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arXiv 2301.04890math.PR
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空间随机图上流行病的分支过程与均匀化

Branching process and homogeneization for epidemics on spatial random graphs

Vincent Bansaye, Michele Salvi

AI总结:

研究空间随机图上的流行病演化过程,通过构造适定模型并证明其流体力学极限为反应扩散方程,利用随机均匀化确定扩散系数,扩展经典估计以应对不可逆性,并推广至含边渗流的复杂图。

摘要:

考虑一个图,其节点根据 $\mathbb R^n$ 上的 Poisson 点过程在空间中分布。我们研究在该网络上演化的种群,个体以随距离指数衰减的速率在节点间跳跃。个体在每个节点上也以恒定速率发生出生(感染)和死亡(恢复)。首先,我们构造了该过程,证明即使从非有界初始条件出发,它也是适定的。其次,我们在扩散标度下证明了流体力学极限。极限过程遵循确定性的反应扩散方程。我们使用随机均匀化将其扩散系数表征为变分原理的解。证明特别涉及对经典 Kipnis-Varadhan 估计的扩展,以应对由出生和死亡导致的过程不可逆性。这项工作受到大型网络上流行病近似的启发,结果被扩展到包括边渗流的更复杂图。

英文摘要:

Consider a graph where the sites are distributed in space according to a Poisson point process on $\mathbb R^n$. We study a population evolving on this network, with individuals jumping between sites with a rate which decreases exponentially in the distance. Individuals give also birth (infection) and die (recovery) at constant rate on each site. First, we construct the process, showing that it is well-posed even when starting from non-bounded initial conditions. Secondly, we prove hydrodynamic limits in a diffusive scaling. The limiting process follows a deterministic reaction diffusion equation. We use stochastic homogenization to characterize its diffusion coefficient as the solution of a variational principle. The proof involves in particular the extension of a classic Kipnis-Varadhan estimate to cope with the non-reversiblity of the process, due to births and deaths. This work is motivated by the approximation of epidemics on large networks and the results are extended to more complex graphs including percolation of edges.

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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