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An environment aware epidemic spreading model and immune strategy in complex networks

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  • Qin, Yang
  • Zhong, Xiaoxiong
  • Jiang, Hao
  • Ye, Yibin

Abstract

In the standard SIS model, each node has the same probability to be infected by its neighbors regardless of its surrounding environment. In the real world, the probability of a node to be infected is varying with the network environment; the prior model is not suitable for this scenario. In this paper, we consider an actual epidemic spreading model in which the probability of a node to be infected is related with the number of the infected nodes among its neighbors. We develop an analytical model for this epidemic spreading, named environment aware SIS model (EA-SIS) considering the heterogeneous infection rates, and analytically investigate the epidemic spreading in complex networks. We find that the threshold of EA-SIS is smaller than SIS which means the virus is easier to spread out in the EA-SIS model. In addition, we study several existing immune strategies on the EA-SIS model and propose a novel immune strategy which is based on expected infection time, ETB, of the nodes around the infected nodes for EA-SIS. The simulation results show that the EA-SIS model is more efficient that the SIS model, also, the proposed immune strategy, ETB, is more effective than the local information method and is close to the target immune strategy.

Suggested Citation

  • Qin, Yang & Zhong, Xiaoxiong & Jiang, Hao & Ye, Yibin, 2015. "An environment aware epidemic spreading model and immune strategy in complex networks," Applied Mathematics and Computation, Elsevier, vol. 261(C), pages 206-215.
    • Handle: RePEc:eee:apmaco:v:261:y:2015:i:c:p:206-215
    DOI: 10.1016/j.amc.2015.03.110
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    References listed on IDEAS

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    1. Cristopher Moore & M. E. J. Newman, 2000. "Epidemics and Percolation in Small-World Networks," Working Papers 00-01-002, Santa Fe Institute.
    2. Lin Wang & Xiang Li & Yi-Qing Zhang & Yan Zhang & Kan Zhang, 2011. "Evolution of Scaling Emergence in Large-Scale Spatial Epidemic Spreading," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-11, July.
    3. X. Li & L. Cao & G. F. Cao, 2010. "Epidemic prevalence on random mobile dynamical networks: individual heterogeneity and correlation," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 75(3), pages 319-326, June.
    4. Huang, He & Yan, Zhijun & Pan, Yaohui, 2014. "Measuring edge importance to improve immunization performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 532-540.
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    Cited by:

    1. Jia, Nan & Ding, Li & Liu, Yu-Jing & Hu, Ping, 2018. "Global stability and optimal control of epidemic spreading on multiplex networks with nonlinear mutual interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 93-105.
    2. Wang, Runzhou & Zhang, Xinsheng & Wang, Minghu, 2024. "A two-layer model with partial mapping: Unveiling the interplay between information dissemination and disease diffusion," Applied Mathematics and Computation, Elsevier, vol. 468(C).
    3. Xu, Menglu & Hou, Xingyi & Wang, Miao & Hu, Lin & Nie, Linfei, 2025. "Global dynamics and optimal control of an environmentally-driven epidemic model on heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 675(C).
    4. Schaum, Alexander & Bernal Jaquez, Roberto, 2016. "Estimating the state probability distribution for epidemic spreading in complex networks," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 197-206.

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