收稿日期: 2021-01-13
修回日期: 2021-03-12
网络出版日期: 2021-04-30
基金资助
四川省科技计划(2018SZ0116)
Trend prediction on the detection rate of newly discovered echinococcosis patients in Sichuan Province based on ARMA and GM (1,1) models
Received date: 2021-01-13
Revised date: 2021-03-12
Online published: 2021-04-30
Supported by
Sichuan Science and Technology Program(2018SZ0116)
目的 探索适用于预测四川省新发现棘球蚴病患者检出率的模型,为制定“十四五”棘球蚴病防治规划和策略提供科学依据。 方法 收集四川省2007—2020年每年新发现棘球蚴病患者检出率数据,分别建立ARMA时间序列模型和灰色系统GM(1,1)模型,对2021—2023年新发现棘球蚴病患者的检出情况进行预测,同时采用判定系数、平均绝对误差和均方误差等指标比较2种模型的拟合和预测效果。 结果 ARMA模型分析结果显示,2017—2020年检出率拟合值和实际值的误差分别为12.44、26.04和33.30,预测2021、2022和2023年四川省新发现棘球蚴病患者检出率分别为50.14/10万、50.04/10万和49.99/10万;GM(1,1)模型分析结果显示,2017—2020年检出率拟合值和实际值的误差分别为6.51、2.94和-1.20,预测2021、2022和2023年四川省新发现棘球蚴病患者检出率分别为 20.55/10万、17.65/10万和15.77/10万。对两种模型进行拟合效果评价结果显示,GM(1,1)模型的预测效果优于ARMA模型。 结论 可采用GM(1,1)模型预测四川省未来新发现棘球蚴病患者检出率变化趋势,预测的检出率呈现出下降趋势,但仍有新病例检出,需继续加强棘球蚴病的综合防治,巩固防治成果。
何伟 , 王奇 , 喻文杰 , 黄燕 , 张光葭 , 廖沙 , 杨柳 , 姚人新 , 李汭芮 , 刘阳 , 钟波 , 王谦 . 基于ARMA和GM(1,1)模型预测四川省新发现棘球蚴病患者检出率变化趋势[J]. 中国寄生虫学与寄生虫病杂志, 2021 , 39(2) : 185 -190 . DOI: 10.12140/j.issn.1000-7423.2021.02.010
Objective To explore a model suitable for predicting the detection rate of newly discovered echinococcosis patients in Sichuan Province, and to provide scientific basis for formulating prevention and control strategies for echinococcosis. Methods The detection rate data of newly discovered echinococcosis patients in Sichuan Province from 2007 to 2020 were collected, and ARMA and GM (1,1) models were established respectively to predict the detection rate from 2021 to 2023. Meanwhile, the fitting and prediction effects of the two models were compared using the coefficient of determination, mean absolute error and mean-square error. Results The ARMA model was established, and the errors between fitting and actual values of detection rate in 2017—2020 were 12.44, 26.04 and 33.30, respectively. The predicted detection rates in 2021, 2022 and 2023 using this model were 50.14/100 000, 50.04/100 000 and 49.99/100 000, respectively. Uing the GM(1,1) model, the errors between fitting and actual values of detection rate in 2017—2020 were 6.51, 2.94 and -1.20, respectively, and the predicted detection rates in 2021, 2022 and 2023 were 20.55/100 000, 17.65/100 000 and 15.77/100 000, respectively. Evaluation of fitting effects showed that the GM(1,1) had better prediction performance than the ARMA model. Conclusion The GM (1,1) model can predict the detection rate of newly discovered echinococcosis patients in Sichuan Province. Although the predicted detection rate show a trend of decrease, new cases are continuously being detected. Therefore, measures should be taken to strength prevention and control of echinococcosis and consolidate the achievements of disease control.
Key words： Sichuan Province; Echinococcosis; ARMA model; GM (1,1) model; Prediction
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