Individual-based foundation of SIR-type epidemic models: mean-field limit and large-time behaviour

Abstract We introduce a kinetic framework for modelling the time evolution of the statistical distributions of the population densities in the three compartments of susceptible, infectious and recovered individuals, under epidemic spreading driven by susceptible-infectious interactions. The model is based on a system of Boltzmann-type equations describing binary interactions between susceptible and infectious individuals, supplemented with linear redistribution operators that account for recovery and reinfection dynamics. The mean values of the kinetic system recover a SIR-type model with reinfection, where the macroscopic parameters are explicitly derived from the underlying microscopic interaction rules. In the grazing collision regime, the Boltzmann system can be approximated by a system of coupled Fokker–Planck equations. This limit allows for a more tractable analysis of the dynamics, including the large-time behaviour of the population densities. In this context, we rigorously prove the convergence to equilibrium of the resulting mean-field system in a suitable Sobolev space by means of the so-called energy distance. The analysis reveals the dissipative structure of the dynamics and the role of the interaction terms in driving the system towards a stable equilibrium configuration. These results provide a multi-scale perspective connecting kinetic theory with classical epidemic models.