| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
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Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels |
| Xiao-Xiao Xie(谢笑笑)1, Liang-An Huo(霍良安)1,2,†, Ya-Fang Dong(董雅芳)1, and Ying-Ying Cheng(程英英)3 |
1 Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
2 School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai 200093, China;
3 School of Management, Henan University of Science and Technology, Luoyang 471023, China |
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Abstract While the interaction between information and disease in static networks has been extensively investigated, many studies have ignored the characteristics of network evolution. In this study, we construct a new two-layer coupling model to explore the interactions between information and disease. The upper layer describes the diffusion of disease-related information, and the lower layer represents the disease transmission. We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation, revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network. Subsequently, we derive the disease outbreak threshold by using the microscopic Markov-chain approach (MMCA). Finally, we perform extensive Monte Carlo (MC) numerical simulations to verify the accuracy of our theoretical results. Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer. Moreover, reducing the damping factor can delay disease outbreaks and suppress disease transmission, while improving individual quarantine measures can contribute positively to disease control. This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.
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Received: 20 September 2023
Revised: 22 October 2023
Accepted manuscript online: 01 December 2023
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PACS:
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87.23.Kg
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(Dynamics of evolution)
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87.23.Ge
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(Dynamics of social systems)
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64.60.aq
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(Networks)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 72174121 and 71774111), the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, and the Project for the Natural Science Foundation of Shanghai, China (Grant No. 21ZR1444100). |
Corresponding Authors:
Liang-An Huo
E-mail: huohuolin@yeah.net
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Cite this article:
Xiao-Xiao Xie(谢笑笑), Liang-An Huo(霍良安), Ya-Fang Dong(董雅芳), and Ying-Ying Cheng(程英英) Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels 2024 Chin. Phys. B 33 038704
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[1] Tu Y and Meng X 2023 Math. Comput. Simul. 214 28
[2] Wang Q, Xiang K, Zhu C, et al. 2023 Math. Comput. Simul. 212 289
[3] Huo L and Gu J 2023 Physica A 609 128323
[4] Kermack W O and McKendrick A G 1927 Proc. R. Soc. A 115 700
[5] Gray A, Greenhalgh D, Hu L, et al. 2011 SIAM J. Appl. Math. 71 876
[6] McCluskey C C 2010 Nonlinear Anal.: Real World Appl. 11 55
[7] Van Mieghem P and Van de Bovenkamp R 2013 Phys. Rev. Lett. 110 108701
[8] Wang B, Gou M and Han Y 2021 Nonlinear Dyn. 105 3835
[9] Watts D J and Strogatz S H 1998 Nature 393 440
[10] Barabási A L and Albert R 1999 Science 286 509
[11] Pastor-Satorras R, Castellano C, Van Mieghem P, et al. 2015 Rev. Mod. Phys. 87 925
[12] Newman M E J 2003 SIAM Rev. 45 167
[13] Boccaletti S, Latora V, Moreno Y, et al. 2006 Phys. Rep. 424 175
[14] Chen J and Li X 2023 Sci. China Inf. Sci. 66 112205
[15] Silva D H, Anteneodo C and Ferreira S C 2023 Commun. Nonlinear Sci. Numerical Simul. 116 106877
[16] Albert R and Barabási A L 2002 Rev. Mod. Phys. 74 47
[17] Ma X, Shen S and Zhu L 2023 Inf. Sci. 622 1141
[18] Yin H, Zhang A and Zeng A 2023 Chaos, Solitons & Fractals 168 113103
[19] Pastorino L and Zanin M 2023 Aerospace 10 36
[20] Wang Y, Wei Z and Cao J 2020 Nonlinear Dyn. 101 1801
[21] Liu C, Wu X, Niu R, et al. 2020 Nonlinear Dyn. 101 1777
[22] Jagtap K D and Kandhway K 2022 Results Control Optim. 6 100078
[23] Huang J and Chen C 2022 Physica A 591 126692
[24] Zhu X, Wang Y, Zhang N, et al. 2022 Chaos 32 083124
[25] Zhao X, Zhou Q, Wang A, et al. 2021 J. Med. Virol. 93 4342
[26] Choi M, Choi Y, Kim S, et al. 2023 Int. J. Contemporary Hospitality Manag. 35 469
[27] Zhu L, Liu W and Zhang Z 2021 Math. Comput. Simul. 188 268
[28] Bu Y, Gregory S and Mills H L 2013 Phys. Rev. E 88 042801
[29] Zhou B, Jiang D, Han B, et al. 2022 Math. Comput. Simul. 196 15
[30] Yin Q, Wang Z and Xia C 2023 Nonlinear Dyn. 111 14583
[31] Feng M, Li X, Li Y, et al. 2023 Chaos 33 043112
[32] Funk S, Gilad E, Watkins C, et al. 2009 Proc. Natl. Acad. Sci. 106 6872
[33] Granell C, Gómez S and Arenas A 2013 Phys. Rev. Lett. 111 128701
[34] Granell C, Gómez S and Arenas A 2014 Phys. Rev. E 90 012808
[35] Ma W, Zhang P, Zhao X, et al. 2022 Physica A 588 126558
[36] Zhang L, Guo C and Feng M 2022 Chaos 32 083138
[37] Billingsley P 1961 The Annals of Mathematical Statistics 32 12
[38] Sharma S 2017 Annual Review of Astronomy and Astrophysics 55 213
[39] Yang H, Gu C, Tang M, et al. 2019 Applied Mathematical Modelling 75 806
[40] Li D, Xie W, Han D, et al. 2023 Information Sciences 651 119723
[41] Li W, Cai M, Zhong X, et al. 2023 Chaos, Solitons & Fractals 168 113102
[42] Gao C, Tang S, Li W, et al. 2018 Physica A 496 330
[43] Kotnis B and Kuri J 2013 Phys. Rev. E 87 062810
[44] Fan C, Jin Y, Huo L, et al. 2016 Physica A 461 523
[45] Putra R U, Basri H, Prakoso A T, et al. 2023 Sustainability 15 823
[46] Goodyear V A, Skinner B, McKeever J, et al. 2023 Phys. Ed. Sport Pedagogy 28 94
[47] Njozing B N, Edin K E, Sebastián M S, et al. 2011 BMC Int. Health Hum. Rights 11 1
[48] Kraemer M U G, Yang C H, Gutierrez B, et al. 2020 Science 368 493
[49] Guo H, Yin Q, Xia C, et al. 2021 Nonlinear Dyn. 105 3819
[50] Olinky R and Stone L 2004 Phys. Rev. E 70 030902
[51] Wu Q, Fu X, Zhang H, et al. 2020 Int. J. Mod. Phys. C 21 1207 |
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