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

Starting from a stochastic individual based description of an SIS epidemic spreading on a random network, we study the dynamics when the size 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, when the number of edges is of order $\Theta(n^a)$ with $a>1$, but not the case of very sparse graphs with $a=1$. In order to establish our limit theorem, we have to deal with both the convergence of the graph 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 infectivity.
Comment: Keywords: random graph, mathematical model for epidemiology, measure-valued process, large network limit, limit theorem, dense graph, sparse graph. Acknowledgments: This work was financed by the Labex B\'ezout (ANR-10-LABX-58) and the platform MODCOV19 of the National Institute of Mathematical Sciences and their Interactions of CNRS. 30 pages, including an appendix of 5 pages