Predicting the epidemic threshold of the susceptible-infected-recovered model.
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.
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.|Big data research center, University of Electronic Science and Technology of China, Chengdu 610054, China.|Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
Wei
W
Wang
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.|Big data research center, University of Electronic Science and Technology of China, Chengdu 610054, China.
Quan-Hui
QH
Liu
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.|Big data research center, University of Electronic Science and Technology of China, Chengdu 610054, China.
Lin-Feng
LF
Zhong
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.|Big data research center, University of Electronic Science and Technology of China, Chengdu 610054, China.
Ming
M
Tang
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.|Big data research center, University of Electronic Science and Technology of China, Chengdu 610054, China.
Hui
H
Gao
Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
H Eugene
HE
Stanley
D016000:Cluster Analysis; D004198:Disease Susceptibility; D058872:Epidemics; D006801:Humans; D008962:Models, Theoretical