Computation of random time-shift distributions for stochastic population models

TL;DR

This paper introduces an efficient numerical method to compute the distribution of random time-shifts in stochastic population models, enabling accurate macro-scale predictions without extensive simulations.

Contribution

The authors develop a novel, automated approach to calculate time-shift distributions in stochastic models, improving upon deterministic approximations by capturing initial noise effects.

Findings

Method effectively computes time-shift distributions for various models.

Application demonstrated on epidemic and viral dynamics models.

Provides a practical tool for macro-scale stochastic modeling.

Abstract

Even in large systems, the effect of noise arising from when populations are initially small can persist to be measurable on the macroscale. A deterministic approximation to a stochastic model will fail to capture this effect, but it can be accurately approximated by including an additional random time-shift to the initial conditions. We present a efficient numerical method to compute this time-shift distribution for a large class of stochastic models. The method relies on differentiation of certain functional equations, which we show can be effectively automated by deriving rules for different types of model rates that arise commonly when mass-action mixing is assumed. Explicit computation of the time-shift distribution can be used to build a practical tool for the efficient generation of macroscopic trajectories of stochastic population models, without the need for costly stochastic simulations. Full code is provided to implement this and we demonstrate our method on an epidemic model and a model of within-host viral dynamics.