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A SIR Stochastic Epidemic Model in Continuous Space: Law of Large Numbers and Central Limit Theorem

The impact of spatial structure on the spread of an epidemic is an important issue in the propagation of infectious diseases. Recent studies, both deterministic and stochastic, have made it possible to understand the importance of the movement of individuals in a population on the persistence or extinction of an endemic disease. In this paper we study a compartmental SIR stochastic epidemic model for a population that moves on $R^{d}$ following SDEs driven by independent Brownian motions. We define the sequences of empirical measures, which describe the evolution of the positions of the susceptible, infected and removed individuals. Next, we obtain large population approximations of those sequence of measures, as weak solution of a system of reaction-diffusion equations. Finaly we study the fluctuations of the stochastic model around its large population limit via the central limit theorem. The limit is a distribution valued Ornstein-Uhlenbeck process and can be represented as the solution of system of stochastic partial differential equations.