Near-critical SIR epidemic on a random graph with given degrees

TL;DR

This paper analyzes the probability and size of large epidemics in a near-critical SIR model on random graphs with given degrees, revealing detailed behavior just above the epidemic threshold.

Contribution

It provides a precise characterization of near-critical epidemic behavior on random graphs with given degrees, extending existing results and confirming a conjecture on giant component size.

Findings

Probability of large epidemic near critical threshold

Size of large epidemic in near-critical regime

Improved bounds on giant component size

Abstract

Emergence of new diseases and elimination of existing diseases is a key public health issue. In mathematical models of epidemics, such phenomena involve the process of infections and recoveries passing through a critical threshold where the basic reproductive ratio is 1. In this paper, we study near-critical behaviour in the context of a susceptible-infective-recovered (SIR) epidemic on a random (multi)graph on $n$ vertices with a given degree sequence. We concentrate on the regime just above the threshold for the emergence of a large epidemic, where the basic reproductive ratio is $1 + \omega (n) n^{-1/3}$, with $\omega (n)$ tending to infinity slowly as the population size, $n$, tends to infinity. We determine the probability that a large epidemic occurs, and the size of a large epidemic. Our results require basic regularity conditions on the degree sequences, and the assumption that the third moment of the degree of a random susceptible vertex stays uniformly bounded as $n \to \infty$. As a corollary, we determine the probability and size of a large near-critical epidemic on a standard binomial random graph in the `sparse' regime, where the average degree is constant. As a further consequence of our method, we obtain an improved result on the size of the giant component in a random graph with given degrees just above the critical window, proving a conjecture by Janson and Luczak.