Fit a nowcast model to censored reporting data

The main entry point. Takes a tbl_now (from the tbl.now package) and a model(), fits the latent-epidemic + reporting-delay model as of a given date, and returns a nowcast_class object. Fitting only – the posterior-predictive nowcast is produced lazily by predict(); the latent incidence by mean()/median()/quantile(); the parameter estimates by coef().

Usage

nowcast(
  data,
  model = diseasenowcasting::model(),
  type = c("two_stage", "one_stage", "auto"),
  now = NULL,
  K = 25L,
  n_draws = 2000L,
  delay_window = 120L,
  np_spread = 1,
  floor_mu = 0.08,
  floor_sig_frac = 0.08,
  temporal_effects = "auto",
  prior_only = FALSE,
  seed = sample.int(.Machine$integer.max, 1),
  ...
)

Arguments

data

A tbl_now object (tbl.now::tbl_now()).

model

A model() object. Default: model() (NB + HSGP + Dirichlet).

type

"two_stage" (default; delay-imputation pooling), "one_stage" (a single joint fit), or "auto" (per delay: dirichlet one-stage, all other delays two-stage – the better choice for each in our experiments).

now

As-of date; only events/reports up to now are used. Default: tbl.now::get_now(data), falling back to the latest report date.

K

Number of delay imputations for the two-stage path.

n_draws

Default number of posterior draws used by predict() and the latent-incidence summaries.

delay_window

Recent window length for the parametric Stage-1 delay fit.

np_spread

Dirichlet simplex imputation covariance inflation (default 1).

floor_mu, floor_sig_frac

Imputation-spread floors (parametric families).

temporal_effects

Controls automatic seasonal / day-of-week covariates. "auto" (default) adds sensible effects based on the data's time unit (weekly -> 52-period seasonality; daily -> day-of-week + 52-period seasonality; monthly -> 12-period seasonality) only if the tbl_now does not already carry computed temporal effects. Use "none" (or "None") to disable, or pre-attach your own effects to the tbl_now with tbl.now::add_temporal_effects() + tbl.now::compute_temporal_effects().

prior_only

If TRUE, ignore the likelihood and draw the epidemic parameters from their priors only, returning the prior-predictive latent incidence. Useful for understanding what a prior implies before seeing data (e.g. how the SIR R0 prior or the AR(1) phi prior reshapes the epidemic). The result is a normal nowcast_class, so predict() / autoplot() / median() / quantile() all work; data only supplies the time grid. Default FALSE.

seed

Optional RNG seed (imputation draws).

...

Passed to prepare_data() (e.g. gp_boundary_frac).

Value

A nowcast_class object.

Overdispersion (phi)

The negative-binomial overdispersion prior is not an argument of nowcast(). Set it on the likelihood instead, e.g. model(nb_likelihood(phi = lognormal_prior(log(5), 0.5)), ...). The default nb_likelihood() already uses lognormal_prior(log(20), 0.5).

Examples

if (requireNamespace("tbl.now", quietly = TRUE)) {
  # data <- tbl.now::tbl_now(my_linelist, event_date = onset, report_date = reported)
  # nc <- nowcast(data, model(nb_likelihood(), hsgp_epidemic(), lognormal_delay()))
  # predict(nc); median(nc); coef(nc)
}
#> NULL