Research article

Role of media coverage in a SVEIR-I epidemic model with nonlinear incidence and spatial heterogeneous environment


  • Received: 10 June 2023 Revised: 16 July 2023 Accepted: 21 July 2023 Published: 28 July 2023
  • In this paper, we propose a SVEIR-I epidemic model with media coverage in a spatially heterogeneous environment, and study the role of media coverage in the spread of diseases in a spatially heterogeneous environment. In a spatially heterogeneous environment, we first set up the well-posedness of the model. Then, we define the basic reproduction number $ R_0 $ of the model and establish the global dynamic threshold criteria: when $ R_0 < 1 $, disease-free steady state is globally asymptotically stable, while when $ R_0 > 1 $, the model is uniformly persistent. In addition, the existence and uniqueness of the equilibrium state of endemic diseases were obtained when $ R_0 > 1 $ in homogeneous space and heterogeneous diffusion environment. Further, by constructing appropriate Lyapunov functions, the global asymptotic stability of disease-free and positive steady states was established. Finally, through numerical simulations, it is shown that spatial heterogeneity can increase the risk of disease transmission, and can even change the threshold for disease transmission; media coverage can make people more widely understand disease information, and then reduce the effective contact rate to control the spread of disease.

    Citation: Pengfei Liu, Yantao Luo, Zhidong Teng. Role of media coverage in a SVEIR-I epidemic model with nonlinear incidence and spatial heterogeneous environment[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 15641-15671. doi: 10.3934/mbe.2023698

    Related Papers:

  • In this paper, we propose a SVEIR-I epidemic model with media coverage in a spatially heterogeneous environment, and study the role of media coverage in the spread of diseases in a spatially heterogeneous environment. In a spatially heterogeneous environment, we first set up the well-posedness of the model. Then, we define the basic reproduction number $ R_0 $ of the model and establish the global dynamic threshold criteria: when $ R_0 < 1 $, disease-free steady state is globally asymptotically stable, while when $ R_0 > 1 $, the model is uniformly persistent. In addition, the existence and uniqueness of the equilibrium state of endemic diseases were obtained when $ R_0 > 1 $ in homogeneous space and heterogeneous diffusion environment. Further, by constructing appropriate Lyapunov functions, the global asymptotic stability of disease-free and positive steady states was established. Finally, through numerical simulations, it is shown that spatial heterogeneity can increase the risk of disease transmission, and can even change the threshold for disease transmission; media coverage can make people more widely understand disease information, and then reduce the effective contact rate to control the spread of disease.



