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Immunization and epidemic threshold of an SIS model in complex networks

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  • Wu, Qingchu
  • Fu, Xinchu

Abstract

We propose an improved mean-field model to investigate immunization strategies of an SIS model in complex networks. Unlike the traditional mean-field approach, the improved model utilizes the degree information of before and after the immunization. The epidemic threshold of degree-correlated networks can be obtained by linear stability analysis. For degree-uncorrelated networks, the model is reduced to the SIS epidemic model in networks after removing the immunized nodes. Compared to the previous results of random and targeted immunization schemes on degree-uncorrelated networks, we find that the infectious disease has a lower epidemic threshold.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:444:y:2016:i:c:p:576-581
    DOI: 10.1016/j.physa.2015.10.043
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    References listed on IDEAS

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    1. Dawei Zhao & Lianhai Wang & Shudong Li & Zhen Wang & Lin Wang & Bo Gao, 2014. "Immunization of Epidemics in Multiplex Networks," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-5, November.
    2. 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.
    3. C.M. Schneider & T. Mihaljev & H.J. Herrmann, "undated". "Inverse targeting - an effective immunization strategy," Working Papers ETH-RC-12-009, ETH Zurich, Chair of Systems Design.
    4. Wu, Qingchu & Fu, Xinchu & Jin, Zhen & Small, Michael, 2015. "Influence of dynamic immunization on epidemic spreading in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 566-574.
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    Cited by:

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    2. Zhang, Jiaquan & Lu, Dan & Yang, Shunkun, 2017. "Comparison of two mean-field based theoretical analysis methods for SIS model," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 209-214.
    3. Wang, Ze & Gao, Xiangyun & Tang, Renwu & Liu, Xueyong & Sun, Qingru & Chen, Zhihua, 2019. "Identifying influential nodes based on fluctuation conduction network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 355-369.
    4. Mukisa, Nicholas & Zamora, Ramon & Lie, Tek Tjing, 2022. "Multi criteria analysis of alternative energy technologies based on their predicted impact on community sustainable livelihoods capitals: A case of Uganda," Renewable Energy, Elsevier, vol. 182(C), pages 1103-1125.
    5. Meng, Xueyu & Han, Sijie & Wu, Leilei & Si, Shubin & Cai, Zhiqiang, 2022. "Analysis of epidemic vaccination strategies by node importance and evolutionary game on complex networks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. Fan, Chong-jun & Jin, Yang & Huo, Liang-an & Liu, Chen & Yang, Yun-peng & Wang, Ya-qiong, 2016. "Effect of individual behavior on the interplay between awareness and disease spreading in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 523-530.
    7. Jia, Peng & Liu, Jiayong & Fang, Yong & Liu, Liang & Liu, Luping, 2018. "Modeling and analyzing malware propagation in social networks with heterogeneous infection rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 240-254.
    8. Shen, Ai-Zhong & Guo, Jin-Li & Suo, Qi, 2017. "Study of the variable growth hypernetworks influence on the scaling law," Chaos, Solitons & Fractals, Elsevier, vol. 97(C), pages 84-89.
    9. Wu, Qingchu & Zhang, Fei, 2016. "Threshold conditions for SIS epidemic models on edge-weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 77-83.

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