| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
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Contagion dynamics on adaptive multiplex networks with awareness-dependent rewiring |
| Xiao-Long Peng(彭小龙)1,2,† and Yi-Dan Zhang(张译丹)1,2,3 |
1 Complex Systems Research Center, Shanxi University, Taiyuan 030006, China;
2 Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan 030006, China;
3 School of Mathematical Sciences, Shanxi University, Taiyuan 030006, China |
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Abstract Over the last few years, the interplay between contagion dynamics of social influences (e.g., human awareness, risk perception, and information dissemination) and biological infections has been extensively investigated within the framework of multiplex networks. The vast majority of existing multiplex network spreading models typically resort to heterogeneous mean-field approximation and microscopic Markov chain approaches. Such approaches usually manifest richer dynamical properties on multiplex networks than those on simplex networks; however, they fall short of a subtle analysis of the variations in connections between nodes of the network and fail to account for the adaptive behavioral changes among individuals in response to epidemic outbreaks. To transcend these limitations, in this paper we develop a highly integrated effective degree approach to modeling epidemic and awareness spreading processes on multiplex networks coupled with awareness-dependent adaptive rewiring. This approach keeps track of the number of nearest neighbors in each state of an individual; consequently, it allows for the integration of changes in local contacts into the multiplex network model. We derive a formula for the threshold condition of contagion outbreak. Also, we provide a lower bound for the threshold parameter to indicate the effect of adaptive rewiring. The threshold analysis is confirmed by extensive simulations. Our results show that awareness-dependent link rewiring plays an important role in enhancing the transmission threshold as well as lowering the epidemic prevalence. Moreover, it is revealed that intensified awareness diffusion in conjunction with enhanced link rewiring makes a greater contribution to disease prevention and control. In addition, the critical phenomenon is observed in the dependence of the epidemic threshold on the awareness diffusion rate, supporting the metacritical point previously reported in literature. This work may shed light on understanding of the interplay between epidemic dynamics and social contagion on adaptive networks.
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Received: 11 December 2020
Revised: 10 January 2021
Accepted manuscript online: 01 February 2021
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PACS:
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87.23.Ge
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(Dynamics of social systems)
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87.19.X-
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(Diseases)
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89.75.Hc
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(Networks and genealogical trees)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11601294 and 61873154), Shanxi Scholarship Council of China (Grant No. 2016-011), the Shanxi Province Science Foundation for Youths (Grant Nos. 201601D021012, 201801D221011, 201901D211159, 201801D221007 and 201801D221003), and the 1331 Engineering Project of Shanxi Province, China. |
Corresponding Authors:
Xiao-Long Peng
E-mail: xlpeng@sxu.edu.cn
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Cite this article:
Xiao-Long Peng(彭小龙) and Yi-Dan Zhang(张译丹) Contagion dynamics on adaptive multiplex networks with awareness-dependent rewiring 2021 Chin. Phys. B 30 058901
