How to plug out of the networks in jeopardy of ASF, Covid-19, social media or markets toxicity
DOI:
https://doi.org/10.15503/emet2020.71.84Słowa kluczowe:
Epidemic spreading, social networks, complex networks, agent-based modelsAbstrakt
Aim: In our research, we examine universal properties of the global network whose structure represents a real-world network that might be later extended to social media, commodity market or countries under the infl uence of diseases like Covid-19 or ASF.
Methods: We propose quasi-epidemiological agent-based model of virus spread on a network. Firstly, we consider countries represented by subnetworks that have a scale-free structure achieved by the preferential attachment construction with a node hierarchy and binary edges. The global network of countries is a complete, directed, weighted network of these
subnetworks connected by their capitals and divided into cultural and geographical proximity. Viruses with a defi ned strength or aggressiveness occur independently at one of the nodes of a selected subnetwork and correspond to a piece of products or messages or diseases.
Results and conclusion: We analyse dynamics set by varying parameter values and observe a variety of phenomena including local and global pandemics and the existence of an epidemic threshold in the subnetworks. These phenomena have been also shown from
individual users points of view because the node removal from the network might have impact on its nearest neighbours differently. The selective participation in global network is proposed here to avoid side effects when the global network has been fully connected and no longer divided into clusters.
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Bibliografia
Boguna, M., Pastor-Satorras, R. (2020). Epidemic spreading in correlated complex networks. Phys. Rev. E 66, 047104.
Buda, A., Jarynowski, A., & Kuźmicz, K. (2020). An attempt to unifi ed approach to the
evolution of product in the entertainment industry. E-Methodology, 6, 80-93.
Chakraborti, A., Muni Toke, I., Patriarca, M., & Aberge F. (2011). Econophysics review:
II. Agent-based models. Quant. Fin,. 11, 1013-1041.
Dybiec, B. (2009). SIR model of epidemic spread with accumulated exposure. Eur. Phys.
J. B 67 377-383.
Edoh, K., MacCarthy, E. (2018). Network and equation-based models in epidemiology.
Int. J. Biomath, 11, 1850046.
Epstein, J.M. (1999). Agent-based computational models and generative social science.
Complexity, 4, 41-60.
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Li Z., Fan G., Xu J.,Gu X, Cheng Z,.
Yu T., Xia J., Xie X, Yin W., Li H., Liu M., Xiao Y., Gao H., Guo L., Xie J., Wang G., Jiang
R., Gao Z., Jin Q.,Wang J., & Bin C.,(2020). Clinical features of patients infected with
novel coronavirus in Wuhan, China“. Lancet, 395(10223), 497–506. doi:10.1016/
S0140-6736(20)30183-5. PMC 7159299. PMID 31986264.
Jarynowski, A., Buda, A., Płatek, D., & Belik, V. (2020). African Swine Fever Awareness
In The Internet Media In Poland – Exploratory Review. E-Methodology, 6, 100-114.
Kephart, J. O., & White, S. R. (1991). Directed-graph epidemiological models of computer viruses in Proc. 1991 IEEE Computer Society Symposium on Research in Security and
Privacy, 343-359.
Kwapień, J.,& Drożdż, S. (2012). Physics Reports: Physical approach to complex systems. Elsevier, 5015, 116-226.
Moreno, Y., Pastor-Satorras, R.,& Vespignani, A. (2002). Epidemic outbreaks in complex heterogeneous networks. Eur. Phys. J. B 26, 521-529.
Maxmen A., Mallapaty S. (2021). The COVID lab-leak hypothesis: what scientists do
and don’t know. Retrieved 25 July, 2020 from https://www.nature.com/articles/
d41586-021-01529-3
Pastor-Satorra,s R., & Vespignani, A. (2001). Epidemic spreading in scale-free networks. Phys. Rev. Lett, 86, 3200-3203.
Pastor-Satorras R., & Vespignani, A. (2001). Epidemic dynamics and endemic states in
complex network., Phys. Rev. E, 63. 066117.
Reppas, A. I., De Decker, Y., & Siettos, C. I. (2012). On the effi ciency of the equation-free
closure of statistical moments: dynamical properties of a stochastic epidemic model on
Erd¨os-R´nyi networks. J. Stat. Mech, P02020.
Sadedin, S.,Dybiec, B.,& Briscoe, G. (2003). A toy model of faith-based systems evolution. Physica, A 323, 715-725.
Samanidou, E., Zschischang, E., Stauffer, D., & Lux, T. (2007). Agent-based models of
fi nancial markets. Rep. Prog. Phys, 70, 409-450.
Stark, J. (2004). Product Lifecycle Management: 21st Century Paradigm for Product Realisation. Hamburg: Springer.
Trpevski, D., Tang, W. K. S., & Kocarevm L. (2010). Model for rumor spreading in networks. Phys. Rev., E 81, 056102
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Prawa autorskie (c) 2021 ANDRZEJ BUDA, KATARZYNA KUŹMICZ
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