,验证2020年1—12月预测的平均相对误差为120.76%。结论与ARIMA模型比较而言,指数平滑模型拟合衡阳市学生肺结核发病数效果更好,预测精度更高。
Objective To explore the feasibility of exponential smoothing method and auto regressive integrated moving average(ARIMA)model in the prediction of pulmonary tuberculosis epidemic among students in Hengyang City,to compare the prediction effects of the two models and then determine a better model so as to provide references for early detection and scientific control of tuberculosis epidemic in schools.Methods We collected the data concerning pulmonary tuberculosis among students in Hengyang City from 2010 to 2020,and then compared the fit and advantages and disadvantages of prediction effects of the two models.Results The best fitting exponential smoothing model was the Holt-Winter additive model.The fitted R^(2),stationary R^(2),root mean square error(RMSE),mean absolute percentage error(MAPE),mean absolute error(MAE),and normalized Bayesian information criterion(BIC)were 0.666,0.469,5.716,31.276,3.873 and 3.606,respectively.Ljung-Box Q was 20.741,P 0.145,and the average relative error of prediction from January to December 2020 verified 39.98%.The best fitting ARIMA model was ARIMA(0,0,1)×(0,1,1)_(12)model,and the fitted stationary R^(2),R^(2),RMSE,MAPE,MAE and normalized BIC were 0.500,0.603,6.532,34.623,4.443 and 3.885,respectively.The average relative error of prediction from January to December 2020 verified was 120.76%.Conclusion Compared with the ARIMA model,the exponential smoothing model is more effective in fitting the number of pulmonary tuberculosis cases among students in Hengyang City;moreover,its prediction accuracy is higher.
作者
周美艳
黄颖
颜宇龙
周洁
黄波
ZHOU Mei-yan;HUANG Ying;YAN Yu-long;ZHOU Jie;HUANG Bo(School of Public Health,University of South China,Hengyang,Hunan 421001,China;Hengyang Municipal Center for Disease Control and Prevention,Hengyang,Hunan 421001,China)
出处
《实用预防医学》
CAS
2022年第1期18-22,共5页
Practical Preventive Medicine
关键词
学生
肺结核
指数平滑法
ARIMA
预测
student
pulmonary tuberculosis
exponential smoothing
auto regressive integrated moving average
prediction
作者简介
周美艳(1993-),女,湖南衡阳,研究生,医师,研究方向:结核病预防和控制;通信作者:黄波,E-mail:huangbo0930@163.com。
