Kemeny-based testing for COVID-19

TL;DR

This paper introduces a novel graph-based method using the Kemeny constant to identify key individuals or links in contact networks for COVID-19 testing, aiming to intercept outbreaks early and prevent super-spreader events.

Contribution

The paper proposes the Kemeny indicator as a new metric to detect critical nodes and edges in contact graphs, enhancing targeted testing strategies for COVID-19.

Findings

Kemeny indicator effectively identifies bridge nodes between communities.

Testing based on the indicator improves early outbreak detection.

Simulation results show potential in preventing super-spreader events.

Abstract

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.