Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading

Abstract The interaction between epidemic spreading and information diffusion is an interdisciplinary research problem. During an epidemic, people tend to take self-protective measures to reduce the infection risk. However, with the diffusion of rumor, people may be difficult to make an appropriate choice. How to reduce the negative impact of rumor and to control epidemic has become a critical issue in the social network. Elaborate mathematical model is instructive to understand such complex dynamics. In this paper, we develop a two-layer network to model the interaction between the spread of epidemic and the competitive diffusions of information. The results show that knowledge diffusion can eradicate both rumor and epidemic, where the penetration intensity of knowledge into rumor plays a vital role. Specifically, the penetration intensity of knowledge significantly increases the thresholds for rumor and epidemic to break out, even when the self-protective measure is not perfectly effective. But eradicating rumor shouldn’t be equated with eradicating epidemic. The epidemic can be eradicated with rumor still diffusing, and the epidemic may keep spreading with rumor being eradicated. Moreover, the communication-layer network structure greatly affects the spread of epidemic in the contact-layer network. When people have more connections in the communication-layer network, the knowledge is more likely to diffuse widely, and the rumor and epidemic can be eradicated more efficiently. When the communication-layer network is sparse, a larger penetration intensity of knowledge into rumor is required to promote the diffusion of knowledge.

[1]  R Pastor-Satorras,et al.  Dynamical and correlation properties of the internet. , 2001, Physical review letters.

[2]  Fuzhong Nian,et al.  Efficient immunization strategies on complex networks. , 2010, Journal of theoretical biology.

[3]  Tao Zhou,et al.  Epidemic spreading on heterogeneous networks with identical infectivity , 2007 .

[4]  Moez Draief,et al.  Epidemics and Rumours in Complex Networks , 2010 .

[5]  Jiajia Wang,et al.  Rumor spreading model considering forgetting and remembering mechanisms in inhomogeneous networks , 2013 .

[6]  Jiming Liu,et al.  A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions , 2014, Journal of The Royal Society Interface.

[7]  Jun Tanimoto,et al.  Analysis of epidemic outbreaks in two-layer networks with different structures for information spreading and disease diffusion , 2019, Commun. Nonlinear Sci. Numer. Simul..

[8]  Wanping Liu,et al.  Modeling cyber rumor spreading over mobile social networks: A compartment approach , 2019, Appl. Math. Comput..

[9]  Jun Tanimoto,et al.  Effect of information spreading to suppress the disease contagion on the epidemic vaccination game , 2019, Chaos, Solitons & Fractals.

[10]  Xin Jiang,et al.  Epidemic spreading with activity-driven awareness diffusion on multiplex network , 2016, Chaos.

[11]  Sergio Gómez,et al.  On the dynamical interplay between awareness and epidemic spreading in multiplex networks , 2013, Physical review letters.

[12]  Jun Tanimoto,et al.  Modelling and analysing the coexistence of dual dilemmas in the proactive vaccination game and retroactive treatment game in epidemic viral dynamics , 2019, Proceedings of the Royal Society A.

[13]  Jun Tanimoto,et al.  Vaccination strategies in a two-layer SIR/V-UA epidemic model with costly information and buzz effect , 2019, Commun. Nonlinear Sci. Numer. Simul..

[14]  Gaoxi Xiao,et al.  A colored mean-field model for analyzing the effects of awareness on epidemic spreading in multiplex networks. , 2018, Chaos.

[15]  Seyed M. Moghadas,et al.  Modelling the effect of imperfect vaccines on disease epidemiology , 2004 .

[16]  Jun Tanimoto,et al.  Analysis of SIR epidemic model with information spreading of awareness , 2019, Chaos, Solitons & Fractals.

[17]  Quanyan Zhu,et al.  Optimal control of influenza epidemic model with virus mutations , 2013, 2013 European Control Conference (ECC).

[18]  Jun Tanimoto,et al.  The impact of information spreading on epidemic vaccination game dynamics in a heterogeneous complex network- A theoretical approach , 2020 .

[19]  Jun Tanimoto,et al.  A game theoretic approach to discuss the positive secondary effect of vaccination scheme in an infinite and well-mixed population , 2019, Chaos, Solitons & Fractals.

[20]  Mark Jit,et al.  A systematic review of the social and economic burden of influenza in low- and middle-income countries. , 2015, Vaccine.

[21]  Danna Zhou,et al.  d. , 1840, Microbial pathogenesis.

[22]  Chuang Liu,et al.  Coupling dynamics of epidemic spreading and information diffusion on complex networks , 2018, Applied Mathematics and Computation.