    加载中


    [1] D. L. Smith, J. Dushoff, F. E. McKenzie, The risk of a mosquito-borne infectionin a heterogeneous environment, PLoS Biol., 2 (2004), e368. https://doi.org/10.1371/journal.pbio.0020368 doi: 10.1371/journal.pbio.0020368
    [2] X. Wang, X. Q. Zhao, J. Wang, A cholera epidemic model in a spatiotemporally heterogeneous environment, J. Math. Anal. Appl., 468 (2018), 893–912. https://doi.org/10.1016/j.jmaa.2018.08.039 doi: 10.1016/j.jmaa.2018.08.039
    [3] Y. Cai, X. Lian, Z. Peng, W. Wang, Spatiotemporal transmission dynamics for influenza disease in a heterogenous environment, Nonlinear Anal. Real World Appl., 46 (2019), 178–194. https://doi.org/10.1016/j.nonrwa.2018.09.006 doi: 10.1016/j.nonrwa.2018.09.006
    [4] Y. Luo, Z. Teng, X. Q. Zhao, Transmission dynamics of a general temporal-spatial vector-host epidemic model with an application to the dengue fever in Guangdong, China, Discrete Contin. Dyn. Syst. Ser. B, 28 (2023), 134–169. https://doi.org/10.3934/dcdsb.2022069 doi: 10.3934/dcdsb.2022069
    [5] T. Zheng, L. Nie, H. Zhu, Y. Luo, Z. Teng, Role of seasonality and spatial heterogeneous in the transmission dynamics of avian influenza, Nonlinear Anal. Real World Appl., 67 (2022), 103567. https://doi.org/10.1016/j.nonrwa.2022.103567 doi: 10.1016/j.nonrwa.2022.103567
    [6] J. Wang, J. Wang, Analysis of a reaction-diffusion cholera model with distinct dispersal rates in the human population, J. Dyn. Differ. Equations, 33 (2021), 549–575. https://doi.org/10.1007/s10884-019-09820-8 doi: 10.1007/s10884-019-09820-8
    [7] J. Wang, F. Xie, T. Kuniya, Analysis of a reaction-diffusion cholera epidemic model in a spatially heterogeneous environment, Commun. Nonlinear Sci. Numerical Simul., 80 (2020), 104951. https://doi.org/10.1016/j.cnsns.2019.104951 doi: 10.1016/j.cnsns.2019.104951
    [8] C. Zhang, J. Gao, H. Sun, J. Wang, Dynamics of a reaction-diffusion SVIR model in a spatial heterogeneous environment, Phys. A Stat. Mech. Appl., 533 (2019), 122049. https://doi.org/10.1016/j.physa.2019.122049 doi: 10.1016/j.physa.2019.122049
    [9] Y. Luo, S. Tang, Z. Teng, L. Zhang, Global dynamics in a reaction-diffusion multi-group SIR epidemic model with nonlinear incidence, Nonlinear Anal. Real World Appl., 50 (2019), 365–385. https://doi.org/10.1016/j.nonrwa.2019.05.008 doi: 10.1016/j.nonrwa.2019.05.008
    [10] Y. Luo, L. Zhang, Z. Teng, et al., Analysis of a general multi-group reaction-diffusion epidemic model with nonlinear incidence and temporary acquired immunity, Math. Comput. Simul., 182 (2021), 428–455. https://doi.org/10.1016/j.matcom.2020.11.002 doi: 10.1016/j.matcom.2020.11.002
    [11] H. Zhao, Y. Shi, X. Zhang, Dynamic analysis of a malaria reaction-diffusion model with periodic delays and vector bias, Math. Biosci. Eng., 19 (2022), 2538–2574. https://doi.org/10.3934/mbe.2022117 doi: 10.3934/mbe.2022117
    [12] J. Wang, B. Dai, Qualitative analysis on a reaction-diffusion host-pathogen model with incubation period and nonlinear incidence rate, J. Math. Anal. Appl., 514 (2022), 126322. https://doi.org/10.1016/j.jmaa.2022.126322 doi: 10.1016/j.jmaa.2022.126322
    [13] A. Marzano, S. Gaia, V. Ghisetti, et al., Viral load at the time of liver transplantation and risk of hepatitis B virus recurrence, Liver Transplant., 11 (2005), 402–409. https://doi.org/10.1002/lt.20402 doi: 10.1002/lt.20402
    [14] A. Y. Kim, J. Schulze zur Wiesch, T. Kuntzen, J. Timm, D. E. Kaufmann, J. E. Duncan, Impaired hepatitis C virus-specific T cell responses and recurrent hepatitis C virus in HIV coinfection, PLoS Med., 3 (2006), e492. https://doi.org/10.1371/journal.pmed.0030492 doi: 10.1371/journal.pmed.0030492
    [15] M. L. Lambert, E. Hasker, A. Van Deun, D. Roberfroid, M. Boelaert, P. Van der Stuyft, Recurrence in tuberculosis: relapse or reinfection?, Lancet Infect. Dis., 3 (2003), 282–287. https://doi.org/10.1016/S1473-3099(03)00607-8 doi: 10.1016/S1473-3099(03)00607-8
