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An efficient immunization strategy based on transmission limit in weighted complex networks

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  • Shen, Dongqin
  • Cao, Shanshan

Abstract

The immunization strategy against the epidemic spreading in real world has attracted widespread attention of scientists from many different fields. However, the traditional immune behavior is achieved by deleting the edges in the network, which can lead to variations in the network structure and consequently serious damage to the efficiency of networks. In this paper, we studied a new type of immune strategy applied to weighted networks, which is to maintain the necessary network efficiency by limit the transmission(reduce the weight of edges) to suppress the spread of epidemic. It is similar to the inflammation around the infected parts of our body, which not only prevent epidemic from further spreading but also do no harm to the function of the body. We first set the rate of transmission be proportional to the edge weight according to the S–I epidemic spreading model. Then, we propose the specific dynamic evolution model for infected nodes that boosts efficient epidemic control. Theoretical analysis and simulation results indicate that the immunization strategy can efficaciously prevent the spread of the epidemic, while maintaining the high efficiency of the network.

Suggested Citation

  • Shen, Dongqin & Cao, Shanshan, 2018. "An efficient immunization strategy based on transmission limit in weighted complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 1-7.
    • Handle: RePEc:eee:chsofr:v:114:y:2018:i:c:p:1-7
    DOI: 10.1016/j.chaos.2018.06.014
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    References listed on IDEAS

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    Cited by:

    1. Wei Duan, 2021. "Matrix-Based Formulation of Heterogeneous Individual-Based Models of Infectious Diseases: Using SARS Epidemic as a Case Study," IJERPH, MDPI, vol. 18(11), pages 1-20, May.
    2. Elisa Frutos Bernal & Angel Martín del Rey, 2019. "Study of the Structural and Robustness Characteristics of Madrid Metro Network," Sustainability, MDPI, vol. 11(12), pages 1-24, June.
    3. Wenyan Wang & Jun Zhang & Fang Zhou & Peng Chen & Bing Wang, 2021. "Paper acceptance prediction at the institutional level based on the combination of individual and network features," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1581-1597, February.

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