epinowcast: Nowcast using partially observed data
In seabbs/epinowcast: Flexible Hierarchical Nowcasting

epinowcast

R Documentation

Nowcast using partially observed data

Description

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.

Usage

epinowcast(
  data,
  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 | day:.group), 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(),
  priors,
  ...
)

Arguments

`data`	Output from `enw_preprocess_data()`.
`reference`	The reference date indexed reporting process model specification as defined using `enw_reference()`.
`report`	The report date indexed reporting process model specification as defined using `enw_report()`.
`expectation`	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)`)).
`missing`	The missing reference date model specification as defined using `enw_missing()`. By default this is set to not be used.
`obs`	The observation model as defined by `enw_obs()`. Observations are also processed within this function for use in modelling.
`fit`	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.
`model`	The model to use within `fit`. By default this uses `enw_model()`.
`priors`	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.

Value

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().

Examples


# Load data.table and ggplot2
library(data.table)
library(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(
  nat_germany_hosp,
  latest_date = "2021-10-01"
)
# Make sure observations are complete
nat_germany_hosp <- enw_complete_dates(
  nat_germany_hosp,
  by = c("location", "age_group")
)
# Make a retrospective dataset
retro_nat_germany <- enw_filter_report_dates(
  nat_germany_hosp,
  remove_days = 40
)
retro_nat_germany <- enw_filter_reference_dates(
  retro_nat_germany,
  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(
  latest_obs,
  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
  )
)
nowcast
# 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")

seabbs/epinowcast documentation built on Feb. 3, 2025, 9:56 p.m.