[23]  Dawei Zhao,et al.  Statistical physics of vaccination , 2016, ArXiv.

[24]  Alessandro Vespignani,et al.  Immunization of complex networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Hui Wan,et al.  An SEIS epidemic model with transport-related infection. , 2007, Journal of theoretical biology.

[26]  Chao Shan,et al.  Evolutionary enhancement of Zika virus infectivity in Aedes aegypti mosquitoes , 2017, Nature.

[27]  Alessandro Vespignani,et al.  Absence of epidemic threshold in scale-free networks with degree correlations. , 2002, Physical review letters.

[28]  Matjaž Perc,et al.  Forecasting COVID-19 , 2020, Frontiers in Physics.

[29]  Ming Tang,et al.  Asymmetrically interacting spreading dynamics on complex layered networks , 2014, Scientific Reports.

[30]  Matthias Dehmer,et al.  Interplay between SIR-based disease spreading and awareness diffusion on multiplex networks , 2018, J. Parallel Distributed Comput..

[31]  Qi Shao,et al.  How the individuals' risk aversion affect the epidemic spreading , 2020, Appl. Math. Comput..

[32]  Wei Wang,et al.  Coevolution spreading in complex networks , 2019, Physics Reports.

[33]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[34]  Wanlin Xie,et al.  A rumor spreading model with variable forgetting rate , 2013 .

[35]  Claudio Castellano,et al.  Thresholds for epidemic spreading in networks , 2010, Physical review letters.

[36]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[37]  Ming Tang,et al.  Suppressing disease spreading by using information diffusion on multiplex networks , 2016, Scientific Reports.

[38]  Chengyi Xia,et al.  The impact of awareness diffusion on SIR-like epidemics in multiplex networks , 2019, Appl. Math. Comput..

[39]  Zhiyong Yu,et al.  Dynamical analysis of rumor spreading model in homogeneous complex networks , 2019, Appl. Math. Comput..

[40]  Jun Tanimoto,et al.  Is subsidizing vaccination with hub agent priority policy really meaningful to suppress disease spreading? , 2020, Journal of theoretical biology.

[41]  Wei Huang,et al.  Rumor spreading model with consideration of forgetting mechanism: A case of online blogging LiveJournal , 2011 .

[42]  Dirk Helbing,et al.  Saving Human Lives: What Complexity Science and Information Systems can Contribute , 2014, Journal of statistical physics.

[43]  Y. Hu,et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China , 2020, The Lancet.

[44]  Donald S. Shepard,et al.  Economic Impact of Dengue Illness in the Americas , 2011, The American journal of tropical medicine and hygiene.

[45]  R. Pastor-Satorras,et al.  Epidemic spreading in correlated complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[46]  Jun Tanimoto,et al.  Interplay between cost and effectiveness in influenza vaccine uptake: a vaccination game approach , 2019, Proceedings of the Royal Society A.

[47]  Zixue Tai,et al.  The rumouring of SARS during the 2003 epidemic in China , 2011, Sociology of health & illness.

[48]  Fathalla A. Rihan,et al.  Mathematical analysis of an SIS model with imperfect vaccination and backward bifurcation , 2014, Math. Comput. Simul..

[49]  H. Sebastian Seung,et al.  A solution to the single-question crowd wisdom problem , 2017, Nature.

[50]  Bing-Hong Wang,et al.  Braess's Paradox in Epidemic Game: Better Condition Results in Less Payoff , 2013, Scientific Reports.

[51]  Chuang Liu,et al.  Epidemic Spreading on Weighted Complex Networks , 2013, ArXiv.

[52]  Duanbing Chen,et al.  The small world yields the most effective information spreading , 2011, ArXiv.

[53]  Jon Cohen,et al.  World on alert for potential spread of new SARS-like virus found in China , 2020 .

[54]  Rui Xu,et al.  Global dynamics of an SEIS epidemic model with saturation incidence and latent period , 2012, Appl. Math. Comput..

[55]  Michael Small,et al.  The impact of awareness on epidemic spreading in networks , 2012, Chaos.

[56]  R. Webster,et al.  Are We Ready for Pandemic Influenza? , 2003, Science.

[57]  Katherine L. Milkman,et al.  What Makes Online Content Viral? , 2012 .

[58]  Yaohui Pan,et al.  Identifying the direct risk source to contain epidemics more effectively. , 2016, Physical review. E.

[59]  Lin Wang,et al.  Coupled disease–behavior dynamics on complex networks: A review , 2015, Physics of Life Reviews.