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

被引：10

作者：

Wu, Qingchu ^{[1
]}

Zhou, Rong ^{[1
]}

Hadzibeganovic, Tarik ^{[2
]}

机构：

[1] Jiangxi Normal Univ, Coll Math & Informat Sci, Nanchang 330022, Jiangxi, Peoples R China

[2] Karl Franzens Univ Graz, Fac Nat Sci, Dept Psychol, A-8010 Graz, Austria

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2019年 / 518卷

基金：

中国国家自然科学基金;

关键词：

Epidemic spreading; Quenched mean-field approach; Recurrent-state epidemics; Susceptible-infected-susceptible epidemics; Dynamic message-passing; Complex networks; STATIONARY DISTRIBUTION; MODEL; DISEASE; EXTINCTION; THRESHOLD; TIME;

D O I：

10.1016/j.physa.2018.11.052

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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 Erdos-Renyi 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. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：71 / 79

页数：9

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