发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出统一框架分析分层网络流行病，引入局部指标量化节点级传播，并据此构建可几何规划求解的最优缓解策略，优于现有网络级方法。
AI 中文摘要
本文提出了一个统一框架，用于分析分层网络化流行病过程，其中人口（以网络化SIS或SIR模型表示）与基础设施网络耦合。我们提出了新颖的局部指标，能够在节点层面分析流行病的局部传播，并在某些充分条件下预测网络范围的传播。受模型双层结构的启发，我们将分层网络划分为层内和层间交互。我们确定了在层层面上的条件，这些条件允许根据分层网络的物理结构来刻画网络级基本再生数。然后，我们利用局部指标，基于一个基本概念提出流行病缓解的优化框架：网络范围的传播可以通过局部（节点级）流行病负担的聚合来量化。我们保证基于局部指标的优化可以作为几何规划求解。我们通过数值示例说明了理论结果，并评估了局部指标及其所支持的优化框架相对于其他基于网络级指标的现有流行病缓解方法的性能。
英文摘要
In this paper, we propose a unifying framework to analyze layered networked epidemic processes where the population, represented either as a networked SIS or SIR model, is coupled with an infrastructure network. We propose novel local metrics that enable the analysis of the epidemic spreading locally (node-level) and the prediction of the network-wide spreading under some sufficient conditions. Inspired by the bilayer nature of our model, we partition the layered network between intra- and inter-layer interactions. We identify conditions at the layer level which allow the characterization of the network-level basic reproduction number in terms of the physical structure of the layered network. Then, we leverage the local metrics to propose an optimization framework for epidemic mitigation based on a fundamental notion: the network-wide spreading can be quantified by the aggregation of the local (node-level) epidemic burden. We provide guarantees that the local metric-based optimization can be solved as a geometric program. We illustrate our theoretical results via numerical examples and evaluate the performance of the local metrics and the optimization framework they enable versus other existing epidemic mitigation approaches based on network-level metrics.
