Research article Special Issues

Effect of decay behavior of information on disease dissemination in multiplex network


  • Received: 22 September 2022 Revised: 26 November 2022 Accepted: 05 December 2022 Published: 26 December 2022
  • The diseases dissemination always brings serious problems in the economy and livelihood issues. It is necessary to study the law of disease dissemination from multiple dimensions. Information quality about disease prevention has a great impact on the dissemination of disease, that is because only the real information can inhibit the dissemination of disease. In fact, the dissemination of information involves the decay of the amount of real information and the information quality becomes poor gradually, which will affect the individual's attitude and behavior towards disease. In order to study the influence of the decay behavior of information on disease dissemination, in the paper, an interaction model between information and disease dissemination is established to describe the effect of the decay behavior of information on the coupled dynamics of process in multiplex network. According to the mean-field theory, the threshold condition of disease dissemination is derived. Finally, through theoretical analysis and numerical simulation, some results can be obtained. The results show that decay behavior is a factor that greatly affects the disease dissemination and can change the final size of disease dissemination. The larger the decay constant, the smaller final size of disease dissemination. In the process of information dissemination, emphasizing key information can reduce the impact of decay behavior.

    Citation: Liang'an Huo, Shiguang Meng. Effect of decay behavior of information on disease dissemination in multiplex network[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 4516-4531. doi: 10.3934/mbe.2023209

    Related Papers:

  • The diseases dissemination always brings serious problems in the economy and livelihood issues. It is necessary to study the law of disease dissemination from multiple dimensions. Information quality about disease prevention has a great impact on the dissemination of disease, that is because only the real information can inhibit the dissemination of disease. In fact, the dissemination of information involves the decay of the amount of real information and the information quality becomes poor gradually, which will affect the individual's attitude and behavior towards disease. In order to study the influence of the decay behavior of information on disease dissemination, in the paper, an interaction model between information and disease dissemination is established to describe the effect of the decay behavior of information on the coupled dynamics of process in multiplex network. According to the mean-field theory, the threshold condition of disease dissemination is derived. Finally, through theoretical analysis and numerical simulation, some results can be obtained. The results show that decay behavior is a factor that greatly affects the disease dissemination and can change the final size of disease dissemination. The larger the decay constant, the smaller final size of disease dissemination. In the process of information dissemination, emphasizing key information can reduce the impact of decay behavior.



