Report case counts by date of report

report_cases(
  nowcast,
  case_forecast = NULL,
  delay_defs,
  incubation_defs,
  type = "median",
  reporting_effect
)

Arguments

nowcast	A dataframe as produced by `nowcast_pipeline`
case_forecast	A data.table of case forecasts as produced by `epi_measures_pipeline` If not supplied the default is not incoperate forecasts.
delay_defs	A data.table that defines the delay distributions (model, parameters and maximum delay for each model). See `get_delay_dist` for an example of the structure.
incubation_defs	A data.table that defines the incubation distributions (model, parameters and maximum delay for each model). See `get_delay_dist` for an example of the structure.
type	Character string indicating the method to use to transfrom counts. Supports either "sample" which approximates sampling or "median" would shift by the median of the distribution.
reporting_effect	A `data.table` giving the weekly reporting effect with the following variables: `sample` (must be the same as in `nowcast`), `effect` (numeric scaling factor for each weekday), `day` (numeric 1 - 7 (1 = Monday and 7 = Sunday)). If not supplied then no weekly reporting effect is assumed.

Value

A data.table containing the following variables sample, date and cases

Examples


## Define example cases
cases <- data.table::as.data.table(EpiSoon::example_obs_cases) 

cases <- cases[, `:=`(confirm = as.integer(cases), import_status = "local")]

## Define a single report delay distribution
delay_defs <- EpiNow::lognorm_dist_def(mean = 5, 
                                       mean_sd = 1,
                                       sd = 3,
                                       sd_sd = 1,
                                       max_value = 30,
                                       samples = 2)
                                       
## Define a single incubation period
incubation_defs <- EpiNow::lognorm_dist_def(mean = EpiNow::covid_incubation_period[1, ]$mean,
                                            mean_sd = EpiNow::covid_incubation_period[1, ]$mean_sd,
                                            sd = EpiNow::covid_incubation_period[1, ]$sd,
                                            sd_sd = EpiNow::covid_incubation_period[1, ]$sd_sd,
                                            max_value = 30, samples = 2)
                                           

## Perform a nowcast
nowcast <- nowcast_pipeline(reported_cases = cases, 
                            target_date = max(cases$date),
                            delay_defs = delay_defs,
                            incubation_defs = incubation_defs)
                            
                     
reported_cases <- report_cases(nowcast, delay_defs = delay_defs,
                               incubation_defs = incubation_defs)
                               
print(reported_cases)
#>      sample sample       date cases
#>   1:      1      1 2020-01-10     1
#>   2:      1      1 2020-01-11     0
#>   3:      1      1 2020-01-12     0
#>   4:      1      1 2020-01-13     0
#>   5:      1      1 2020-01-14     0
#>  ---                               
#> 150:      2      2 2020-03-19   320
#> 151:      2      2 2020-03-20   312
#> 152:      2      2 2020-03-21   328
#> 153:      2      2 2020-03-22   350
#> 154:      2      2 2020-03-23   404