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Impact of positive and negative information on epidemic spread in a three-layer network

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  • Han, Dun
  • Wang, Xin

Abstract

In this paper, we introduce an epidemic model based on a three-layer network, which includes the mass media layer, the information layer, and the epidemic layer, to explore the impact of positive and negative information on epidemic spreading. The mass media layer comprises two distinct categories of mass media. The information layer classifies individuals as U state, A1 state and A2 state. The epidemic layer follows the SIS paradigm. We applied the microscopic Markov chain approach to characterize the epidemic threshold. We use threshold models to depict individuals connecting to different mass media at different times. Our results indicate that threshold models significantly affect the final scale of epidemics and the proportion of individuals in different awareness states, with the threshold having a two-stage effect on epidemic spread. Interestingly, our research found that promoting the spread of negative news about diseases while suppressing positive news can control the outbreak and spread of epidemics, differing from previous findings. Our study also found that promoting the dissemination of negative news about diseases is more effective in suppressing epidemic spread than promoting positive information. Finally, our analysis suggests that measures such as reducing individuals’ forgetting rates of epidemic information and improving recovery rates can effectively suppress epidemic outbreaks and spread.

Suggested Citation

  • Han, Dun & Wang, Xin, 2024. "Impact of positive and negative information on epidemic spread in a three-layer network," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:chsofr:v:186:y:2024:i:c:s0960077924008166
    DOI: 10.1016/j.chaos.2024.115264
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    References listed on IDEAS

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    1. Wu, Dayu & Liu, Ying & Tang, Ming & Xu, Xiao-Ke & Guan, Shuguang, 2022. "Impact of hopping characteristics of inter-layer commuters on epidemic spreading in multilayer networks," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
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    3. Sun, Qingyi & Wang, Zhishuang & Zhao, Dawei & Xia, Chengyi & Perc, Matjaž, 2022. "Diffusion of resources and their impact on epidemic spreading in multilayer networks with simplicial complexes," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
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    5. Jia, Mengqi & Li, Xin & Ding, Li, 2021. "Epidemic spreading with awareness on multi-layer activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    6. Wang, Huan & Ma, Chuang & Chen, Han-Shuang & Zhang, Hai-Feng, 2021. "Effects of asymptomatic infection and self-initiated awareness on the coupled disease-awareness dynamics in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 400(C).
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