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Provides a user friendly interface around package functionality to produce a nowcast from observed preprocessed data, and a series of user defined models. By default a model that assumes a fixed parametric reporting distribution with a flexible expectation model is used. Explore the individual model components for additional documentation and see the package case studies for example model specifications for different tasks.


  reference = epinowcast::enw_reference(parametric = ~1, distribution = "lognormal",
    non_parametric = ~0, data = data),
  report = epinowcast::enw_report(non_parametric = ~0, structural = ~0, data = data),
  expectation = epinowcast::enw_expectation(r = ~0 + (1 |, generation_time =
    1, observation = ~1, latent_reporting_delay = 1, data = data),
  missing = epinowcast::enw_missing(formula = ~0, data = data),
  obs = epinowcast::enw_obs(family = "negbin", data = data),
  fit = epinowcast::enw_fit_opts(sampler = epinowcast::enw_sample, nowcast = TRUE, pp =
    FALSE, likelihood = TRUE, debug = FALSE, output_loglik = FALSE),
  model = epinowcast::enw_model(),



Output from enw_preprocess_data().


The reference date indexed reporting process model specification as defined using enw_reference().


The report date indexed reporting process model specification as defined using enw_report().


The expectation model specification as defined using enw_expectation(). By default this is set to be a highly flexible random effect by reference date for each group and thus weakly informed. Depending on your context (and in particular the density of data reporting) other choices that enforce more assumptions may be more appropriate (for example a weekly random walk (specified using rw(week, by = .group))).


The missing reference date model specification as defined using enw_missing(). By default this is set to not be used.


The observation model as defined by enw_obs(). Observations are also processed within this function for use in modelling.


Model fit options as defined using enw_fit_opts(). This includes the sampler function to use (with the package default being enw_sample()), whether or now a nowcast should be used, etc. See enw_fit_opts() for further details.


The model to use within fit. By default this uses enw_model().


A data.frame with the following variables: variable, mean, sd describing normal priors. Priors in the appropriate format are returned by enw_reference() as well as by other similar model specification functions. Priors in this data.frame replace the default priors specified by each model component.


Additional model modules to pass to model. User modules may be used but currently require the supplied model to be adapted.


A object of the class "epinowcast" which inherits from enw_preprocess_data() and data.table, and combines the input data, priors, and output from the sampler specified in enw_fit_opts().

See also

Other epinowcast: plot.epinowcast(), summary.epinowcast()


if (FALSE) { # interactive()
# Load data.table and ggplot2

# Use 2 cores
options(mc.cores = 2)
# Load and filter germany hospitalisations
nat_germany_hosp <-
  germany_covid19_hosp[location == "DE"][age_group == "00+"]
nat_germany_hosp <- enw_filter_report_dates(
  latest_date = "2021-10-01"
# Make sure observations are complete
nat_germany_hosp <- enw_complete_dates(
  by = c("location", "age_group")
# Make a retrospective dataset
retro_nat_germany <- enw_filter_report_dates(
  remove_days = 40
retro_nat_germany <- enw_filter_reference_dates(
  include_days = 40
# Get latest observations for the same time period
latest_obs <- enw_latest_data(nat_germany_hosp)
latest_obs <- enw_filter_reference_dates(
  remove_days = 40, include_days = 20
# Preprocess observations (note this maximum delay is likely too short)
pobs <- enw_preprocess_data(retro_nat_germany, max_delay = 20)
# Fit the default nowcast model and produce a nowcast
# Note that we have reduced samples for this example to reduce runtimes
nowcast <- epinowcast(pobs,
  fit = enw_fit_opts(
    save_warmup = FALSE, pp = TRUE,
    chains = 2, iter_warmup = 500, iter_sampling = 500
# plot the nowcast vs latest available observations
plot(nowcast, latest_obs = latest_obs)

# plot posterior predictions for the delay distribution by date
plot(nowcast, type = "posterior") +
  facet_wrap(vars(reference_date), scale = "free")