# Individual based SIS models on (not so) dense large random networks

## TL;DR

This paper derives a limit integro-differential equation for SIS epidemic models on large random networks, covering dense and some sparse graphs, by analyzing the convergence of stochastic processes and graph structures.

## Contribution

It extends the understanding of epidemic dynamics on large networks by establishing a limit theorem that connects individual-based models to graphon-based equations.

## Key findings

- Limit theorem for SIS epidemic on large networks
- Convergence of random graphs to graphons
- Coupling method for stochastic process analysis

## Abstract

Starting from a stochastic individual-based description of an SIS epidemic spreading on a random network, we study the dynamics when the size $n$ of the network tends to infinity. We recover in the limit an infinite-dimensional integro-differential equation studied by Delmas, Dronnier and Zitt (2022) for an SIS epidemic propagating on a graphon. Our work covers the case of dense and sparse graphs, provided that the number of edges grows faster than $n$, but not the case of very sparse graphs with $O(n)$ edges. In order to establish our limit theorem, we have to deal with both the convergence of the random graphs to the graphon and the convergence of the stochastic process spreading on top of these random structures: in particular, we propose a coupling between the process of interest and an epidemic that spreads on the complete graph but with a modified infection rate. Keywords: Random graph, mathematical models of epidemics, measure-valued process, large network limit, limit theorem, graphon.