School of Automation, Northwestern Polytechnical University (NWPU), Xi'an, Shaanxi 710072, China.
School of Computer Science and Engineering, NWPU, Xi'an, Shaanxi 710072, China.
Chaos. 2019 Jul;29(7):073111. doi: 10.1063/1.5093047.
Spatial epidemic spreading, a fundamental dynamical process upon complex networks, attracts huge research interest during the past few decades. To suppress the spreading of epidemic, a couple of effective methods have been proposed, including node vaccination. Under such a scenario, nodes are immunized passively and fail to reveal the mechanisms of active activity. Here, we suggest one novel model of an observer node, which can identify infection through interacting with infected neighbors and inform the other neighbors for vaccination, on multiplex networks, consisting of epidemic spreading layer and information spreading layer. In detail, the epidemic spreading layer supports susceptible-infected-recovered process, while observer nodes will be selected according to several algorithms derived from percolation theory. Numerical simulation results show that the algorithm based on large degree performs better than random placement, while the algorithm based on nodes' degree in the information spreading layer performs the best (i.e., the best suppression efficacy is guaranteed when placing observer nodes based on nodes' degree in the information spreading layer). With the help of state probability transition equation, the above phenomena can be validated accurately. Our work thus may shed new light into understanding control of empirical epidemic control.
空间流行病传播是复杂网络上的一个基本动态过程，在过去几十年中引起了巨大的研究兴趣。为了抑制流行病的传播，已经提出了一些有效的方法，包括节点接种。在这种情况下，节点是被动免疫的，无法揭示主动活动的机制。在这里，我们在由传染病传播层和信息传播层组成的多重网络上，提出了一种新型的观测节点模型，该模型可以通过与感染邻居交互并通知其他邻居接种疫苗来识别感染。具体来说，传染病传播层支持易感染-感染-恢复过程，而观测节点将根据从渗流理论中导出的几种算法进行选择。数值模拟结果表明，基于大度数的算法比随机放置的算法表现更好，而基于信息传播层中节点度数的算法表现最好（即，基于信息传播层中节点度数放置观测节点可以保证最佳的抑制效果）。借助状态概率转移方程，可以准确地验证上述现象。因此，我们的工作可能为理解经验性流行病控制提供新的视角。
