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A hybrid one-vertex model for susceptible–infected–susceptible diseases on networks with partial connection information

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  • Wu, Qingchu

Abstract

We analyze the propagation of the susceptible–infected–susceptible model on the network, where the partial connection information is known. In order to analyze the spread of network epidemics, the quenched mean-field method requires complete connection information, while the heterogeneous mean-field method requires complete topology information. When only partial connection information is available, the quenched mean-field method may be disabled. In view of this situation, a hybrid model consisting of connection and topology information is proposed. The theoretical results are in good agreement with stochastic simulations on scale-free networks. In addition, as the proportion of connection information in the network increases, model predictions about final epidemic size become more accurate when the effective transmission rate is slightly larger than the simulation threshold.

Suggested Citation

  • Wu, Qingchu, 2024. "A hybrid one-vertex model for susceptible–infected–susceptible diseases on networks with partial connection information," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:chsofr:v:178:y:2024:i:c:s0960077923012729
    DOI: 10.1016/j.chaos.2023.114370
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    References listed on IDEAS

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    1. Wu, Qingchu & Chen, Shufang, 2022. "Coupled simultaneous evolution of disease and information on multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    2. Zhesi Shen & Wen-Xu Wang & Ying Fan & Zengru Di & Ying-Cheng Lai, 2014. "Reconstructing propagation networks with natural diversity and identifying hidden sources," Nature Communications, Nature, vol. 5(1), pages 1-10, September.
    3. Huang, He & Xu, Yang & Xing, Jingli & Shi, Tianyu, 2023. "Social influence or risk perception? A mathematical model of self-protection against asymptomatic infection in multilayer network," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
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