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Immunization of susceptible–infected model on scale-free networks

Author

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  • Bai, Wen-Jie
  • Zhou, Tao
  • Wang, Bing-Hong

Abstract

In this paper, we investigate two major immunization strategies, random immunization and targeted immunization, of the susceptible–infected (SI) model on the Barabási–Albert (BA) networks. For the heterogeneous structure, the random strategy is quite ineffective if the vaccinated proportion is small, while the targeted one which prefers to vaccinate the individuals with the largest degree can sharply depress the epidemic spreading even only a tiny fraction of population are vaccinated. The analytical solution is also obtained, which can capture the trend of velocity change vs. the amount of vaccinated population.

Suggested Citation

  • Bai, Wen-Jie & Zhou, Tao & Wang, Bing-Hong, 2007. "Immunization of susceptible–infected model on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 656-662.
    • Handle: RePEc:eee:phsmap:v:384:y:2007:i:2:p:656-662
    DOI: 10.1016/j.physa.2007.04.107
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    References listed on IDEAS

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    1. Dorogovtsev, S.N. & Mendes, J.F.F., 2003. "Evolution of Networks: From Biological Nets to the Internet and WWW," OUP Catalogue, Oxford University Press, number 9780198515906.
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    Cited by:

    1. Thomas Parmer & Luis M. Rocha & Filippo Radicchi, 2022. "Influence maximization in Boolean networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Yupeng Li & Zhaotong Wang & Xiaoyu Zhong & Fan Zou, 2019. "Identification of influential function modules within complex products and systems based on weighted and directed complex networks," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2375-2390, August.
    3. Bing Li & Ziye Xiang, 2023. "Evolutionary Game of Vaccination Considering Both Epidemic and Economic Factors by Infectious Network of Complex Nodes," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    4. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    5. Li, Qian & Zhou, Tao & Lü, Linyuan & Chen, Duanbing, 2014. "Identifying influential spreaders by weighted LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 47-55.
    6. Fei, Liguo & Deng, Yong, 2017. "A new method to identify influential nodes based on relative entropy," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 257-267.
    7. Wu, Qingchu & Liu, Huaxiang & Small, Michael, 2013. "Dynamical diversity induced by individual responsive immunization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(12), pages 2792-2802.
    8. Wu, Qingchu & Fu, Xinchu, 2016. "Immunization and epidemic threshold of an SIS model in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 576-581.
    9. Du, Yuxian & Gao, Cai & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "A new method of identifying influential nodes in complex networks based on TOPSIS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 57-69.
    10. Peng, Chengbin & Jin, Xiaogang & Shi, Meixia, 2010. "Epidemic threshold and immunization on generalized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 549-560.

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