Rare Event Extinction on Stochastic Networks

TL;DR

This paper extends large deviation theory to stochastic networks to predict rare extinction events, analyzing mean extinction times and paths in epidemic models on Erdos-Renyi networks, validated by simulations.

Contribution

It introduces a novel application of large deviations to network extinction processes, providing a framework to predict rare event timings and pathways.

Findings

Mean extinction times scale with network and epidemiological parameters.

Most probable extinction paths can be identified using the extended theory.

Predictions align well with Monte Carlo simulation results.

Abstract

We consider the problem of extinction processes on random networks with a given structure. For sufficiently large well-mixed populations, the process of extinction of one or more state variable components occurs in the tail of the quasi-stationary probability distribution, thereby making it a rare event. Here we show how to extend the theory of large deviations to random networks to predict extinction times. In particular, we use the theory to find the most probable path leading to extinction. We apply the methodology to epidemic models and discover how mean extinction times scale with epidemiological and network parameters in Erdos-Renyi networks. The results are shown to compare quite well with Monte Carlo simulations of the network in predicting both the most probable paths to extinction and mean extinction times.