From Individual-Based Models to Macroscopic Dynamics of Antimicrobial Resistance

TL;DR

This paper develops a kinetic model to analyze how antimicrobial use influences the spread of resistance, bridging microscopic interactions with macroscopic epidemic dynamics.

Contribution

It introduces a Boltzmann-type kinetic framework and derives coupled Fokker-Planck equations to study antimicrobial resistance evolution.

Findings

The model predicts long-term stable equilibria of resistant populations.

Inappropriate antimicrobial use accelerates resistance spread.

The Fokker-Planck approximation simplifies analysis of resistance dynamics.

Abstract

We introduce and discuss a kinetic framework describing the time evolution of the statistical distributions of a population divided into the compartments of susceptible, infectious, recovered, and resistant in the presence of a microbial infection driven by susceptible infectious interactions. Our main objective is to quantify the impact of excessive and inappropriate antimicrobial use, which accelerates the spread of resistance by enabling a fraction of infectious individuals to transition into the resistant compartment. The model consists of a system of Boltzmann type equations capturing binary interactions between susceptible and infectious individuals, complemented by linear redistribution operators that represent recovery, the development of resistance, and reinfection processes. In the grazing collision limit, we show that this Boltzmann system is well approximated by a system of coupled Fokker Planck equations. This limiting description allows for a more tractable analysis of the dynamics, including the characterization of the long-time behavior of the population densities. Our analysis highlights how interaction terms drive the system toward a stable equilibrium and quantifies the effects of inappropriate antimicrobial use on the distribution of resistant individuals. Overall, the results offer a multiscale perspective that bridges kinetic theory with classical epidemic modeling.