  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.
