npfseir - Nested Particle Filter for Stochastic SEIR Epidemic Models
Implements the online Bayesian inference framework for
joint state and parameter estimation in a stochastic
Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model
with a time-varying transmission rate. The log-transmission
rate is modelled as a latent Ornstein-Uhlenbeck (OU) process
with exact Gaussian discrete-time transitions. Inference is
performed via the nested particle filter (NPF) of Crisan and
Miguez (2018) <doi:10.3150/17-BEJ954>, which maintains an outer
particle layer over the OU hyperparameters and, for each outer
particle, an inner bootstrap filter over epidemic states. The
Cori-style renewal-equation estimator follows Cori et al.
(2013) <doi:10.1093/aje/kwt133>. The package also provides
utilities for simulation, posterior summarisation, and
forecasting.
