Predicting the epidemic threshold of the susceptible-infected-recovered model
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Date
2016-04-19
Authors
Wang, Wei
Liu, Quan-Hui
Zhong, Lin-Feng
Tang, Ming
Gao, Hui
Stanley, Harry Eugene
Version
Published version
OA Version
Citation
Wei Wang, Quan-Hui Liu, Lin-Feng Zhong, Ming Tang, Hui Gao, H Eugene Stanley. 2016. "Predicting the epidemic threshold of the susceptible-infected-recovered model." SCIENTIFIC REPORTS, Volume 6, 12 pp. https://doi.org/10.1038/srep24676
Abstract
Researchers have developed several theoretical methods for predicting epidemic thresholds, including the mean-field like (MFL) method, the quenched mean-field (QMF) method and the dynamical message passing (DMP) method. When these methods are applied to predict epidemic threshold they often produce differing results and their relative levels of accuracy are still unknown. We systematically analyze these two issues—relationships among differing results and levels of accuracy—by studying the susceptible-infected-recovered (SIR) model on uncorrelated configuration networks and a group of 56 real-world networks. In uncorrelated configuration networks the MFL and DMP methods yield identical predictions that are larger and more accurate than the prediction generated by the QMF method. As for the 56 real-world networks, the epidemic threshold obtained by the DMP method is more likely to reach the accurate epidemic threshold because it incorporates full network topology information and some dynamical correlations. We find that in most of the networks with positive degree-degree correlations, an eigenvector localized on the high k-core nodes, or a high level of clustering, the epidemic threshold predicted by the MFL method, which uses the degree distribution as the only input information, performs better than the other two methods.
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