An SIR-like kinetic model tracking individuals' viral load

TL;DR

This paper develops a kinetic multi-agent model to study how individual viral load dynamics influence epidemic spread, incorporating heterogeneity and testing strategies, and derives macroscopic equations validated by simulations.

Contribution

It introduces a novel kinetic model linking viral load evolution with epidemic dynamics, including isolation strategies based on viral load, and derives macroscopic equations from microscopic interactions.

Findings

Macroscopic models match particle simulations well.

Viral load-dependent testing impacts epidemic control.

Heterogeneity in viral load influences transmission dynamics.

Abstract

Mathematical models are formal and simplified representations of the knowledge related to a phenomenon. In classical epidemic models, a neglected aspect is the heterogeneity of disease transmission and progression linked to the viral load of each infectious individual. Here, we attempt to investigate the interplay between the evolution of individuals' viral load and the epidemic dynamics from a theoretical point of view. In the framework of multi-agent systems, we propose a particle stochastic model describing the infection transmission through interactions among agents and the individual physiological course of the disease. Agents have a double microscopic state: a discrete label, that denotes the epidemiological compartment to which they belong and switches in consequence of a Markovian process, and a microscopic trait, representing a normalized measure of their viral load, that changes in consequence of binary interactions or interactions with a background. Specifically, we consider Susceptible--Infected--Removed--like dynamics where infectious individuals may be isolated from the general population and the isolation rate may depend on the viral load sensitivity and frequency of tests. We derive kinetic evolution equations for the distribution functions of the viral load of the individuals in each compartment, whence, via suitable upscaling procedures, we obtain a macroscopic model for the densities and viral load momentum. We perform then a qualitative analysis of the ensuing macroscopic model, and we present numerical tests in the case of both constant and viral load-dependent isolation control. Also, the matching between the aggregate trends obtained from the macroscopic descriptions and the original particle dynamics simulated by a Monte Carlo approach is investigated.