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Analysis of epidemic spreading process in multi-communities

Author

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  • Zhu, Peican
  • Wang, Xing
  • Zhi, Qiang
  • Ma, Jiezhong
  • Guo, Yangming

Abstract

In practice, an epidemic might be spreading among multi-communities; while the communities are usually intra-connected. In this manuscript, each community is modeled as a multiplex network (i.e., virtual layer and physical one). The connections inside certain community are referred as inter-contacts while the intra-contacts denote the connections among communities. For the epidemic spreading process, the traditional susceptible-infected-recovered (SIR) model is adopted. Then, corresponding state transition trees are determined and simulations are conducted to study the epidemic spreading process in multi-communities. Here, the effect of incorporating virtual layer on the range of individual affected by the epidemic is pursued. As illustrated, multi-summits are incurred if the spreading in multi-communities is considered; furthermore, the disparity between summits varies. This is affected by various factors. As indicated, the incorporation of virtual layer is capable of reducing the proportion of individuals being affected; moreover, disparity of different summits is likely to be increased regarding with scenarios of excluding virtual layer. Furthermore, the summit is likely to be postponed if virtual layer is incorporated.

Suggested Citation

  • Zhu, Peican & Wang, Xing & Zhi, Qiang & Ma, Jiezhong & Guo, Yangming, 2018. "Analysis of epidemic spreading process in multi-communities," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 231-237.
  • Handle: RePEc:eee:chsofr:v:109:y:2018:i:c:p:231-237
    DOI: 10.1016/j.chaos.2018.02.007
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    References listed on IDEAS

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    1. Nicola Perra & Duygu Balcan & Bruno Gonçalves & Alessandro Vespignani, 2011. "Towards a Characterization of Behavior-Disease Models," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-15, August.
    2. Gao, Bo & Deng, Zhenghong & Zhao, Dawei, 2016. "Competing spreading processes and immunization in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 175-181.
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    Cited by:

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    3. Yang, Jin-Xuan & Zhang, Yun, 2020. "Epidemic spreading of evolving community structure," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Meng, Xueyu & Lin, Jianhong & Fan, Yufei & Gao, Fujuan & Fenoaltea, Enrico Maria & Cai, Zhiqiang & Si, Shubin, 2023. "Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    5. Kabir, K.M. Ariful & Tanimoto, Jun, 2019. "Evolutionary vaccination game approach in metapopulation migration model with information spreading on different graphs," Chaos, Solitons & Fractals, Elsevier, vol. 120(C), pages 41-55.
    6. Cheng, Le & Li, Xianghua & Han, Zhen & Luo, Tengyun & Ma, Lianbo & Zhu, Peican, 2022. "Path-based multi-sources localization in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    7. Wang, Qiuling & Ren, Xiaobin & Gao, Bo & Wang, Jiaqian, 2020. "Heterogeneity reproductive ability promotes cooperation in spatial prisoner's dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    8. Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).

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