考虑死亡因素和不同时段接触数的COVID-19动态SEIR模型
SEIR Model for COVID-19 Dynamics Considering Death Factor and Contact Numbers in Different Periods
DOI: 10.12677/AAM.2021.106196, PDF,    科研立项经费支持
作者: 张儆轩:宁夏大学物理与电子电气工程学院,宁夏 银川;于 洁, 赵秉新*:宁夏大学数学统计学院,宁夏 银川
关键词: COVID-19SEIR模型全局稳定性相轨线基本再生数COVID-19 SEIR Model Global Stability Phase Trajectory Basic Regeneration Number
摘要: 目前新型冠状病毒肺炎(COVID-19)仍在全球大面积蔓延,对公众健康构成严重威胁,疫情防控形式依然严峻。通过构建合理的数学模型,可以科学地预测传染病在不同地区的发展趋势并对发展阶段进行合理的评估,具有重要的现实意义。本文建立了考虑死亡因素和不同时段接触数的SEIR模型,对模型作了平衡点的稳定性分析,并利用美国纽约州疫情数据进行验证,模型预测结果与实际情况吻合的很好,表明模型具有较好的预测能力。此外,根据基本再生数R0的特点,给出了一个判断疫情能否稳定的判据,通过降低人口流动以及人群的接触率可以较好地控制疫情。
Abstract: Coronavirus disease 2019 (COVID-19) is spreading in large areas worldwide and poses a serious public health risk. Epidemic prevention is still a great challenge. By using a reasonable mathematical model, we can scientifically predict the trend of infectious diseases in different regions and make reasonable assessment of the development stage, which is of great significance. In this paper, a SEIR model for COVID-19 dynamics considering death factors and the number of contacts in different periods is presented. The stability of the equilibrium point of the model is analyzed and verified by using the epidemic data of New York state of the United States. The prediction results of the model are in good agreement with the actual situation, indicating that the model has good prediction ability. In addition, according to the characteristics of the basic reproduction number R0, we give a criterion to judge whether the epidemic situation is stable. The epidemic situation can be well controlled by reducing population mobility and contact rate.
文章引用:张儆轩, 于洁, 赵秉新. 考虑死亡因素和不同时段接触数的COVID-19动态SEIR模型[J]. 应用数学进展, 2021, 10(6): 1871-1879. https://doi.org/10.12677/AAM.2021.106196

参考文献

[1] Sun, D., Duan, L., Xiong, L. and Wang, D. (2020) Modeling and Forecasting the Spread Tendency of the COVID-19 in China. Advances in Difference Equations, 2020, Article No. 489. [Google Scholar] [CrossRef
[2] 朱翌民, 黄勃, 王忠震, 巨家骥, 朱良奇. 隔离措施对COVID-19疫情控制的模型分析[J]. 武汉大学学报(理学版), 2020, 66(5): 442-450.
[3] 曹盛力, 冯沛华, 时朋朋. 修正SEIR传染病动力学模型应用于湖北省2019冠状病毒病(COVID-19)疫情预测和评估[J]. 浙江大学学报(医学版), 2020, 49(2): 178-184.
[4] Mwalili, S., Kimathi, M., Ojiambo, V., Gathungu, D. and Mbogo, R. (2020) SEIR Model for COVID-19 Dynamics Incorporating the Environment and Social Distancing. BMC Research Notes, 13, Article No. 352. [Google Scholar] [CrossRef] [PubMed]
[5] Lyra, W., Do Nascimento, J.D., Belkhiria, J., de Almeida, L., Chrispim, P.P.M. and de Andrade, I. (2020) COVID-19 Pandemics Modeling with Modified Determinist SEIR, Social Distancing, and Age Stratification: The Effect of Vertical Confinement and Release in Brazil. PLoS ONE, 15, e0237627. [Google Scholar] [CrossRef] [PubMed]
[6] Legido-Quigley, H., Asgari, N., Teo, Y.Y., Leung, G.M., Oshitani, H., Fukuda, K., et al. (2020) Are High-Performing Health Systems Resilient against the COVID-19 Epidemic? The Lancet, 395, 848-850. [Google Scholar] [CrossRef
[7] Khyar, O. and Allali, K. (2020) Global Dynamics of a Multi-Strain SEIR Epidemic Model with General Incidence Rates: Application to COVID-19 Pandemic. Nonlinear Dynamics, 102, 489-509. [Google Scholar] [CrossRef] [PubMed]
[8] 马艳丽, 褚正清, 聂东明. 具有饱和接触率的SI传染病模型的稳定性分析[J]. 长春师范大学学报, 2020, 39(5): 1-5.
[9] 鲁银霞, 廖新元, 陈会利, 李佳季. 一类随机离散的SIR流行病模型解的稳定性分析[J]. 南华大学学报(自然科学版), 2019, 33(1): 58-61.
[10] 陈姗姗, 黄勃, 方志军. 传染病垂直传染的传播动力学分析:以COVID-19为例[J]. 武汉大学学报(理学版), 2020, 66(5): 433-441.
[11] Asif, M., Ali Khan, Z., Haider, N. and Al-Mdallal, Q. (2020) Numerical Simulation for Solution of SEIR Models by Meshless and Finite Difference Methods. Chaos, Solitons & Fractals, 141, Article ID: 110340. [Google Scholar] [CrossRef
[12] 秦军. Runge-Kutta法在求解微分方程模型中的应用[D]: [硕士学位论文]. 合肥: 安徽大学, 2010.
[13] Liu, P.Y., He, S., Rong, L.B. and Tang, S.-Y. (2020) The Effect of Control Measures on COVID-19 Transmission in Italy: Comparison with Guangdong Province in China. Infectious Diseases of Poverty, 9, Article No. 130. [Google Scholar] [CrossRef] [PubMed]
[14] Xu, C., Yu, Y., Chen, Y. and Lu, Z. (2020) Forecast Analysis of the Epidemics Trend of COVID-19 in the USA by a Generalized Fractional-Order SEIR Model. Nonlinear Dynamics, 101, 1621-1634. [Google Scholar] [CrossRef] [PubMed]
[15] Inter-Agency Standing Committee (IASC) (2020) The Humanitarian Data Exchange. Inter-Agency Standing Committee, Geneva.
https://data.humdata.org/
[16] 1 Point 3 Acres (2010) Global COVID-19 Tracker & Interactive Charts.
https://coronavirus.1point3acres.com/