Delay compartment models from a stochastic process

TL;DR

This paper derives delay compartment models from stochastic processes, clarifying when delays occur and providing an exact simulation method, with applications in epidemiology and pharmacokinetics.

Contribution

It introduces a stochastic process derivation of delay models, establishes conditions for delay distributions, and develops an exact simulation algorithm for small populations.

Findings

Delay terms arise from delay exponential waiting times.

Conditions for delay exponential as a probability distribution are established.

An exact stochastic simulation method is validated in epidemiology and pharmacokinetics.

Abstract

Compartment models with delay terms are widely used across a range of disciplines. The motivation to include delay terms varies across different contexts. In epidemiological and pharmacokinetic models, the delays are often used to represent an incubation period. In this work, we derive a compartment model with delay terms from an underlying non-Markov stochastic process. Delay terms arise when waiting times are drawn from a delay exponential distribution. This stochastic process approach allows us to preserve the physicality of the model, gaining understanding into the conditions under which delay terms can arise. By providing the conditions under which the delay exponential function is a probability distribution, we establish a critical value for the delay terms. An exact stochastic simulation method is introduced for the generalized model, enabling us to utilize the simulation in scenarios where intrinsic stochasticity is significant, such as when the population size is small. We illustrate the applications of the model and validate our simulation algorithm on examples drawn from epidemiology and pharmacokinetics.