论文标题
通过识别局部接触网络瓶颈有针对性的大流行遏制
Targeted Pandemic Containment Through Identifying Local Contact Network Bottlenecks
论文作者
论文摘要
关于大流行的缓解措施的决策通常依赖于模拟建模。通过接触网络或人口中心之间的接触网络传播模型 - 越来越多地用于这些目的。现实世界中的接触网络具有影响感染传播的结构特征，例如紧密联系的当地社区彼此之间相互薄弱。在本文中，我们提出了一种新的基于流动的边缘中心性方法，用于检测连接触点网络中节点的瓶颈边缘。特别是，我们基于p-norm网络流的扩散概念来利用凸优化公式。使用COVID-19的模拟模型通过个人和县级的真实网络数据传输的模拟模型，我们证明，针对通过拟议方法识别的瓶颈边缘可将受感染案例的数量减少到最高10％的范围，而不是先进的边缘中间方法。此外，所提出的方法是比现有方法快的数量级。
Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts--between individuals or between population centres--are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.
