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（SMC）算法是一套用于动态系统状态估计与参数推断的鲁棒计算方法，尤其适用于数据随时间顺序到达的实时或在线环境。本研究提出一个集成框架，将随机传染病模拟器与序贯重要性采样（SIS）方案相结合，以动态推断模型参数。这些参数在传染病暴发过程中因社会及生物学进程而演变，同时受到不断变化的数据测量偏差影响。通过基于观测数据对一组加权模拟轨迹进行迭代更新，该框架能够估计这些参数的后验分布，从而捕捉其时间变异性及相关不确定性。通过模拟研究，我们展示了SMC在准确追踪传染病动态演变的同时合理量化不确定性的有效性。此外，本文深入探讨了在动态流行病学环境中应用SMC进行参数估计所面临的实践考量与挑战，而高性能计算资源的强大计算能力可在这些领域发挥重要作用。