论文标题
接触跟踪网络中的流行源检测：图形和通过消息算法中的流行中心性
Epidemic Source Detection in Contact Tracing Networks: Epidemic Centrality in Graphs and Message-Passing Algorithms
论文作者
论文摘要
我们研究了使用流行病学中的易感性感染模型，研究了以图形约束的最大似然估计问题建模的接触追踪网络中的流行源检测问题。基于对感染子图的快照观察，我们首先研究了有限程度的常规图和带有循环的常规图，从而在有限的无链图和环形图的情况下建立了最大似然比的数学等效性。特别是，我们表明，最大似然估计器的最佳解决方案可以基于新颖的统计距离中心性来完善图形距离，该统计距离中心捕获了非凸问题的最佳性。然后提出了有效的接触式追踪算法，以解决具有多个循环的有限程度规范图的一般情况。我们在各种图表上的性能评估表明，使用SARS-COV 2003和COVID-19-Pandemics的触点跟踪数据，我们的算法的表现优于现有的最新启发式方法，通过正确识别一些最大的超级广播感染群体在新加坡和台湾的一些最大的超级广播感染群中。
We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs and regular graphs with cycles separately, thereby establishing a mathematical equivalence in maximal likelihood ratio between the case of finite acyclic graphs and that of cyclic graphs. In particular, we show that the optimal solution of the maximum likelihood estimator can be refined to distances on graphs based on a novel statistical distance centrality that captures the optimality of the nonconvex problem. An efficient contact tracing algorithm is then proposed to solve the general case of finite degree-regular graphs with multiple cycles. Our performance evaluation on a variety of graphs shows that our algorithms outperform the existing state-of-the-art heuristics using contact tracing data from the SARS-CoV 2003 and COVID-19 pandemics by correctly identifying the superspreaders on some of the largest superspreading infection clusters in Singapore and Taiwan.
