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Conditional quenched mean-field approach for recurrent-state epidemic dynamics in complex networks

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  • Wu, Qingchu
  • Zhou, Rong
  • Hadzibeganovic, Tarik

Abstract

We introduce conditional quenched mean-field (cQMF) method for recurrent-state susceptible–infected–susceptible epidemics in complex networks. This novel analytic method and three other competing models are systematically investigated and compared against continuous-time Gillespie algorithm-based computer simulations. We find that analytical results of our cQMF method are in good agreement with numerical simulations on Erdős–Rényi random graphs and various scale-free network configurations. While being formally similar to the recurrent dynamic message passing (rDMP) model, our cQMF method clearly outperforms rDMP in the prediction of the final epidemic size. Our method offers an advanced approach to modeling recurrent-state epidemic dynamics, where individuals face repeated infections in the course of their lifetime due to continuous virus evolution or waning immunity, as in seasonal influenza or pertussis.

Suggested Citation

  • Wu, Qingchu & Zhou, Rong & Hadzibeganovic, Tarik, 2019. "Conditional quenched mean-field approach for recurrent-state epidemic dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 71-79.
    • Handle: RePEc:eee:phsmap:v:518:y:2019:i:c:p:71-79
    DOI: 10.1016/j.physa.2018.11.052
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    Cited by:

    1. 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.
    2. Wang, Zhixiao & Rui, Xiaobin & Yuan, Guan & Cui, Jingjing & Hadzibeganovic, Tarik, 2021. "Endemic information-contagion outbreaks in complex networks with potential spreaders based recurrent-state transmission dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).

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