Law of large numbers for the SIR model with random vertex weights on Erd\H{o}s-R\'{e}nyi graph

TL;DR

This paper establishes a law of large numbers for the SIR epidemic model with random vertex weights on Erdős-Rényi graphs, describing the asymptotic behavior of susceptible and infective vertices as the network size grows.

Contribution

It introduces a rigorous law of large numbers for the SIR model with random vertex weights on Erdős-Rényi graphs, providing deterministic limits for key epidemic quantities.

Findings

Proves convergence of susceptible vertex proportion to a deterministic function.

Shows the mean infective capability converges to a deterministic limit.

Provides explicit functions describing epidemic dynamics over time.

Abstract

In this paper we are concerned with the SIR model with random vertex weights on Erd\H{o}s-R\'{e}nyi graph $G(n,p)$. The Erd\H{o}s-R\'{e}nyi graph $G(n,p)$ is generated from the complete graph $C_n$ with $n$ vertices through independently deleting each edge with probability $(1-p)$. We assign i. i. d. copies of a positive r. v. $\rho$ on each vertex as the vertex weights. For the SIR model, each vertex is in one of the three states `susceptible', `infective' and `removed'. An infective vertex infects a given susceptible neighbor at rate proportional to the production of the weights of these two vertices. An infective vertex becomes removed at a constant rate. A removed vertex will never be infected again. We assume that at $t=0$ there is no removed vertex and the number of infective vertices follows a Bernoulli distribution $B(n,\theta)$. Our main result is a law of large numbers of the model. We give two deterministic functions $H_S(\psi_t), H_V(\psi_t)$ for $t\geq 0$ and show that for any $t\geq 0$, $H_S(\psi_t)$ is the limit proportion of susceptible vertices and $H_V(\psi_t)$ is the limit of the mean capability of an infective vertex to infect a given susceptible neighbor at moment $t$ as $n$ grows to infinity.