A growth-fragmentation-isolation process on random recursive trees and contact tracing

TL;DR

This paper models a stochastic process on recursive trees involving growth, fragmentation, and isolation, revealing a phase transition that impacts cluster dynamics and has applications in epidemic control and contact tracing.

Contribution

It introduces a novel growth-fragmentation-isolation process on recursive trees, analyzing phase transitions and cluster behavior with implications for epidemic management.

Findings

Identifies a phase transition where isolation halts growth and causes extinction.

Shows exponential increase in the number of clusters when the process survives.

Provides a limit law for the empirical distribution of clusters on recursive trees.

Abstract

We consider a random process on recursive trees, with three types of events. Vertices give birth at a constant rate (growth), each edge may be removed independently (fragmentation of the tree) and clusters (or trees) are frozen with a rate proportional to their sizes (isolation of connected component). A phase transition occurs when the isolation is able to stop the growth fragmentation process and cause extinction. When the process survives, the number of clusters increases exponentially and we prove that the normalized empirical measure of clusters a.s. converges to a limit law on recursive trees. We exploit the branching structure associated with the size of clusters, which is inherited from the splitting property of random recursive trees. This work is motivated by the control of epidemics and contact tracing where clusters correspond to trees of infected individuals that can be identified and isolated. We complement this work by providing results on the Malthusian exponent to describe the effect of control policies on epidemics.