基于ECM模型和AR模型的我国境内新型冠状病毒COVID-19疫情数据仿真

楚雄师范学院学报 ›› 2020, Vol. 35 ›› Issue (6): 1-10.

• 数学 • 下一篇

基于ECM模型和AR模型的我国境内新型冠状病毒COVID-19疫情数据仿真

杨云源¹, 杨新平^2,*, 张洁³

1.楚雄师范学院地理科学与旅游管理学院,云南楚雄 675000;
2.楚雄师范学院数学与统计学院,云南楚雄 675000;
3.楚雄医药高等专科学校公共部,云南楚雄 675005

收稿日期:2020-05-29 出版日期:2020-11-20 发布日期:2021-03-29
通讯作者: *杨新平（1969-）,男,硕士,楚雄师范学院数学与统计学院副教授,主要研究方向为应用统计。E-mail：yangxp@cxtc.edu.cn,Tel：13638768267
作者简介:杨云源（1979–）,男,硕士,楚雄师范学院地理科学与旅游管理学院讲师,主要研究方向为GIS可视化和数据模拟。E-mail：yyy@cxtc.edu.cn,Tel：18987803165
基金资助:
云南省高校联合基金面上项目（NO.2017FH001-068）; 云南省教育厅资助项目（NO.2017ZZX018）

Simulation of COVID-19 Epidemic Data in China Pharmacutical on ECM Model and AR Model

YANG Yunyuan, YANG Xinping, ZHANG Jie

College of Geographical Science and Tourism Management, Chuxiong Normal University, Chuxiong, Yunnan Province 675000;
School of Mathematics &Statistics, Chuxiong Normal University, Chuxiong, Yunnan Province 675000;
Public Department, Chuxiong Medical College, Chuxiong, Yunnan Province 675005

Received:2020-05-29 Online:2020-11-20 Published:2021-03-29

摘要/Abstract

摘要： 我国境内COVID-19疫情数据仿真,对疫情研判具有一定指导意义。使用2020年1月20日至3月19日国家卫健委公布的疫情数据,建立误差修正模型（ECM）和自回归模型（AR）进行疫情数据仿真。ECM建模,R²为0.9439,模型t检验相伴概率小于0.05,差异显著;治愈率和累计确诊病例数两个AR模型,阶数分别为6和7,R²分别为0.9998和0.9974,F检验、t检验模型相伴概率都小于0.05,差异都显著,残差都是白噪声。ECM模型揭示可根据上一日的新增疑似病例数、新增确诊病例数定量估算当日的新增确诊病例数;随时间推移,新增确诊病例数和新增疑似病例数同步减少。AR模型估算表明：累计确诊病例数于2020年3月1日以后增长放缓并趋于稳定;到第60 天（2020年3月19日）,治愈率达到87.8278%,对应样本计算值为87.8753%,相对误差为0.0514%;到第60天全国累计确诊病例仿真值为81796.0989例,相对误差1.0240%。基于具有较强相关性的我国境内COVID-19疫情数据,本研究建立的ECM模型和两个AR模型的仿真度高,计算结果误差较小、精度较高,仿真结果可用于其他相关疫情指标的估算,不会因此造成误差放大。

关键词: COVID-19, 疫情时序数据, 误差修正模型, 自回归模型, 仿真

Abstract: Simulation of COVID-19 epidemic situation has certain guiding significance for judgment of epidemic situation in China. The daily data released by the National Health Commission from January 20 to March 29, 2020 have been used to build ECM model and AR model to simulate the epidemic data trend. In ECM model, R² is 0.9449 and t-test model associated probability is less than 0.05, showing significant difference. The order numbers of two AR models are 6 and 7 respectively. The R² of two AR models are 0.9998 and 0.9978 respectively. F-test and t-test models have significant associated probability of less than 0.05, and the residual is white noise. ECM model reveals that the number of newly confirmed cases on a certain day can be estimated quantitatively according to the number of newly added suspected cases and newly confirmed cases of the previous day. Over time, the number of new cases and suspected new cases has decreased simultaneously. AR model estimates show that the cumulative number of confirmed cases will slow down and stabilize after March 1, 2020. By the 60th day, or March 19, 2020, the cure rate is 87.8278% and the corresponding sample value is 87.8753% with a relative error of 0.0514%. By the 60th day, the national cumulative simulation value of confirmed cases is 81796.0989 with a relative error of 1.0240%. The ECM model and the two AR models established in this study based on the time series data of Covid-19 epidemic in China have high simulation degree, low error and high accuracy of the calculation results. The simulation results can be used for the estimation of other relevant epidemic indicators without causing error amplification.

Key words: COVID-19, time series data, ECM model, AR model, simulation

中图分类号:

R373.1

杨云源, 杨新平, 张洁. 基于ECM模型和AR模型的我国境内新型冠状病毒COVID-19疫情数据仿真[J]. 楚雄师范学院学报, 2020, 35(6): 1-10.

YANG Yunyuan, YANG Xinping, ZHANG Jie. Simulation of COVID-19 Epidemic Data in China Pharmacutical on ECM Model and AR Model[J]. Journal of Chuxiong Normal University, 2020, 35(6): 1-10.

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基于ECM模型和AR模型的我国境内新型冠状病毒COVID-19疫情数据仿真

Simulation of COVID-19 Epidemic Data in China Pharmacutical on ECM Model and AR Model

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