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[1] Bauch C T and Galvani A P 2013 Science 342 47
[2] Ferguson N 2007 Nature 446 733
[3] Ruan Z, Iñiguez G, Karsai M and Kertész J 2015 Phys. Rev. Lett. 115 218702
[4] Wang W, Tang M, Zhang H F and Lai Y C 2015 Phys. Rev. E 92 012820
[5] Huang W M, Zhang L J, Xu X J and Fu X 2016 Sci. Rep. 6 23766
[6] Funk S, Gilad E, Watkins C and Jansen V A A 2009 Proc. Natl. Acad. Sci. USA 106 6872
[7] Wu Q, Fu X, Small M and Xu X J 2012 Chaos 22 013101
[8] Granell C, Gómez S and Arenas A 2013 Phys. Rev. Lett. 111 128701
[9] Gómez-Gardeñes J, de Domenico M, Gutiérrez G, Arenas A and Gómez S 2015 Phil. Trans. R. Soc. A 373 20150117
[10] Anderson R M and May R M 1991 Infectious Diseases of Humans: Dynamics and Control (Oxford: Oxford University Press)
[11] Granell C, Gómez S and Arenas A 2014 Phys. Rev. E 90 012808
[12] Guo Q, Lei Y, Li M, Ma Y and Zheng Z 2015 Phys. Rev. E 91 012822
[13] Kan J Q and Zhang H F 2017 Commun. Nonlin. Sci. Numer. Simulat. 44 193
[14] Xia C, Wang Z, C Zheng, Guo Q, Shi Y, Dehmer M and Chen Z 2019 Inform. Sci. 471 185
[15] Wang Z, Guo Q, Sun S and Xia C 2019 Appl. Math. Comput. 349 134
[16] Zheng C, Xia C, Guo Q and Dehmer M 2018 J. Parallel Distrib. Comput. 115 20
[17] Gross T, Dommar D'Lima C J and Blasius B 2006 Phys. Rev. Lett. 96 208701
[18] Gross T and Blasius B 2008 J. R. Soc. Interface 5 259
[19] Shaw L B and Schwartz I B 2008 Phys. Rev. E 77 066101
[20] Marceau V, Noël P A, Hébert-Dufresne L, Allard A and Dubé L J 2010 Phys. Rev. E 82 036116
[21] Segbroeck S V, Santos F C and Pacheco J M 2010 PLoS Comput. Biol. 6 e1000895
[22] Kamp C 2010 PLoS Comput. Biol. 6 e1000984
[23] Wang B, Cao L, Suzuki H and Aihara K 2011 J. Phys. A: Math. Theor. 44 035101
[24] Poletti P, Ajelli M and Merler S 2012 Math. Biosci. 238 80
[25] Juher D, Ripoll J and Saldaña J 2013 J. Math. Biol. 67 411
[26] Peng X L, Xu X J, Small M, Fu X and Jin Z 2016 J. Math. Biol. 73 1561
[27] Sherborne N, Blyuss K B and Kiss I Z 2018 Phys. Rev. E 97 042306
[28] Zhang X, Shan C, Jin Z and Zhu H 2019 J. Differ. Equations 266 803
[29] Lu J and Zhang X 2019 Math. Biosci. Eng. 16 2973
[30] Cai C R, Wu Z X, Chen M Z Q, Holme P and Guan J Y 2016 Phys. Rev. Lett. 116 258301
[31] Pastor-Satorras P, Castellano C, Van Mieghem P and Vespignani A 2015 Rev. Mod. Phys. 87 925
[32] Fu X, Small M and Chen G 2014 Propagation Dynamics on Complex Networks: Models, Methods and Stability Analysis (Wiley Online Library: Higher Education Press)
[33] Wang W, Tang M, Stanley H E and Braunstein L A 2017 Rep. Prog. Phys. 80 036603
[34] Lindquist J, Ma J, van den Driessche P and Willeboordse F H 2011 J. Math. Biol. 62 143
[35] Zhou Y, Zhou J, Chen G and Stanley H E 2019 New J. Phys. 21 035002
[36] Rizzo A, Frasca M and Porfiri M 2014 Phys. Rev. E 90 042801
[37] Kotnis B and Kuri J 2013 Phys. Rev. E 87 062810
[38] Hu P, Ding L and An X 2018 Phys. Rev. E 98 062322
[39] Paolotti D, Carnahan A, Colizza V, Eames K, Edmunds J, Gomes G, Koppeschaar C, Rehn M, Smallenburg R, Turbelin C, Van Noort S and Vespignani A 2014 Clin. Microbiol. Infect. 20 17
[40] Martcheva M 2015 An Introduction to Mathematical Epidemiology (Boston: Springer)
[41] Li M Y 2018 An Introduction to Mathematical Modeling of Infectious Diseases (Cham: Springer)
[42] van den Driessche P and Watmough J 2002 Math. Biosci. 180 29
[43] Bapat R B and Raghavan T E S 1997 Nonnegative Matrices and Applications (New York: Cambridge University Press)
[44] Kiss I Z, Cassell J, Recker M and Simon P L 2010 Math. Biolsci. 225 1
[45] Huang Y J, Juang J, Liang Y H and Wang H Y 2018 J. Math. Biol. 76 1339
[46] Erdös P and Rényi A 1959 Publicationes Mathematicae (Debrecen) 6 290 |
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