Sequential Monte Carlo (SMC) algorithms represent a suite of robust computational methodologies utilized for state estimation and parameter inference within dynamical systems, particularly in real-time or online environments where data arrives sequentially over time. In this research endeavor, we propose an integrated framework that combines a stochastic epidemic simulator with a sequential importance sampling (SIS) scheme to dynamically infer model parameters, which evolve due to social as well as biological processes throughout the progression of an epidemic outbreak and are also influenced by evolving data measurement bias. Through iterative updates of a set of weighted simulated trajectories based on observed data, this framework enables the estimation of posterior distributions for these parameters, thereby capturing their temporal variability and associated uncertainties. Through simulation studies, we showcase the efficacy of SMC in accurately tracking the evolving dynamics of epidemics while appropriately accounting for uncertainties. Moreover, we delve into practical considerations and challenges inherent in implementing SMC for parameter estimation within dynamic epidemiological settings, areas where the substantial computational capabilities of high-performance computing resources can be usefully brought to bear.

翻译：序贯蒙特卡洛算法是一套鲁棒的计算方法，用于动态系统（尤其在数据随时间序贯到达的实时或在线环境中）的状态估计和参数推断。在本项研究中，我们提出一个集成框架，将随机流行病模拟器与序贯重要性采样方案相结合，以动态推断模型参数——这些参数因流行病暴发过程中的社会及生物学进程而演变，且受不断变化的数据测量偏差影响。通过基于观测数据迭代更新一组带权重的模拟轨迹，该框架能够估计这些参数的后验分布，从而捕捉其时变特性及相关不确定性。通过模拟研究，我们展示了序贯蒙特卡洛算法在准确追踪流行病动态演变的同时合理量化不确定性的效能。此外，我们深入探讨了在动态流行病学场景中应用序贯蒙特卡洛算法进行参数估计所固有的实践考量与挑战——这些领域正是高性能计算资源的强大算力可发挥重要作用的所在。