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.
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