Branching process and homogeneization for epidemics on spatial random graphs

TL;DR

This paper models epidemic spread on spatial random graphs using a stochastic process, deriving a reaction-diffusion limit and characterizing its diffusion coefficient through homogenization, with implications for large network epidemic approximations.

Contribution

It constructs a well-posed epidemic process on spatial Poisson graphs and establishes hydrodynamic limits with a novel homogenization approach for non-reversible dynamics.

Findings

Derivation of a deterministic reaction-diffusion equation as the hydrodynamic limit.

Characterization of the diffusion coefficient via a variational principle.

Extension of homogenization techniques to non-reversible epidemic processes.

Abstract

Consider a graph where the sites are distributed in space according to a Poisson point process on $\mathbb R^n$. We study a population evolving on this network, with individuals jumping between sites with a rate which decreases exponentially in the distance. Individuals give also birth (infection) and die (recovery) at constant rate on each site. First, we construct the process, showing that it is well-posed even when starting from non-bounded initial conditions. Secondly, we prove hydrodynamic limits in a diffusive scaling. The limiting process follows a deterministic reaction diffusion equation. We use stochastic homogenization to characterize its diffusion coefficient as the solution of a variational principle. The proof involves in particular the extension of a classic Kipnis-Varadhan estimate to cope with the non-reversiblity of the process, due to births and deaths. This work is motivated by the approximation of epidemics on large networks and the results are extended to more complex graphs including percolation of edges.