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

Qingchu Wu, Rong Zhou and Tarik Hadzibeganovic

Physica A: Statistical Mechanics and its Applications, 2019, vol. 518, issue C, 71-79

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.

Keywords: Epidemic spreading; Quenched mean-field approach; Recurrent-state epidemics; Susceptible–infected–susceptible epidemics; Dynamic message-passing; Complex networks (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:518:y:2019:i:c:p:71-79

DOI: 10.1016/j.physa.2018.11.052

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