On an epidemic model on finite graphs. (English) Zbl 1434.82074

Summary: We study a system of random walks, known as the frog model, starting from a profile of independent Poisson \((\lambda)\) particles per site, with one additional active particle planted at some vertex \(\mathbf{o}\) of a finite connected simple graph \(G=(V,E)\). Initially, only the particles occupying \(\mathbf{o}\) are active. Active particles perform \(t\in \mathbb{N}\cup\{\infty\}\) steps of the walk they picked before vanishing and activate all inactive particles they hit. This system is often taken as a model for the spread of an epidemic over a population. Let \(\mathcal{R}_t\) be the set of vertices which are visited by the process, when active particles vanish after \(t\) steps. We study the susceptibility of the process on the underlying graph, defined as the random quantity \(\mathcal{S}(G):=\inf\{t:\mathcal{R}_t=V\}\) (essentially, the shortest particles’ lifespan required for the entire population to get infected). We consider the cases that the underlying graph is either a regular expander or a \(d\)-dimensional torus of side length \(n\) (for all \(d\ge 1)\mathbb{T}_d(n)\) and determine the asymptotic behavior of \(\mathcal{S}\) up to a constant factor. In fact, throughout we allow the particle density \(\lambda\) to depend on \(n\) and for \(d\ge 2\) we determine the asymptotic behavior of \(\mathcal{S}(\mathbb{T}_d(n))\) up to smaller order terms for a wide range of \(\lambda=\lambda_n\).


82C41 Dynamics of random walks, random surfaces, lattice animals, etc. in time-dependent statistical mechanics
60K35 Interacting random processes; statistical mechanics type models; percolation theory
82B43 Percolation
60J10 Markov chains (discrete-time Markov processes on discrete state spaces)
92D30 Epidemiology
Full Text: DOI arXiv Euclid


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