系统仿真学报 ›› 2022, Vol. 34 ›› Issue (7): 1532-1546.doi: 10.16182/j.issn1004731x.joss.21-0912

• 仿真建模理论与方法 • 上一篇    下一篇

基于SEIiRD模型的COVID-19传播及防控仿真研究

汪婧(), 董莹   

  1. 福州大学 经济与管理学院,福建 福州 350108
  • 收稿日期:2021-09-06 修回日期:2021-12-21 出版日期:2022-07-30 发布日期:2022-07-20
  • 作者简介:汪婧(1985-),女,博士,讲师,研究方向为突发事件应急管理。E-mail:jingwang1110@fzu.edu.com
  • 基金资助:
    国家社科青年基金(16CGL063)

Simulation Research on COVID-19 Transmission and Control Measures Based on SEIiRD Model

Jing Wang(), Ying Dong   

  1. School of Economics and Management, Fuzhou University, Fuzhou 350108, China
  • Received:2021-09-06 Revised:2021-12-21 Online:2022-07-30 Published:2022-07-20

摘要:

随着新冠肺炎疫情在全世界各地传播,通过对其数据和传播机理的分析,已有SEIRD模型的基础上构建SEIiRD模型,将感染人群分为无症状感染者、轻症感染者、重症感染者和危重症感染者,分析不同感染人群的传播率对疫情发展的影响。在拟合现实数据的基础上进行仿真实验,研究发现影响疫情发展的主要感染人群是无症状感染者和轻症感染者。在此基础上进一步分析了不同的无症状和轻症感染者的传播速率所造成感染人数和死亡人数的变化情况。针对介入干预时间对感染人数和死亡人数的影响进行了仿真分析。实验结果表明该模型能有效模拟新冠肺炎疫情传播规律,有助于防控职能部门实施相应的疫情防控策略提供决策支持

关键词: SEIiRD模型, 疫情传播, COVID-19, 防控措施, 传播动力学

Abstract:

With the spread of the novel coronavirus pneumonia around the world, the data and transmission mechanism are analyzed. The SEIiRD model is constructed based on the existing SEIRD model, and the infected population is divided into asymptomatic infections, mild infections, severe infections and critical infections. The impact of the transmission rate of different infected people on the development of the epidemic was analyzed. Simulation experiments were carried out on the basis of fitting real data, and it was found that the main infected populations that affected the discovery of the epidemic were asymptomatic and mildly infected. On this basis, the transmission rate of different asymptomatic and mildly infected people was further analyzed. The impact of different intervention times on the number of infections and deaths was simulated. Results show that the model can effectively simulate the spread of COVID-19 and provide decision-making support to the departments to implement corresponding epidemic prevention and control strategies.

Key words: SEIiRD model, epidemic spread, COVID-19, prevention and control measures, spread dynamics

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