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What information sources can prevent the epidemic: Local information or kin information?

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  • Zou, Rongcheng
  • Duan, Xiaofang
  • Han, Zhen
  • Lu, Yikang
  • Ma, Kewei

Abstract

In the process of disease transmission, the decision of whether or not to vaccinate is not only influenced by the environment, but also by household factors. Here, we extend the susceptible–infected–susceptible–vaccinated (SIVS) model by introducing two kinds of information related to decisions and study disease transmission on household networks, where vaccination not only depends on the environment, that is, the number of infected neighbors, but also relies on the situation of household members. If a household member decides to take the vaccine, his susceptible household members will also be vaccinated unconditionally. Through extensive numerical simulations and theoretic exploration, results reveal that such a household-based collective vaccination decision cannot prevent the epidemic compared with the personal-based vaccination decision. The household-based collective decision can only protect the vaccinated families, but it cannot protect the unvaccinated families. Unlike family-based collective decisions, personal-based vaccination decisions can successfully stop the spread of disease by separating susceptible individuals from infected ones. Our study thus enhances the understanding of the evolution of epidemic about collective vaccination and personal vaccination decisions.

Suggested Citation

  • Zou, Rongcheng & Duan, Xiaofang & Han, Zhen & Lu, Yikang & Ma, Kewei, 2023. "What information sources can prevent the epidemic: Local information or kin information?," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
  • Handle: RePEc:eee:chsofr:v:168:y:2023:i:c:s096007792300005x
    DOI: 10.1016/j.chaos.2023.113104
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    References listed on IDEAS

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    Cited by:

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    2. Dai, Hui & Wang, Xiaoyue & Lu, Yikang & Hou, Yunxiang & Shi, Lei, 2024. "The effect of intraspecific cooperation in a three-species cyclic predator-prey model," Applied Mathematics and Computation, Elsevier, vol. 470(C).
    3. Xie, Xiaoxiao & Huo, Liang'an, 2024. "Co-evolution dynamics between information and epidemic with asymmetric activity levels and community structure in time-varying multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 181(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. Wu, Bingjie & Huo, Liang'an, 2024. "The influence of different government policies on the co-evolution of information dissemination, vaccination behavior and disease transmission in multilayer networks," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    6. Lecorvaisier, Florian & Pontier, Dominique & Soubeyrand, Benoît & Fouchet, David, 2024. "Using a dynamical model to study the impact of a toxoid vaccine on the evolution of a bacterium: The example of diphtheria," Ecological Modelling, Elsevier, vol. 487(C).
    7. Huang, Wenting & Duan, Xiaofang & Qin, Lijuan & Park, Junpyo, 2023. "Fitness-based mobility enhances the maintenance of biodiversity in the spatial system of cyclic competition," Applied Mathematics and Computation, Elsevier, vol. 456(C).

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