    [16] D. W. Kimberlin, D. J. Rouse, Genital herpes, New Eng. J. Med., 350 (2004), 1970–1977. https://doi.org/10.1056/NEJMcp023065
    [17] J. Benedetti, L. Corey, R. Ashley, Recurrence rates in genital herpes after symptomatic first-episode infection, Annals Int. Med., 121 (1994), 847–854. https://doi.org/10.7326/0003-4819-121-11-199412010-00004 doi: 10.7326/0003-4819-121-11-199412010-00004
    [18] P. Van den Driessche, L. Wang, X. Zou, Modeling diseases with latency and relapse, Math. Biosci. Eng., 4 (2007), 205. https://doi.org/10.3934/mbe.2007.4.205 doi: 10.3934/mbe.2007.4.205
    [19] M. Ghosh, S. Olaniyi, O. S. Obabiyi, Mathematical analysis of reinfection and relapse in malaria dynamics, Appl. Math. Comput., 373 (2020), 125044. https://doi.org/10.1016/j.amc.2020.125044 doi: 10.1016/j.amc.2020.125044
    [20] S. Liu, S. Wang, L. Wang, Global dynamics of delay epidemic models with nonlinear incidence rate and relapse, Nonlinear Anal. Real World Appl., 12 (2011), 119–127. https://doi.org/10.1016/j.nonrwa.2010.06.001 doi: 10.1016/j.nonrwa.2010.06.001
    [21] C. Vargas-De-Leon, On the global stability of infectious diseases models with relapse, Abstraction Appl. Mag., 9 (2014).
    [22] Y. Chen, J. Li, S. Zou, Global dynamics of an epidemic model with relapse and nonlinear incidence, Math. Methods Appl. Sci., 42 (2019), 1283–1291. https://doi.org/10.1002/mma.5439 doi: 10.1002/mma.5439
    [23] A. Lahrouz, H. El Mahjour, A. Settati, A. Bernoussi, Dynamics and optimal control of a non-linear epidemic model with relapse and cure, Phys. A Stat.l Mech. Appl., 496 (2018), 299–317. https://doi.org/10.1016/j.physa.2018.01.007 doi: 10.1016/j.physa.2018.01.007
    [24] D. Tudor, A deterministic model for herpes infections in human and animal populations, Siam Rev., 32 (1990), 136–139. https://doi.org/10.1137/1032003 doi: 10.1137/1032003
    [25] T. K. Kar, S. K. Nandi, S. Jana, M. Mandal, Stability and bifurcation analysis of an epidemic model with the effect of media, Chaos Solitons Fractals, 120 (2019), 188–199. https://doi.org/10.1016/j.chaos.2019.01.025 doi: 10.1016/j.chaos.2019.01.025
    [26] J. Cui, Y. Sun, H. Zhu, The impact of media on the control of infectious diseases. J. Dyn. Differ. Equations, 20 (2008), 31–53. https://doi.org/10.1007/s10884-007-9075-0
    [27] D. K. Das, S. Khajanchi, T. K. Kar, The impact of the media awareness and optimal strategy on the prevalence of tuberculosis, Appl. Math. Comput., 366 (2020), 124732. https://doi.org/10.1016/j.amc.2019.124732 doi: 10.1016/j.amc.2019.124732
    [28] S. M. Salman, Memory and media coverage effect on an HIV/AIDS epidemic model with treatment, J. Comput. Appl. Math., 385 (2021), 113203. https://doi.org/10.1016/j.cam.2020.113203 doi: 10.1016/j.cam.2020.113203
    [29] X. Wang, D. Gao, J. Wang, Influence of human behavior on cholera dynamics, Math. Biosci., 267 (2015), 41–52. https://doi.org/10.1016/j.mbs.2015.06.009 doi: 10.1016/j.mbs.2015.06.009
    [30] L. Wang, Z. Liu, X. Zhang, Global dynamics for an age-structured epidemic model with media impact and incomplete vaccination, Nonlinear Anal. Real World Appl., 32 (2016), 136–158. https://doi.org/10.1016/j.nonrwa.2016.04.009 doi: 10.1016/j.nonrwa.2016.04.009
    [31] R. K. Rai, A. K. Misra, Y. Takeuchi, Modeling the impact of sanitation and awareness on the spread of infectious diseases, Math. Biosci. Eng., 16 (2019), 667–700. https://doi.org/10.3934/mbe.2019032 doi: 10.3934/mbe.2019032
    [32] P. Song, Y. Xiao, Analysis of a diffusive epidemic system with spatial heterogeneity and lag effect of media impact, J. Math. Biol., 85 (2022), 17. https://doi.org/10.1007/s00285-022-01780-w doi: 10.1007/s00285-022-01780-w
    [33] D. P. Oran, E. J. Topol, The proportion of SARS-CoV-2 infections that are asymptomatic: a systematic review, Annals Int. Med., 174 (2021), 655–662. https://doi.org/10.7326/M20-6976 doi: 10.7326/M20-6976