    加载中


    [1] W. Li, L. Deng, J. Wang, The medical resources allocation problem based on an improved SEIRmodel with sharing behavior, Mod. Phys. Lett. B, 35 (2021), 2150517. https://doi.org/10.1142/S0217984921505175 doi: 10.1142/S0217984921505175
    [2] N. Petford, J. Campbell, Covid-19 mortality rates in Northamptonshire UK: Initial sub-regional comparisons and provisional SEIR model of first wave disease spread, Open Public Health J., 14 (2021), 218–224. https://doi.org/10.2174/1874944502114010218 doi: 10.2174/1874944502114010218
    [3] T. Kano, K. Yasui, T. Mikami, M. Asally, A. Ishiguro, An agent-based model of the interrelation between the COVID-19 outbreak and economic activities, Proc. R. Soc. A, 477 (2021), 20200604. https://doi.org/10.1098/rspa.2020.0604 doi: 10.1098/rspa.2020.0604
    [4] W. O. Kermack, A. G. McKendrick, Contributions to the mathematical theory of epidemics. Ⅱ.—The problem of endemicity, Proc. R. Soc. London Ser. A, 138 (1932), 55–83. https://doi.org/10.1098/rspa.1932.0171 doi: 10.1098/rspa.1932.0171
    [5] S. H. Strogatz, Exploring complex networks, Nature, 410 (2001), 268–276. https://doi.org/10.1038/35065725 doi: 10.1038/35065725
    [6] W. Wang, M. Tang, H. F. Zhang, H. Gao, Y. Do, Z. H. Liu, Epidemic spreading on complex networks with general degree and weight distributions, Phys. Rev. E, 90 (2014), 042803. https://doi.org/10.1103/PhysRevE.90.042803 doi: 10.1103/PhysRevE.90.042803
    [7] R. Pastor-Satorras, A. Vespignani, Epidemic dynamics and endemic states in complex networks, Phys. Rev. E, 63 (2001), 066117. https://doi.org/10.1103/PhysRevE.63.066117 doi: 10.1103/PhysRevE.63.066117
    [8] R. Pastor-Satorras, A. Vespignani, Epidemic spreading in scale-free networks, Phys. Rev. Lett., 86 (2001), 3200–3203. https://doi.org/10.1103/PhysRevLett.86.3200 doi: 10.1103/PhysRevLett.86.3200
    [9] R. Pastor-Satorras, C. Castellano, P. Van Mieghem, A. Vespignani, Epidemic processes in complex networks, Rev. Modern Phys., 87 (2015), 925–979. https://doi.org/10.1103/RevModPhys.87.925 doi: 10.1103/RevModPhys.87.925
    [10] M. E. J. Newman, Spread of epidemic disease on networks, Phys. Rev. E, 66 (2002), 016128. https://doi.org/10.1103/PhysRevE.66.016128 doi: 10.1103/PhysRevE.66.016128
    [11] J. Fan, Q. Yin, C. Xia, M. Perc, Epidemics on multilayer simplicial complexes, Proc. R. Soc. A, 478 (2022), 20220059. https://doi.org/10.1098/rspa.2022.0059 doi: 10.1098/rspa.2022.0059
    [12] Z. K. Zhang, C. Liu, X. X. Zhan, X. Lu, C. X. Zhang, Y. C. Zhang, Dynamics of information diffusion and its applications on complex networks, Phys. Rep., 651 (2016), 1–34. https://doi.org/10.1016/j.physrep.2016.07.002 doi: 10.1016/j.physrep.2016.07.002
    [13] X. Qiu, L. Zhao, J. Wang, X. Wang, Q. Wang, Effects of time-dependent diffusion behaviors on the rumor spreading in social networks, Phys. Lett. A, 380 (2016), 2054–2063. https://doi.org/10.1016/j.physleta.2016.04.025 doi: 10.1016/j.physleta.2016.04.025
    [14] R. J. Mei, L. Ding, X. M. An, P. Hu, Modeling for heterogeneous multi-stage information propagation networks and maximizing information, Chin. Phys. B, 28 (2019), 028701. https://doi.org/10.1088/1674-1056/28/2/028701 doi: 10.1088/1674-1056/28/2/028701
    [15] Y. Liu, B. Wang, B. Wu, S. Shang, Y. Zhang, C. Shi, Characterizing super-spreading in microblog: An epidemic-based information propagation model, Phys. A, 463 (2016), 202–218. https://doi.org/10.1016/j.physa.2016.07.022 doi: 10.1016/j.physa.2016.07.022
    [16] P. J. Mucha, T. Richardson, K. Macon, M. A. Porter, J. P. Onnela, Community structure in time-dependent, multiscale, and multiplex networks, Science, 328 (2010), 876–878. https://doi.org/10.1126/science.1184819 doi: 10.1126/science.1184819
    [17] F. Battiston, V. Nicosia, V. Latora, Structural measures for multiplex networks, Phys. Rev. E, 89 (2014), 032804. https://doi.org/10.1103/PhysRevE.89.032804 doi: 10.1103/PhysRevE.89.032804
    [18] C. Zheng, C. Xia, Q. Guo, M. Dehmer, Interplay between SIR-based disease spreading and awareness diffusion on multiplex networks, J. Parallel Distrib. Comput., 115 (2018), 20–28. https://doi.org/10.1016/j.jpdc.2018.01.001 doi: 10.1016/j.jpdc.2018.01.001
