SIR epidemics in populations with large sub-communities

TL;DR

This paper analyzes the final outcomes of SIR epidemics in populations with large sub-communities, revealing how intra- and inter-community transmission affect epidemic severity and variability.

Contribution

It introduces a new approximation method for epidemics in large sub-communities and derives LLN and CLT results, including a novel convergence rate for the expected infected fraction.

Findings

Within-community outbreaks dominate transmission dynamics.

Between-community connections introduce randomness in large populations.

Derived convergence rate for expected infected fraction in standard SIR models.

Abstract

We investigate final outcome properties of an SIR (susceptible $\to$ infective $\to$ recovered) epidemic model defined on a population of large sub-communities in which there is stronger disease transmission within the communities than between them. Our analysis involves approximation of the epidemic process by a chain of within-community large outbreaks spreading between the communities. We derive law of large numbers and central limit type results for the number of individuals and the number of communities affected and the so-called severity of the outbreak. These results are valid as the size of communities tends to infinity, with the number of communities either fixed or also tending to infinity. The weaker between-community connections lead to randomness even in the law of large numbers type limit. As part of our proofs we also obtain a new result concerning the rate of convergence of the expected fraction infected in a standard SIR epidemic to its large-population limit.