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Effects of active links on epidemic transmission over social networks

Author

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  • Zhu, Guanghu
  • Chen, Guanrong
  • Fu, Xinchu

Abstract

A new epidemic model with two infection periods is developed to account for the human behavior in social network, where newly infected individuals gradually restrict most of future contacts or are quarantined, causing infectivity change from a degree-dependent form to a constant. The corresponding dynamics are formulated by a set of ordinary differential equations (ODEs) via mean-field approximation. The effects of diverse infectivity on the epidemic dynamics ​are examined, with a behavioral interpretation of the basic reproduction number. Results show that such simple adaptive reactions largely determine the impact of network structure on epidemics. Particularly, a theorem proposed by Lajmanovich and Yorke in 1976 is generalized, so that it can be applied for the analysis of the epidemic models with multi-compartments especially network-coupled ODE systems.

Suggested Citation

  • Zhu, Guanghu & Chen, Guanrong & Fu, Xinchu, 2017. "Effects of active links on epidemic transmission over social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 614-621.
    • Handle: RePEc:eee:phsmap:v:468:y:2017:i:c:p:614-621
    DOI: 10.1016/j.physa.2016.10.064
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    References listed on IDEAS

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    1. Li, Tao & Liu, Xiongding & Wu, Jie & Wan, Chen & Guan, Zhi-Hong & Wang, Yuanmei, 2016. "An epidemic spreading model on adaptive scale-free networks with feedback mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 649-656.
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    Cited by:

    1. Xue Yang & Zhiliang Zhu & Hai Yu & Yuli Zhao & Li Guo, 2019. "Evolutionary Game Dynamics of the Competitive Information Propagation on Social Networks," Complexity, Hindawi, vol. 2019, pages 1-11, December.
    2. Ezzeddine Belgacem Mosbah & Parakramaweera Sunil Dharmapala, 2022. "Evaluating the Effects of COVID-19 and Vaccination on Employment Behaviour: A Panel Data Analysis Acrossthe World," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    3. Noel Rapa, 2021. "Mitigation measures, prevalence response and public mobility during the COVID-19 emergency," CBM Working Papers WP/03/2021, Central Bank of Malta.
    4. Cheng, Xinxin & Wang, Yi & Huang, Gang, 2021. "Global dynamics of a network-based SIQS epidemic model with nonmonotone incidence rate," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    5. Wei, Xiaodan & Xu, Gaochao & Zhou, Wenshu, 2018. "Global stability of endemic equilibrium for a SIQRS epidemic model on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 203-214.
    6. Xiaoyang Liu & Chao Liu & Xiaoping Zeng, 2017. "Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling," Complexity, Hindawi, vol. 2017, pages 1-7, June.
    7. Mendolia, Silvia & Stavrunova, Olena & Yerokhin, Oleg, 2021. "Determinants of the community mobility during the COVID-19 epidemic: The role of government regulations and information," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 199-231.
    8. Liu, Lijun & Wei, Xiaodan & Zhang, Naimin, 2019. "Global stability of a network-based SIRS epidemic model with nonmonotone incidence rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 587-599.
    9. Caroline Orset, 2018. "People’s perception and cost-effectiveness of home confinement during an influenza pandemic: evidence from the French case," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(9), pages 1335-1350, December.

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