Accurate stochastic simulation algorithm for multiscale models of infectious diseases

TL;DR

This paper introduces a new exact stochastic simulation algorithm for multiscale infectious disease models that effectively handles non-Markovian dynamics, ensuring accuracy and efficiency across different scales.

Contribution

It presents a novel simulation algorithm capable of accurately and efficiently modeling multiscale infectious disease systems with non-Markovian properties.

Findings

The algorithm maintains accuracy with reasonable within-host data resolution.

It remains computationally efficient even at finer resolutions.

Applicable to various multiscale systems beyond infectious diseases.

Abstract

In the infectious disease literature, significant effort has been devoted to studying dynamics at a single scale. For example, compartmental models describing population-level dynamics are often formulated using differential equations. In cases where small numbers or noise play a crucial role, these differential equations are replaced with memoryless Markovian models, where discrete individuals can be members of a compartment and transition stochastically. Classic stochastic simulation algorithms, such as the next reaction method, can be employed to solve these Markovian models exactly. The intricate coupling between models at different scales underscores the importance of multiscale modelling in infectious diseases. However, several computational challenges arise when the multiscale model becomes non-Markovian. In this paper, we address these challenges by developing a novel exact stochastic simulation algorithm. We apply it to a showcase multiscale system where all individuals share the same deterministic within-host model while the population-level dynamics are governed by a stochastic formulation. We demonstrate that as long as the within-host information is harvested at a reasonable resolution, the novel algorithm will always be accurate. Furthermore, our implementation is still efficient even at finer resolutions. Beyond infectious disease modelling, the algorithm is widely applicable to other multiscale systems, providing a versatile, accurate, and computationally efficient framework.