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Global dynamics of a network-based SIQS epidemic model with nonmonotone incidence rate

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  • Cheng, Xinxin
  • Wang, Yi
  • Huang, Gang

Abstract

The risk of propagation of infectious diseases such as avian influenza and COVID-19 is generally controlled or reduced by quarantine measures. Considering this situation, a network-based SIQS (susceptible-infected-quarantined-susceptible) infectious disease model with nonmonotone incidence rate is established and analyzed in this paper. The psychological impact of the transmission of certain diseases in heterogeneous networks at high levels of infection may be characterized by the related nonmonotone incidence rate. The expressions of the basic reproduction number and equilibria of the model are determined analytically. We demonstrate in detail the uniform persistence of system and the global asymptotic stability of the disease-free equilibrium. The global attractivity of the unique endemic equilibrium is discussed by using monotone iteration technique. We obtain that the endemic equilibrium is globally asymptotically stable under certain conditions by constructing appropriate Lyapunov function. In addition, numerical simulations are performed to indicate the theoretical results.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:153:y:2021:i:p2:s0960077921008560
    DOI: 10.1016/j.chaos.2021.111502
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Wei, Xiaodan & Liu, Lijun & Zhou, Wenshu, 2017. "Global stability and attractivity of a network-based SIS epidemic model with nonmonotone incidence rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 789-798.
    4. Li, Chun-Hsien, 2015. "Dynamics of a network-based SIS epidemic model with nonmonotone incidence rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 234-243.
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