基于接种的新型冠状病毒模型及其分析:以上海为例
Novel Coronavirus Model Based on Vaccination and Its Analysis: A Case of Shanghai
摘要: 新冠病毒感染是一种由新型冠状病毒引起的急性传染性疾病,自2019年12月新冠病毒感染爆发以来,不仅危及人类的生命健康,还对全世界的经济发展造成了严重的影响,由于新冠病毒可在复制过程中不断适应宿主而产生突变,其具有更强的传播力和免疫逃逸能力,导致其对疫苗的敏感性降低,接种疫苗的人群仍旧有感染新冠病毒的风险,同时接种的疫苗在一定时间后会对人体的保护作用逐渐减弱,导致疫苗的有效性逐渐减弱,因此研究接种疫苗对新冠病毒的影响具有一定的意义,本文将接种人群单独考虑为一个仓室建立了数学模型。文中给出了模型的基本再生数以及正平衡点,通过构造Lyapunov函数证明了无病平衡点以及Rv > 1时唯一的正平衡点的稳定性。以上海2022年2月26日~5月31日的数据为例,结合当时上海所采取的政策,对模型进行数据拟合,并给出了上海在此期间的基本再生数为2.5709、5.1583、0.2982,最后通过数值模拟表明:提高每日的接种率、增加初始接种比例、能够有效降低确诊病例,从而降低新冠病毒的传染规模。但是即使初始接种比例或每日接种比例为100%,并不能完全阻断新冠病毒的传播。
Abstract: Novel coronavirus infection is an acute infectious disease caused by novel coronavirus. Since novel coronavirus infection broke out in December 2019, it not only endangers human life and health, but also has a serious impact on the economic development of the whole world. As novel coronavirus can constantly adapt to the host in the process of replication, it has a stronger transmission power and immune escape ability. Resulting in the reduction of its sensitivity to the vaccine, the vaccinat-ed population is still at risk of infection with novel coronavirus. At the same time, the protective ef-fect of the vaccinated vaccine on the human body will gradually weaken after a certain period of time, resulting in the gradual weakening of the effectiveness of the vaccine. Therefore, it is of certain significance to study the impact of vaccination on novel coronavirus. In this paper, the basic regen-eration number and the positive equilibrium point of the model are given, and the stability of the disease-free equilibrium point and the unique positive equilibrium point when Rv > 1 are proved by constructing the Lyapunov function. Taking the data of Shanghai from February 26 to May 31, 2022 as an example, and combining the policies adopted by Shanghai at that time, the model is fitted to the data. The basic regeneration numbers of Shanghai during this period are 2.5709, 5.1583 and 0.2982. Finally, numerical simulation shows that increasing the daily vaccination rate and increas-ing the initial vaccination rate can effectively reduce the confirmed cases, thereby reducing the in-fection scale of the new coronavirus. However, even if the initial vaccination rate or the daily vac-cination rate is 100%, it will not completely block the transmission of the novel coronavirus.
文章引用:宋彩霞, 柴玉珍, 李明涛, 刘军军. 基于接种的新型冠状病毒模型及其分析:以上海为例[J]. 应用数学进展, 2023, 12(5): 2376-2391. https://doi.org/10.12677/AAM.2023.125240

参考文献

[1] https://www.who.int/zh/director-general/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020
[2] Gorbalenya, A.E., Baker, S.C., Baric, R.S., Groot, R.J.D. and Ziebuhr, J. (2020) The Species Severe acute respiratory Syndrome-related coronavirus: Classifying 2019-NCoV and Naming It SARS-CoV-2. Nature Microbiology, 5, 536-544. [Google Scholar] [CrossRef] [PubMed]
[3] http://www.chinanews.com/m/34/2020/0318/1388/globalfeiyan.html
[4] Callaway, E. (2021) Omicron Likely to Weaken COVID Vaccine Protection. Nature, 600, 367-368. [Google Scholar] [CrossRef] [PubMed]
[5] China Daily Global (2021) Experts: Omicron Likely No Worse than Other Variants. China Daily Global.