    [34] M. Day, Covid-19: identifying and isolating asymptomatic people helped eliminate virus in Italian village, BMJ British Med. J., 368 (2020).
    [35] L. Wang, Z. Liu, C. Guo, Y. Li, X. Zhang, New global dynamical results and application of several SVEIS epidemic models with temporary immunity, Appl. Math. Comput., 390 (2021), 125648. https://doi.org/10.1016/j.amc.2020.125648 doi: 10.1016/j.amc.2020.125648
    [36] S. Zhao, L. Stone, D. Gao, D. He, Modelling the large-scale yellow fever outbreak in Luanda, Angola, and the impact of vaccination, PLoS Neglected Trop. Dis., 12 (2018), e0006158. https://doi.org/10.1371/journal.pntd.0006158 doi: 10.1371/journal.pntd.0006158
    [37] C. C. Zhu, J. Zhu, X. L. Liu, Influence of spatial heterogeneous environment on long-term dynamics of a reaction-diffusion SVIR epidemic model with relapse, Math. Biosci. Eng., 16 (2019), 5897–5922. https://doi.org/10.3934/mbe.2019295 doi: 10.3934/mbe.2019295
    [38] H. L. Smith, Monotone Dynamical Systems: An Introduction to the Theory of Competitive and Cooperative Systems, in Mathematical Surveys And Monographs, Providence, RI, 1995.
    [39] Y. Lou, X. Q, Zhao, A reaction-diffusion malaria model with incubation period in the vector population, J. Math. Biol., 62 (2011), 543–568. https://doi.org/10.1007/s00285-010-0346-8 doi: 10.1007/s00285-010-0346-8
    [40] T. Zheng, Y. Luo, X. Zhou, L. Zhang, Z. Teng, Spatial dynamic analysis for COVID-19 epidemic model with diffusion and Beddington-DeAngelis type incidence, Commun. Pure Appl. Anal., 22 (2023), 365–396. https://doi.org/10.3934/cpaa.2021154 doi: 10.3934/cpaa.2021154
    [41] M. H. Protter, H. F. Weinberger, Maximum Principles in Differential Equations, Prentice Hall, Englewood Cliffs, 1967.
    [42] H. Amann, Fixed point equations and nonlinear eigenvalue problems in ordered Banach spaces, SIAM Rev., 18 (1976), 620–709. https://doi.org/10.1137/1018114 doi: 10.1137/1018114
    [43] J. Groeger, Divergence theorems and the supersphere, J. Geom. Phys., 77 (2014), 13–29. https://doi.org/10.1016/j.geomphys.2013.11.004 doi: 10.1016/j.geomphys.2013.11.004
    [44] J. Wu, Theory and applications of partial functional differential equations, Springer Science - Business Media, 1996.
    [45] X. Q. Zhao, Dynamical systems in population biology, Springer, 2003.
    [46] H. R. Thieme, Spectral bound and reproduction number for infinite-dimensional population structure and time heterogeneity, SIAM J. Appl. Math., 70 (2009), 29–48. https://doi.org/10.1137/080732870 doi: 10.1137/080732870
    [47] W. Wang, X. Q. Zhao, Basic reproduction numbers for reaction-diffusion epidemic models, SIAM J. Appl. Dyn. Syst., (2012), 1652–1673. https://doi.org/10.1137/120872942
    [48] O. Diekmann, J. A. P. Heesterbeek, J. A. J. Metz, On the definition and the computation of the basic reproduction ratio $R_0$ in the models for infectious disease in heterogeneous populations, J. Math. Biol., 28 (1990), 365–382. https://doi.org/10.1007/BF00178324 doi: 10.1007/BF00178324
    [49] P. V. Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6 doi: 10.1016/S0025-5564(02)00108-6
    [50] H. R. Thieme, Convergence results and a Poincare-Bendixson trichoyomy for asymptotically autonomous differential equations, J. Math. Biol., 30 (1992), 755–763. https://doi.org/10.1007/BF00173267 doi: 10.1007/BF00173267
    [51] H. L. Smith, X. Q. Zhao, Robust persistence for semidynamical systems, Nonlinear Anal., 47 (2001), 6169–6179. https://doi.org/10.1016/S0362-546X(01)00678-2 doi: 10.1016/S0362-546X(01)00678-2
    [52] P. Magal, X. Q. Zhao, Global attractors and steady states for uniformly persistent dynamical systems, SIAM J. Math. Anal., 37 (2005), 251–275. https://doi.org/10.1137/S0036141003439173 doi: 10.1137/S0036141003439173
  • Reader Comments
  • © 2023 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(979) PDF downloads(162) Cited by(0)

Article outline

Figures and Tables

Figures(9)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog