Kemeny-based testing for COVID-19.
Kemeny-based testing for COVID-19.
复制标题
DOI：
10.1371/journal.pone.0242401
复制
发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Shorten R
中科院分区：
文献类型：
--
作者：
Yilmaz S;Dudkina E;Bin M;Crisostomi E;Ferraro P;Murray-Smith R;Parisini T;Stone L;Shorten R
检测、跟踪和追踪能力已被确定为帮助各国在第一波COVID-19病毒后安全重新开放活动的关键。联系人追踪应用程序为重建日常联系人图表提供了前所未有的可能性，所以问题是：谁应该接受测试？由于人类接触网络是已知的社区结构，在本文中，我们证明了一个图的Kemeny常数可以用来识别和分析社区之间的桥梁图。我们的“Kemeny指标”是新图中的Kemeny常数的值，该值是从原始图中删除节点时获得的。我们表明，测试与Kemeny指标的大值相关的个体可以帮助有效地拦截新的病毒爆发，当它们仍处于早期阶段时。广泛的模拟在早期识别和阻断可能在不同社区之间传播疾病的“超级传播者”联系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 value of the Kemeny constant in the new graph that is obtained when a node is removed from the original 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 the possible ‘super-spreaders’ links that transmit disease between different communities.
登录
查看更多内容
影响因子：
0.5
作者：
Grolmusz, Vince
通讯作者：
Grolmusz, Vince
影响因子：
1.1
作者：
Cho, GE;Meyer, CD
通讯作者：
Meyer, CD
影响因子：
8.6
作者：
Cohen, R;Havlin, S;ben-Avraham, D
通讯作者：
ben-Avraham, D
影响因子：
9.3
作者：
Jarvis, Christopher I.;Van Zandvoort, Kevin;Edmunds, W. John
通讯作者：
Edmunds, W. John
影响因子：
0.7
作者：
Breen, Jane;Butler, Steve;Riesen, Jacob
通讯作者：
Riesen, Jacob