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Epidemic spreading dynamics on complex networks with adaptive social-support

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  • Zhou, Rong
  • Wu, Qingchu

Abstract

Using conditional quenched mean-field (cQMF) method, we develop a continuous-time susceptible–infected–susceptible epidemic model on quenched complex networks with combined self-recovery and social support from healthy network nodes. We determined the lower bound of the epidemic threshold which was found to depend directly on social support α. Our analytical results of the generalized cQMF model were in good agreement with numerical simulations on Erdő s–Rényi (ER) random graphs and different scale-free (SF) network configurations. We observed an interaction effect between network type (ER vs. SF) and social support level (α), finding a significant difference between the final epidemic sizes observed on ER versus SF networks when α was large, but not when α level was small or zero. Our findings suggest that social support from non-infected individuals contributes substantially towards the inhibition of an epidemic outbreak, and that the type of network structure plays a role in determining the final epidemic size but only when the social support level is sufficiently high.

Suggested Citation

  • Zhou, Rong & Wu, Qingchu, 2019. "Epidemic spreading dynamics on complex networks with adaptive social-support," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 778-787.
    • Handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:778-787
    DOI: 10.1016/j.physa.2019.03.107
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    References listed on IDEAS

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