https://epaper.chinadaily.com.cn/a/202112/09/WS61b13202a31019b029ba2661.html
[6] Ren, S.Y., Wang, W.B., Gao, R.D., et al. (2022) Omicron Variant (B.1.1.529) of SARSCoV-2: Mutation, Infectivity, Transmission and Vaccine Resistance. World Journal of Clinical Cases, 10, 1-11. [Google Scholar] [CrossRef] [PubMed]
[7] Levine-Tiefenbrun, M., Yelin, I., Katz, R., Herzel, E., Golan, Z., Schreiber, L., et al. (2021) Initial Report of Decreased SARS-CoV-2 Viral Load after Inoculation with the BNT162b2 Vaccine. Nature Medicine, 27, 790-792. [Google Scholar] [CrossRef] [PubMed]
[8] Young, G., Xiao, P., Newcomb, K. and Michael, E. (2021) In-terplay between COVID-19 Vaccines and social Measures for Ending the SARS-CoV-2 Pandemic. F1000 Research, 10, 803 [Google Scholar] [CrossRef
[9] Cai, J., Deng, X., Yang, J., et al. (2022) Modeling Transmission of SARS-CoV-2 Omicron in China. Nature Medicine, 28, 1468-1475. [Google Scholar] [CrossRef] [PubMed]
[10] Sun, G.Q., Ma, X., Zhang, Z., Liu, Q.-H. and Li, B.-L. (2022) What Is the Role of Aerosol Transmission in SARS- Cov-2 Omicron Spread in Shanghai? BMC Infectious Diseases, 22, Article No. 880. [Google Scholar] [CrossRef] [PubMed]
[11] Yang, B., Yu, Z. and Cai, Y. (2022) The Impact of Vaccination on the Spread of COVID-19: Studying by a Mathematical Model. Physica A: Statistical Mechanics and its Applications, 590, Article ID: 126717. [Google Scholar] [CrossRef] [PubMed]
[12] Khan, T., Ullah, R., Zaman, G. and El Khatib, Y. (2021) Model-ing the Dynamics of the SARS-CoV-2 Virus in a Population with Asymptomatic and Symptomatic Infected Individuals and Vaccination. Physica Scripta, 96, Article ID: 104009. [Google Scholar] [CrossRef
[13] Turkyilmazoglu, M. (2022) An Extended Epidemic Model with Vaccination: Weak-Immune SIRVI. Physica A: Statistical Mechanics and Its Applications, 598, Article ID: 127429. [Google Scholar] [CrossRef] [PubMed]
[14] Arino, J. and Milliken, E. (2022) Bistability in Deterministic and Stochastic SLIAR-Type Models with Imperfect and Waning Vaccine Protection. Journal of Mathematical Biology, 84, Article No. 61. [Google Scholar] [CrossRef] [PubMed]
[15] van den Driessche, P. and Watmough. J. (2002) Reproduction Numbers and Sub-Threshold Endemic Equilibria for Compartmental Models of Disease Transmission. Mathematical Bi-osciences, 180, 29-48. [Google Scholar] [CrossRef
[16] Diekmann, O., Heesterbeek, J.A.P. and Roberts, M.G. (2010) The Construction of Next-Generation Matrices for Compartmental Epidemic Models. Journal of the Royal Society Inter-face, 7, 873-885. [Google Scholar] [CrossRef] [PubMed]
[17] Lasalle, J.P. (1976) The Stability of Dynamical Systems. Society for Industrial and Applied Mathematics, Philadelphia.
[18] Shanghai Municipal Health Commission.
https://wsjkw.sh.gov.cn/xwfb/index.html
[19] National Center for Immunization and Respiratory Diseases (U.S.). Division of Viral Diseases (2020) COVID-19 Pandemic Planning Scenarios. Tech Report. Center for Disease Control.
https://stacks.cdc.gov/view/cdc/88617
[20]
https://wap.ceidata.cei.cn/detail?id=zuauf8GClws%3D
[21] Gamerman, D. and Lopes, H.F. (2006) Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition, CRC Press, New York. [Google Scholar] [CrossRef
[22] Martcheva, M. (2015) An Introduc-tion to Mathematical Epidemiology, Springer, New York. [Google Scholar] [CrossRef