Exact solution of a stochastic SIR model

TL;DR

This paper presents an exact analytical solution to a stochastic SIR epidemic model on a linear chain, revealing detailed dynamics and stationary distributions influenced by initial conditions and fluctuations.

Contribution

It introduces an exact solution method for the stochastic SIR model using a quantum formulation, applicable to arbitrary initial conditions on a linear chain.

Findings

Exact time evolution of the SIR model derived analytically.

Finite stationary distribution of susceptibles observed due to fluctuations.

Comparison with simulations and mean-field theory validates the analytical results.

Abstract

The susceptible-infectious-recovered (SIR) model describes the evolution of three species of individuals which are subject to an infection and recovery mechanism. A susceptible $S$ can become infectious with an infection rate $\beta$ by an infectious $I$- type provided that both are in contact. The $I$- type may recover with a rate $\gamma$ and from then on stay immune. Due to the coupling between the different individuals, the model is nonlinear and out of equilibrium. We adopt a stochastic individual-based description where individuals are represented by nodes of a graph and contact is defined by the links of the graph. Mapping the underlying Master equation into a quantum formulation in terms of spin operators, the hierarchy of evolution equations can be solved exactly for arbitrary initial conditions on a linear chain. In case of uncorrelated random initial conditions the exact time evolution for all three individuals of the SIR model is given analytically. Depending on the initial conditions and reaction rates $\beta$ and $\gamma$, the $I$-population may increase initially before decaying to zero. Due to fluctuations, isolated regions of susceptible individuals evolve and unlike in the standard mean-field SIR model one observes a finite stationary distribution of the $S$-type even for large population size. The exact results for the ensemble averaged population size are compared with simulations for single realizations of the process and also with standard mean field theory which is expected to be valid on large fully-connected graphs.