Research article Special Issues

Modeling and analysis of COVID-19 based on a time delay dynamic model

  • Received: 30 August 2020 Accepted: 27 October 2020 Published: 24 November 2020
  • The new type of coronavirus pneumonia is caused by the new type of coronavirus which appeared at the end of 2019. Because of its strong contagiousness, rapid spread and great harm, it has already given countries around the world serious effects. So far there is no clear specific drug. Scientifically grasping the development law of epidemics is extremely important for preventing and controlling epidemics. Since the latent of this epidemic are also highly contagious, traditional infectious disease models cannot accurately describe the regularity of this epidemic transmission. Based on the traditional infectious disease model, an infectious disease model with a time delay is proposed. The time difference is used to characterize the cycle of viral infection and treatment time. Using the epidemic data released in real time, firstly, through the numerical simulation parameter inversion, the minimum error is obtained; then we simulate the development trend of the epidemic according to the dynamics system; finally, we compare and analyze the effectiveness of isolation measures. This article has simulated COVID-19 and analyzed the development of the epidemic in Beijing and Wuhan. By comparing the severity of the epidemic in the two regions, early detection and isolation are still the top priority of epidemic prevention and control.

    Citation: Cong Yang, Yali Yang, Zhiwei Li, Lisheng Zhang. Modeling and analysis of COVID-19 based on a time delay dynamic model[J]. Mathematical Biosciences and Engineering, 2021, 18(1): 154-165. doi: 10.3934/mbe.2021008

    Related Papers:

  • The new type of coronavirus pneumonia is caused by the new type of coronavirus which appeared at the end of 2019. Because of its strong contagiousness, rapid spread and great harm, it has already given countries around the world serious effects. So far there is no clear specific drug. Scientifically grasping the development law of epidemics is extremely important for preventing and controlling epidemics. Since the latent of this epidemic are also highly contagious, traditional infectious disease models cannot accurately describe the regularity of this epidemic transmission. Based on the traditional infectious disease model, an infectious disease model with a time delay is proposed. The time difference is used to characterize the cycle of viral infection and treatment time. Using the epidemic data released in real time, firstly, through the numerical simulation parameter inversion, the minimum error is obtained; then we simulate the development trend of the epidemic according to the dynamics system; finally, we compare and analyze the effectiveness of isolation measures. This article has simulated COVID-19 and analyzed the development of the epidemic in Beijing and Wuhan. By comparing the severity of the epidemic in the two regions, early detection and isolation are still the top priority of epidemic prevention and control.


    加载中


    [1] World Health Organization, Coronavirus, 2020. Available from: https://www.who.int/health-topics/coronavirus.
    [2] World Health Organization, Situation Report, 2020. Available from: https://www.who.int/docs/default/source/coronaviruse/situation-reports.
    [3] R. M. Anderson, R. M. May, Infectious diseases of humans: Dynamics and control, Oxford University Press, Oxford, 1991.
    [4] M. J. Keeling, P. Rohnai, Modeling infectious diseases in humans and animals, Princeton University Press, Princeton, 2011.
    [5] M. Gilbert, G. Pullano, F. Pinotti, E. Valdano, C. Poletto, P. Boelle, et al., Preparedness and vulnerability of African countries against importations of COVID-19 : a modelling study. Lancet, 395 (2020), 871-877. doi: 10.1016/S0140-6736(20)30411-6
    [6] Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, et al., Early Transmission Dynamics in Wuhan, China, of Novel Coronavious-Infected Pneumonia, N. Engl. J. Med., 382 (2020), 1199-1207. doi: 10.1056/NEJMoa2001316
    [7] B. Tang, X. Wang, Q. Li, N. L. Bragazzi, S. Y. Tang, Y. Xiao, et al., Estimation of the transmission risk of 2019-nCov and its implication for public health interventions, J. Clin. Med., 9 (2020), 462. doi: 10.3390/jcm9020462
    [8] B. Tang, F. Xia, S. Y. Tang, N. L. Bragazzi, Q. Li, X. Sun, et al., The evolution of quarantined and suspected cases determines the final trend of the 2019-nCoV epidemics based on multi-source data analyses, Available at SSRN, (2020), 3537099.
    [9] Special Expert Group for Control of the Epidemic of Novel Coronavirus Pneumonia of the Chinese Preventive Medicine Association, An update on the epidemiological characteristics of novel coronavirus pneumonia (COVID-19), Chin. J. Epidemiol., 41 (2020), 139-144.
    [10] Y. Liu, A. A. Gayle, A. Wilder-Smith, J. Rocklov, The reproductive number of COVID-19 is higher compared to SARS coronavirus, J. Travel Med., 27 (2020), 32052846.
    [11] X. Wang, S. Y. Tang, N. L, Bragazzi, When will be the resumption of work in Wuhan and its surrounding areas during COVID-19 epidemic? A data-driven network modeling analysis, Sci. Sin. Math., 50 (2020), 969. doi: 10.1360/SSM-2020-0037
    [12] S. Y. Tang, B. Tang, N. L. Bragazzi, F. Xia, Analysis of COVID-19 epidemic traced data and stochastic discrete transmission dynamic model, Sci. Sin. Math., 50 (2020), 1071. doi: 10.1360/SSM-2020-0053
    [13] B. Cantó, C. Coll, E.Sánchez, Estimation of parameters in a structured SIR model, Adv. Differ. Equ., 1 (2017), 33.
    [14] Y. Chen, J. Cheng, Y. Jiang, K. Liu, A time delay dynamical model for outbreak of 2019-nCoV and the parameter identification, J. Inverse Ill-posed Probl., 28 (2020), 243-250. doi: 10.1515/jiip-2020-0010
    [15] M. Gatto, E. Bertuzzo, L. Mari, S. Miccoli, L. Carraro, R. Casagrandi, et al., Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures, Proc. Natl. Acad. Sci., 117 (2020), 10484-10491. doi: 10.1073/pnas.2004978117
    [16] J. T. Wu, K. Leung, G. M. Leung, Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study, Lancet, 395 (2020), 689-697. doi: 10.1016/S0140-6736(20)30260-9
    [17] National Health Commission of the People's Republic of China, 2020. Available from: http://www.nhc.gov.cn.
    [18] A. J. Kucharski, T. W. Russell, C. Diamond, Y. Liu, J. Edmunds, S. Funk, et al., Early dynamics of transmission and control of COVID-19: a mathematical modelling study, Lancet. Infect. Dis., 20 (2020), 553-558. doi: 10.1016/S1473-3099(20)30144-4
    [19] J. Wallinga, P. Teunis, Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures, Am. J. Epidemiol., 160 (2004), 509-516. doi: 10.1093/aje/kwh255
    [20] N. Chintalapudi, G. Battineni, G. G. Sagaro, F. Amenta, COVID-19 outbreak reproduction number estimations and forecasting in Marche, Italy, Int. J. Infect. Dis., 96 (2020), 327-333. doi: 10.1016/j.ijid.2020.05.029
    [21] M. Coccia, An index to quantify environmental risk of exposure to future epidemics of the COVID-19 and similar viral agents: Theory and Practice, Environ. Res., (2020), 110155.
    [22] M. Coccia, Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID, Sci. Total Environ., (2020), 138474.
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(916) PDF downloads(218) Cited by(0)

Article outline

Figures and Tables

Figures(5)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog