Converging towards the optimal path to extinction

TL;DR

This paper explores the concept of an optimal path to extinction in stochastic models, linking it to dynamical systems theory and sensitive dependence, with applications in epidemics and other fields.

Contribution

It introduces a dynamical systems perspective to identify the optimal extinction path, connecting sensitive dependence to stochastic extinction events.

Findings

Optimal path maximizes extinction probability

Sensitive dependence relates to the optimal extinction route

Dynamical systems approach applies to epidemic models

Abstract

Extinction appears ubiquitously in many fields, including chemical reactions, population biology, evolution, and epidemiology. Even though extinction as a random process is a rare event, its occurrence is observed in large finite populations. Extinction occurs when fluctuations due to random transitions act as an effective force which drives one or more components or species to vanish. Although there are many random paths to an extinct state, there is an optimal path that maximizes the probability to extinction. In this article, we show that the optimal path is associated with the dynamical systems idea of having maximum sensitive dependence to initial conditions. Using the equivalence between the sensitive dependence and the path to extinction, we show that the dynamical systems picture of extinction evolves naturally toward the optimal path in several stochastic models of epidemics.