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arXiv 2212.02149math.PR
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具有平均场相互作用的空间随机传染病模型的中心极限定理

Central limit theorem for a spatial stochastic epidemic model with mean field interaction

Maxime Hauray, Etienne Pardoux, Yen V. Vuong

摘要:

在这篇文章中,我们研究了一个相互作用的粒子系统的流行病学的背景下,个人(粒子)的特点是他们的位置和感染状态。我们开始在微观层面上的描述,其中个人的位移是由平均场的相互作用和状态依赖的扩散,而流行病学的动态描述的泊松过程与感染率的基础上分布的其他附近的个人,也是平均场类型。然后在适当的假设下,建立了一种形式的大数定律,证明了与上述系统相关的经验测度收敛于非线性McKean-Vlasov方程的唯一解的定律.作为一个自然的后续问题,我们研究这个随机系统在其极限附近的波动。我们证明了这个波动过程收敛到一个极限过程,它可以被刻画为线性随机偏微分方程的唯一解。与现有文献中采用耦合方法证明相互作用粒子系统的中心极限定理不同,本文的主要思想是利用半群形式和适当的估计,在适当的加权索伯列夫空间中直接研究涨落过程的线性化发展方程,并遵循希尔伯特方法.

英文摘要:

In this article, we study an interacting particle system in the context of epidemiology where the individuals (particles) are characterized by their position and infection state. We begin with a description at the microscopic level where the displacement of individuals is driven by mean field interactions and state-dependent diffusion, whereas the epidemiological dynamic is described by the Poisson processes with an infection rate based on the distribution of other nearby individuals, also of the mean-field type. Then under suitable assumptions, a form of law of large numbers has been established to show that the associated empirical measure to the above system converges to the law of the unique solution of a nonlinear McKean-Vlasov equation. As a natural follow-up question, we study the fluctuation of this stochastic system around its limit. We prove that this fluctuation process converges to a limit process, which can be characterized as the unique solution of a linear stochastic PDE. Unlike the existing literature using a coupling approach to prove the central limit theorem for interacting particle systems, the main idea in our proof is to use a semigroup formalism and some appropriate estimates to directly study the linearized evolution equation of the fluctuation process in a suitable weighted Sobolev space and follows a Hilbertian approach.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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