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Epidemic spread in bipartite network by considering risk awareness

Author

Listed:
  • Han, She
  • Sun, Mei
  • Ampimah, Benjamin Chris
  • Han, Dun

Abstract

Human awareness plays an important role in the spread of infectious diseases and the control of propagation patterns. Exploring the interplay between human awareness and epidemic spreading is a topic that has been receiving increasing attention. Considering the fact, some well-known diseases only spread between different species we propose a theoretical analysis of the Susceptible–Infected–Susceptible (SIS) epidemic spread from the perspective of bipartite network and risk aversion. Using mean field theory, the epidemic threshold is calculated theoretically. Simulation results are consistent with the proposed analytic model. The results show that, the final infection density is negative linear with the value of individuals’ risk awareness. Therefore, the epidemic spread could be effectively suppressed by improving individuals’ risk awareness.

Suggested Citation

  • Han, She & Sun, Mei & Ampimah, Benjamin Chris & Han, Dun, 2018. "Epidemic spread in bipartite network by considering risk awareness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1909-1916.
  • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:1909-1916
    DOI: 10.1016/j.physa.2017.11.107
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    Citations

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    Cited by:

    1. Meng, Xueyu & Cai, Zhiqiang & Si, Shubin & Duan, Dongli, 2021. "Analysis of epidemic vaccination strategies on heterogeneous networks: Based on SEIRV model and evolutionary game," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    2. Yao, Qianyi & Fan, Ruguo & Chen, Rongkai & Qian, Rourou, 2023. "A model of the enterprise supply chain risk propagation based on partially mapping two-layer complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    3. Han, Dun & Shao, Qi & Li, Dandan & Sun, Mei, 2020. "How the individuals’ risk aversion affect the epidemic spreading," Applied Mathematics and Computation, Elsevier, vol. 369(C).

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