When is a network epidemic hard to eliminate?

TL;DR

This paper analyzes the difficulty of eradicating epidemics on networks by establishing lower bounds on extinction time based on network resistance and curing budget, revealing a phase transition in epidemic persistence.

Contribution

It introduces a lower bound on epidemic extinction time for bounded degree graphs, linking network resistance and curing budget, and identifies a phase transition in epidemic persistence.

Findings

Expected extinction time grows exponentially if curing budget is below a threshold.

Resistance and CutWidth are key parameters determining epidemic persistence.

Sharp phase transition between quick eradication and long-lasting epidemics.

Abstract

We consider the propagation of a contagion process (epidemic) on a network and study the problem of dynamically allocating a fixed curing budget to the nodes of the graph, at each time instant. For bounded degree graphs, we provide a lower bound on the expected time to extinction under any such dynamic allocation policy, in terms of a combinatorial quantity that we call the resistance of the set of initially infected nodes, the available budget, and the number of nodes n. Specifically, we consider the case of bounded degree graphs, with the resistance growing linearly in n. We show that if the curing budget is less than a certain multiple of the resistance, then the expected time to extinction grows exponentially with n. As a corollary, if all nodes are initially infected and the CutWidth of the graph grows linearly, while the curing budget is less than a certain multiple of the CutWidth, then the expected time to extinction grows exponentially in n. The combination of the latter with our prior work establishes a fairly sharp phase transition on the expected time to extinction (sub-linear versus exponential) based on the relation between the CutWidth and the curing budget.