Predicting the epidemic threshold of the susceptible-infected-recovered model.
Predicting the epidemic threshold of the susceptible-infected-recovered model.
复制标题
易感者-感染者-康复模型的流行阈值预测
DOI：
10.1038/srep24676
复制
发表时间：
2016-04-19
影响因子：
4.6
通讯作者：
Stanley HE
中科院分区：
文献类型：
--
作者：
Wang W;Liu QH;Zhong LF;Tang M;Gao H;Stanley HE
研究人员已经发展了几种预测流行阈值的理论方法，包括类平均场（MFL）方法、猝灭平均场（QMF）方法和动态消息传递（DIM）方法。当这些方法应用于预测流行阈值时，它们往往产生不同的结果，并且它们的相对准确度仍然未知。我们系统地分析了这两个问题之间的关系不同的结果和准确性水平，通过研究不相关的配置网络和一组56个真实世界的网络的易感染的恢复（SIR）模型。在不相关的组态网络中，MFL和QMF方法产生相同的预测，比QMF方法产生的预测更大，更准确。对于56个真实网络，由于该方法包含了完整的网络拓扑信息和一定的动态相关性，因此更有可能达到准确的流行阈值。我们发现，在大多数的网络与积极的度度相关性，本地化的高k-核心节点，或一个高层次的聚类的特征向量，由MFL方法，它使用的度分布作为唯一的输入信息预测的流行阈值，表现优于其他两种方法。
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 highk-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.