论文标题
基于Kemeny的COVID-19
Kemeny-based testing for COVID-19
论文作者
论文摘要
测试，跟踪和追踪能力已被确定为在Covid-19病毒的第一波浪潮之后帮助各国安全重新开放活动的关键。接触示踪应用程序给重建日常联系的前所未有的可能性，所以问题是谁应该进行测试？由于人类接触网络已知可以表现出社区结构，因此在本文中，我们表明图形的凯梅尼常数可用于识别和分析图中社区之间的桥梁。我们的“ Kemeny指示器”是从图中删除节点或边缘时Kemeny常数的变化。我们表明，与大量凯门尼指标相关的人的测试可以帮助有效拦截新病毒爆发，而他们仍处于早期阶段。广泛的模拟在早期识别和阻止可能在不同社区之间传播疾病的可能的“超级宣传者”链接方面提供了有希望的结果。
Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Contact tracing apps give the unprecedented possibility to reconstruct graphs of daily contacts, so the question is who should be tested? As human contact networks are known to exhibit community structure, in this paper we show that the Kemeny constant of a graph can be used to identify and analyze bridges between communities in a graph. Our "Kemeny indicator" is the change in Kemeny constant when a node or edge is removed from the graph. We show that testing individuals who are associated with large values of the Kemeny indicator can help in efficiently intercepting new virus outbreaks, when they are still in their early stage. Extensive simulations provide promising results in early identification and in blocking possible "super-spreaders" links that transmit disease between different communities.
