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This function preprocesses raw observations under the assumption they are reported as cumulative counts by a reference and report date and is used to assign groups. It also constructs data objects used by visualisation and modelling functions including the observed empirical probability of a report on a given day, the cumulative probability of report, the latest available observations, incidence of observations, and metadata about the date of reference and report (used to construct models). This function wraps other preprocessing functions that may be instead used individually if required. Note that internally reports beyond the user specified delay are dropped for modelling purposes with the cum_prop_reported and max_confirm variables allowing the user to check the impact this may have (if cum_prop_reported is significantly below 1 a longer max_delay may be appropriate). Also note that if missing reference or report dates are suspected to occur in your data then these need to be completed with enw_complete_dates().


  by = NULL,
  timestep = "day",
  set_negatives_to_zero = TRUE,
  copy = TRUE



A data.frame containing at least the following variables: reference_date (index date of interest), report_date (report date for observations), confirm (cumulative observations by reference and report date).


A character vector describing the stratification of observations. This defaults to no grouping. This should be used when modelling multiple time series in order to identify them for downstream modelling


The maximum number of days to model in the delay distribution. If not specified the maximum observed delay is assumed to be the true maximum delay in the model. Otherwise, an integer greater than or equal to 1 can be specified. Observations with delays larger then the maximum delay will be dropped. If the specified maximum delay is too short, nowcasts can be biased as important parts of the true delay distribution are cut off. At the same time, computational cost scales non-linearly with this setting, so you want the maximum delay to be as long as necessary, but not much longer.

Steps to take to determine the maximum delay:

  • Consider what is realistic and relevant for your application.

  • Check the proportion of observations reported (prop_reported) by delay in the new_confirm output of enw_preprocess_obs.

  • Use check_max_delay() to check the coverage of a candidate max_delay.

  • If in doubt, check if increasing the maximum delay noticeably changes the delay distribution or nowcasts as estimated by epinowcast. If it does, your maximum delay may still be too short.

Note that delays are zero indexed and so include the reference date and max_delay - 1 other days (i.e. a max_delay of 1 corresponds to no delay).


The timestep to used in the process model (i.e. the reference date model). This can be a string ("day", "week", "month") or a numeric whole number representing the number of days. If your data does not have this timestep then you may wish to make use of enw_aggregate_cumulative() to aggregate your data to the desired timestep.


Logical, defaults to TRUE. Should negative counts (for calculated incidence of observations) be set to zero? Currently downstream modelling does not support negative counts and so setting must be TRUE if intending to use epinowcast().


Other arguments to enw_add_metaobs_features(), e.g. holidays, which sets commonly used metadata (e.g. day of week, days since start of time series)


A logical; if TRUE (the default) creates a copy; otherwise, modifies obs in place.


A data.table containing processed observations as a series of nested data.frames as well as variables containing metadata. These are:

  • obs: (observations with the addition of empirical reporting proportions and restricted to the specified maximum delay).

  • new_confirm: Incidence of notifications by reference and report date. Empirical reporting distributions are also added.

  • latest: The latest available observations.

  • missing_reference: Observations missing reference dates.

  • reporting_triangle: Incident observations by report and reference date in the standard reporting triangle matrix format.

  • metareference: Metadata reference dates derived from observations.

  • metrareport: Metadata for report dates.

  • metadelay: Metadata for reporting delays produced using enw_metadata_delay().

  • max_delay: Maximum delay to be modelled by epinowcast.

  • time: Numeric, number of timepoints in the data.

  • snapshots: Numeric, number of available data snapshots to use for nowcasting.

  • groups: Numeric, Number of groups/strata in the supplied observations (set using by).

  • max_date: The maximum available report date.


If max_delay is numeric, it will be internally coerced to integer using as.integer()).



# Filter example hospitalisation data to be national and over all ages
nat_germany_hosp <- germany_covid19_hosp[location == "DE"]
nat_germany_hosp <- nat_germany_hosp[age_group == "00+"]

# Preprocess with default settings
pobs <- enw_preprocess_data(nat_germany_hosp)
#> Using the maximum observed delay of 82 days. You may want to specify a shorter
#> (or, in special cases, longer) maximum delay via the `max_delay` argument. See
#> help(enw_preprocess_data) (`?epinowcast::enw_preprocess_data()`) for details.
#>                      obs            new_confirm               latest
#>                   <list>                 <list>               <list>
#> 1: <data.table[12915x9]> <data.table[12915x11]> <data.table[198x10]>
#>    missing_reference   reporting_triangle       metareference
#>               <list>               <list>              <list>
#> 1: <data.table[0x6]> <data.table[198x84]> <data.table[198x9]>
#>              metareport          metadelay max_delay  time snapshots     by
#>                  <list>             <list>     <num> <int>     <int> <list>
#> 1: <data.table[279x12]> <data.table[82x5]>        82   198       198       
#>    groups   max_date timestep
#>     <int>     <IDat>   <char>
#> 1:      1 2021-10-20      day