流行病SIS模型的高效加权集成网络模拟
Efficient weighted-ensemble network simulations of the SIS model of epidemics
AI总结：
针对标准动力学蒙特卡罗模拟在过渡路径采样和罕见事件识别中的不足，本文将加权集成方法应用于大型异质种群网络的SIS模型，实现了对罕见事件的高效采样和复杂路径探索，并在新参数范围计算了灭绝平均时间及准平稳分布。
摘要：
标准动力学蒙特卡罗模拟中不稳定路径的存在严重影响了过渡路径的准确模拟与采样。虽然Gillespie算法等通常可靠的方法被用于模拟此类路径，但由于其顺序性和对精确蒙特卡罗采样的依赖，在高效识别罕见事件方面面临挑战。相比之下，加权集成方法通过在多个副本间分配计算资源来有效采样罕见事件并加速复杂反应路径的探索，每个副本被赋予反映其重要性的权重，且独立于其他副本演化。本文将高效稳健的加权集成方法应用于大型异质种群网络的易感-感染-易感（SIS）动力学建模，探索最终导致疾病清除的随机性与接触异质性之间的相互作用。通过研究多种具有胖尾不对称度分布的网络，我们能够在先前无法触及的参数范围内计算灭绝平均时间及其准平稳分布。
英文摘要：
The presence of erratic or unstable paths in standard kinetic Monte Carlo simulations significantly undermines the accurate simulation and sampling of transition pathways. While typically reliable methods, such as the Gillespie algorithm, are employed to simulate such paths, they encounter challenges in efficiently identifying rare events due to their sequential nature and reliance on exact Monte Carlo sampling. In contrast, the weighted ensemble method effectively samples rare events and accelerates the exploration of complex reaction pathways by distributing computational resources among multiple replicas, where each replica is assigned a weight reflecting its importance, and evolves independently from the others. Here, we implement the highly efficient and robust weighted ensemble method to model susceptible-infected-susceptible (SIS) dynamics on large heterogeneous population networks, and explore the interplay between stochasticity and contact heterogeneity which ultimately gives rise to disease clearance. Studying a wide variety of networks characterized by fat-tailed asymmetric degree distributions, we are able to compute the mean time to extinction and quasi-stationary distribution around it in previously-inaccessible parameter regimes.
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