基于系统动力学的新冠病毒传播过程预测

doi:10.16182/j.issn1004731x.joss.20-1019

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (7): 1713-1721.doi: 10.16182/j.issn1004731x.joss.20-1019

基于系统动力学的新冠病毒传播过程预测

路雪鹏, 尚娇, 赵俊辉, 吕露露, 周丽

北京物资学院信息学院,北京 101149

收稿日期:2020-12-21 修回日期:2021-02-24 出版日期:2021-07-18 发布日期:2021-07-20
作者简介:路雪鹏(1995-),男,硕士生,研究方向为大数据分析。E-mail：luxuepeng822@163.com
基金资助:
国家自然科学基金(71501015); 北京社科基金重点项目(18GLA009); 北京市长城学者项目(CIT & TCD20170317)

Transmission Process Prediction of Novel Coronavirus Based on System Dynamics

Lu Xuepeng, Shang Jiao, Zhao Junhui, Lü Lulu, Zhou Li

School of Information, Beijing Wuzi University, Beijing 101149, China

Received:2020-12-21 Revised:2021-02-24 Online:2021-07-18 Published:2021-07-20

摘要/Abstract

摘要： 考虑新冠病毒的传播特点,基于系统动力学原理提出了一种新的SE4IR2 (Susceptible-Exposed- 4-Infected-Removed-2)模型,利用美国2020年6-11月份疫情数据设置隔离率等参数,运用SE4IR2模型拟合分析并预测疫情下一阶段的发展趋势。实证部分使用美国2020年6-11月份的数据求解SE4IR2模型的参数,通过时间序列预测模型得出2020年12月份和2021年1月份的参数值,并将死亡人数、治愈人数预测值与2020年12月份和2021年1月份世卫组织公布的数据对比,得出2020年12月份的误差为0.75%,- 0.86%,2021年1月份的误差为2.78%,3.57%。根据实证分析结果,考虑各参数之间的制约条件给出相应的疫情防控建议。结果表明：SE4IR2模型具有更好的仿真精度,更适合模拟COVID-19的传播过程。

关键词: COVID-19, 系统动力学, 动态规划, 时间序列预测, 自回归滑动平均模型

Abstract: The transmission characteristics of novel coronavirus is considered and a new SE4IR2 model based on the principle of system dynamics is proposed. The US epidemic data from June to November is used to set the isolation rate and other parameters, and the SE4IR2 model is used to fit, analyze and predict the development of the epidemic trend in the next stage. The empirical part uses the data from June to November in the United States to achieve the parameters of the SE4IR2 model, obtains the parameter values in December and January through the time series prediction model, and compares the predicted number of deaths and the people cured with those of the WTO published in December and January. The error in December is 0.75% and - 0.86%, and the error in January is 2.78% and 3.57%. Based on the results of the empirical analysis, considering the constraints between the various parameters, the corresponding recommendations for epidemic prevention and control are given. The results show that the SE4IR2 model has better simulation accuracy and is more suitable for simulating the spread of COVID-19.

Key words: COVID-19, system dynamics, dynamic programming, time series prediction, Auto Regression Moving Average

中图分类号:

TP391.9

路雪鹏, 尚娇, 赵俊辉, 吕露露, 周丽. 基于系统动力学的新冠病毒传播过程预测[J]. 系统仿真学报, 2021, 33(7): 1713-1721.

Lu Xuepeng, Shang Jiao, Zhao Junhui, Lü Lulu, Zhou Li. Transmission Process Prediction of Novel Coronavirus Based on System Dynamics[J]. Journal of System Simulation, 2021, 33(7): 1713-1721.

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基于系统动力学的新冠病毒传播过程预测

Transmission Process Prediction of Novel Coronavirus Based on System Dynamics

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