Wang Bing, Tanaka Gouhei, Suzuki Hideyuki, Aihara Kazuyuki
FIRST, Aihara Innovative Mathematical Modelling Project, Japan Science and Technology Agency and Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
Graduate School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Sep;90(3):032806. doi: 10.1103/PhysRevE.90.032806. Epub 2014 Sep 16.
Numerous real-world networks have been observed to interact with each other, resulting in interconnected networks that exhibit diverse, nontrivial behavior with dynamical processes. Here we investigate epidemic spreading on interconnected networks at the level of metapopulation. Through a mean-field approximation for a metapopulation model, we find that both the interaction network topology and the mobility probabilities between subnetworks jointly influence the epidemic spread. Depending on the interaction between subnetworks, proper controls of mobility can efficiently mitigate epidemics, whereas an extremely biased mobility to one subnetwork will typically cause a severe outbreak and promote the epidemic spreading. Our analysis provides a basic framework for better understanding of epidemic behavior in related transportation systems as well as for better control of epidemics by guiding human mobility patterns.
人们观察到许多现实世界中的网络会相互作用，从而形成相互连接的网络，这些网络在动态过程中表现出多样且复杂的行为。在此，我们研究在集合种群层面上相互连接的网络中的疫情传播。通过对集合种群模型的平均场近似，我们发现交互网络拓扑结构和子网之间的迁移概率共同影响疫情传播。根据子网之间的相互作用，适当控制迁移可以有效缓解疫情，而向一个子网的极端偏向性迁移通常会导致严重爆发并促进疫情传播。我们的分析为更好地理解相关交通系统中的疫情行为以及通过引导人类移动模式更好地控制疫情提供了一个基本框架。
