% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{predict.npf_seir}
\alias{predict.npf_seir}
\title{Posterior predictive forecasts from an npf_seir object}
\usage{
\method{predict}{npf_seir}(object, horizon = 14L, n_draws = 1000L, ...)
}
\arguments{
\item{object}{An \code{npf_seir} object.}

\item{horizon}{Integer. Number of days ahead to forecast. Default 14.}

\item{n_draws}{Integer. Number of Monte Carlo draws. Default 1000.}

\item{...}{Ignored.}
}
\value{
A named list with components: \code{mean} (numeric vector of
length \code{horizon}, posterior predictive mean), \code{lo} and
\code{hi} (95\\% predictive interval bounds), and \code{draws}
(\code{n_draws}-by-\code{horizon} matrix of Monte Carlo draws).
}
\description{
Generates forward simulations from the filter's final filtered particles to produce
a posterior predictive distribution over future case counts.
}
\examples{
\donttest{
fixed <- list(eps = 1/5, gamma = 1/10, delta = 1/(70*365),
              b = 1/(70*365), q = 0.3, N = 1e6, sigma_comp = 0.01)
x0    <- c(1e6 - 100, 50, 50, 0, log(0.25))
set.seed(1)
traj  <- seir_simulate(200, 0.05, 0.10, log(0.25), x0, fixed)
fit   <- npf_seir(traj$obs[-1], fixed, K=50, M=100, seed=42)
fc    <- predict(fit, horizon = 14)
print(fc$mean)
}
}