    [19] C. Xia, Z. Wang, C. Zheng, Q. Guo, Y. Shi, M. Dehmer, Z. Chen, A new coupled disease-awareness spreading model with mass media on multiplex networks, Inf. Sci., 471 (2019), 185–200. https://doi.org/10.1016/j.ins.2018.08.050 doi: 10.1016/j.ins.2018.08.050
    [20] R. Zhao, L. Zhao, Effects of official information and rumor on resource-epidemic coevolution dynamics, J. King Saud Univ. Comput. Inf. Sci., 2022 (2022). https://doi.org/10.1016/j.jksuci.2022.09.003 doi: 10.1016/j.jksuci.2022.09.003
    [21] Q. Guo, Y. Lei, C. Xia, L. Guo, X. Jiang, Z. Zheng, The role of node heterogeneity in the coupled spreading of epidemics and awareness, PloS One, 11 (2016), e0161037. https://doi.org/10.1371/journal.pone.0161037 doi: 10.1371/journal.pone.0161037
    [22] X. L. Peng, Y. D. Zhang, Contagion dynamics on adaptive multiplex networks with awarenessdependent rewiring, Chin. Phys. B, 30 (2021), 058901. https://doi.org/10.1088/1674-1056/abe1ab doi: 10.1088/1674-1056/abe1ab
    [23] C. Granell, S. Gómez, A. Arenas, Dynamical interplay between awareness and epidemic spreading in multiplex networks, Phys. Rev. Lett., 111 (2013), 128701. https://doi.org/10.1103/PhysRevLett.111.128701 doi: 10.1103/PhysRevLett.111.128701
    [24] C. Granell, S. Gómez, A. Arenas, Competing spreading processes on multiplex networks: awareness and epidemics, Phys. Rev. E, 90 (2014), 012808. https://doi.org/10.1103/PhysRevE.90.012808 doi: 10.1103/PhysRevE.90.012808
    [25] Y. Shang, Modeling epidemic spread with awareness and heterogeneous transmission rates in networks, J. Biol. Phys., 39 (2013), 489–500. https://doi.org/10.1007/s10867-013-9318-8 doi: 10.1007/s10867-013-9318-8
    [26] W. Wang, M. Tang, H. Yang, Y. Do, Y. C. Lai, G. W. Lee, Asymmetrically interacting spreading dynamics on complex layered networks, Sci. Rep., 4 (2015), 5097. https://doi.org/10.1038/srep05097 doi: 10.1038/srep05097
    [27] H. F. Zhang, J. R. Xie, M. Tang, Y. C. Lai, Suppression of epidemic spreading in complex networks by local information based behavioral responses, Chaos Interdiscip. J. Nonlinear Sci., 24 (2014), 043106. https://doi.org/10.1063/1.4896333 doi: 10.1063/1.4896333
    [28] X. Nie, M. Tang, Y. Zou, S. Guan, J. Zhou, The impact of heterogeneous response on coupled spreading dynamics in multiplex networks, Phys. A, 484 (2017), 225–232. https://doi.org/10.1016/j.physa.2017.04.140 doi: 10.1016/j.physa.2017.04.140
    [29] C. Fan, Y. Jin, L. Huo, C. Liu, Y. Yang, Y. Wang, Effect of individual behavior on the interplay between awareness and disease spreading in multiplex networks, Phys. A, 461 (2016), 523–530. https://doi.org/10.1016/j.physa.2016.06.050 doi: 10.1016/j.physa.2016.06.050
    [30] P. Hu, D. Geng, T. Lin, L. Ding, Coupled propagation dynamics on multiplex activity-driven networks, Phys. A, 561 (2021), 125212. https://doi.org/10.1016/j.physa.2020.125212 doi: 10.1016/j.physa.2020.125212
    [31] P. R. Rich, M. S. Zaragoza, The continued influence of implied and explicitly stated misinformation in news reports, J. Exp. Psychol. Learn. Mem. Cognit., 42 (2016), 62–74. https://doi.org/10.1037/xlm0000155 doi: 10.1037/xlm0000155
    [32] E. Anastasiades, M. Argyrides, M. Mousoulidou, Misinformation about COVID-19: Psychological insights, Encyclopedia, 1 (2021), 1200–1214. https://doi.org/10.3390/encyclopedia1040091 doi: 10.3390/encyclopedia1040091
    [33] Y. Shang, Discrete-time epidemic dynamics with awareness in random networks, Int. J. Biomath., 6 (2013), 1350007. https://doi.org/10.1142/S1793524513500071 doi: 10.1142/S1793524513500071
    [34] S. Funk, V. A. A. Jansen, The talk of the town: modelling the spread of information and changes in behavior, in Modeling The Interplay Between Human Behavior And The Spread Of Infectious Diseases, Springer New York, (2013), 93–102. https://doi.org/10.1007/978-1-4614-5474-8_6
    [35] S. Funk, E. Gilad, C. Watkins, V. A. A. Jansen, The spread of awareness and its impact on epidemic outbreaks, Proc. Natl. Acad. Sci., 106 (2009), 6872–6877. https://doi.org/10.1073/pnas.0810762106 doi: 10.1073/pnas.0810762106
  • 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(1324) PDF downloads(94) Cited by(0)

Article outline

Figures and Tables

Figures(7)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog