A message-passing approach to epidemic tracing and mitigation with apps

TL;DR

This paper presents a theoretical message-passing framework to analyze how contact tracing apps can effectively mitigate epidemics by increasing the epidemic threshold, especially when targeting high-degree nodes, supported by simulations.

Contribution

It introduces a novel message-passing approach combining percolation theory to quantify contact tracing app effectiveness in epidemic mitigation.

Findings

Higher app adoption raises the epidemic threshold.

Targeting high-degree nodes maximizes mitigation.

Analytical results align with Monte Carlo simulations.

Abstract

With the hit of new pandemic threats, scientific frameworks are needed to understand the unfolding of the epidemic. The use of mobile apps that are able to trace contacts is of utmost importance in order to control new infected cases and contain further propagation. Here we present a theoretical approach using both percolation and message--passing techniques, to the role of contact tracing, in mitigating an epidemic wave. We show how the increase of the app adoption level raises the value of the epidemic threshold, which is eventually maximized when high-degree nodes are preferentially targeted. Analytical results are compared with extensive Monte Carlo simulations showing good agreement for both homogeneous and heterogeneous networks. These results are important to quantify the level of adoption needed for contact-tracing apps to be effective in mitigating an epidemic.