A kinetic theory for age-structured stochastic birth-death processes

TL;DR

This paper develops a new stochastic kinetic theory for age-structured populations that incorporates stochastic fluctuations, age-dependent birth and death rates, and population-size effects, extending classical models.

Contribution

It introduces a systematic derivation of a hierarchy of kinetic equations for stochastic age-structured populations, generalizing existing deterministic and master equation models.

Findings

Derived explicit solutions in two simple limits.

Compared stochastic results with mean-field models.

Provided a framework for modeling age- and population-dependent stochastic dynamics.

Abstract

Classical age-structured mass-action models such as the McKendrick-von Foerster equation have been extensively studied but they are structurally unable to describe stochastic fluctuations or population-size-dependent birth and death rates. Stochastic theories that treat semi-Markov age-dependent processes using e.g., the Bellman-Harris equation, do not resolve a population's age-structure and are unable to quantify population-size dependencies. Conversely, current theories that include size-dependent population dynamics (e.g., mathematical models that include carrying capacity such as the Logistic equation) cannot be easily extended to take into account age-dependent birth and death rates. In this paper, we present a systematic derivation of a new fully stochastic kinetic theory for interacting age-structured populations. By defining multiparticle probability density functions, we derive a hierarchy of kinetic equations for the stochastic evolution of an ageing population undergoing birth and death. We show that the fully stochastic age-dependent birth-death process precludes factorization of the corresponding probability densities, which then must be solved by using a BBGKY-like hierarchy. However, explicit solutions are derived in two simple limits and compared with their corresponding mean-field results. Our results generalize both deterministic models and existing master equation approaches by providing an intuitive and efficient way to simultaneously model age- and population-dependent stochastic dynamics applicable to the study of demography, stem cell dynamics, and disease evolution.