The real-time growth rate of stochastic epidemics on random intersection graphs

TL;DR

This paper analyzes the early exponential growth rate of SIR epidemics on random intersection graphs with complex clustering, deriving a Malthusian parameter via branching process coupling.

Contribution

It introduces a novel approach to determine the growth rate of epidemics on clustered networks using branching process techniques.

Findings

Epidemics grow exponentially in large populations on these graphs.

The Malthusian parameter satisfies a variant of the Euler-Lotka equation.

The method applies to graphs with mixed Poisson degree distributions.

Abstract

This paper is concerned with the growth rate of SIR (Susceptible-Infectious-Recovered) epidemics with general infectious period distribution on random intersection graphs. This type of graph is characterized by the presence of cliques (fully connected subgraphs). We study epidemics on random intersection graphs with a mixed Poisson degree distribution and show that in the limit of large population sizes the number of infected individuals grows exponentially during the early phase of the epidemic, as is generally the case for epidemics on asymptotically unclustered networks. The Malthusian parameter is shown to satisfy a variant of the classical Euler-Lotka equation. To obtain these results we construct a coupling of the epidemic process and a continuous-time multitype branching process, where the type of an individual is (essentially) given by the length of its infectious period. Asymptotic results are then obtained via an embedded single-type Crump-Mode-Jagers branching process.