Confirmed, retracted and net counts per event date

Once a tbl_now carries a validation process, "how many cases were there" has three different answers, and which one you want depends on the question:

• get_latest_reported_cases() (the existing function) counts everything that was ever reported, whatever the laboratory later said. It is what a nowcast of the reporting process predicts.

• get_latest_confirmed() counts only the cases that came back confirmed. Pending and retracted cases are not counted.

• get_net_confirmed() counts confirmed minus retracted: the running total as a surveillance system would publish it, which can go down when a case is withdrawn.

That last one is the quantity a count-cumulative stream actually reports, and the one diseasenowcasting's signed-increment (Skellam / SkNB) likelihood is built for – see diseasenowcasting::confirmation_process().

Usage

get_latest_confirmed(x)

get_net_confirmed(x)

get_nth_confirmed(x, delay)

get_initial_confirmed(x)

Arguments

x

A tbl_now with a validation process (see add_validation_date()).

delay

Longest validation delay to count, in the object's validation units. get_nth_confirmed(x, delay = 7) answers "how many cases per event date had been resolved within a week of being reported".

Value

A tibble with the event-date column, the strata columns and a count column named after the object's own case_count (or n for a line list).

get_initial_confirmed() and get_nth_confirmed() answer the same three questions at an earlier point in the process: what was confirmed by the first result to arrive, and what was confirmed within a given delay.

Which date the count is indexed by

By the event date, as every other get_*_cases() function is. A case confirmed three weeks after onset still belongs to the week it began. If you want counts by validation date instead, group on get_validation_date(x) yourself – that is a different question (how busy was the laboratory) and this package does not silently answer it.

See also

get_latest_reported_cases() for the same counts on the reporting process; add_validation_date() to attach a validation; validation_delay for how long resolution takes; plot_validation_status() to see confirmed, retracted and pending over time.

Examples

cases <- data.frame(
  onset = as.Date("2021-01-04") + c(0, 0, 1, 1, 2),
  visit = as.Date("2021-01-05") + c(0, 0, 1, 1, 2),
  result = as.Date("2021-01-06") + c(0, 0, 1, 1, 2),
  outcome = c("confirmed", "retracted", "confirmed", "confirmed", "retracted")
)
flu <- tbl_now(cases,
  event_date = onset, report_date = visit,
  validation_date = result, validation_type = outcome,
  data_type = "linelist", verbose = FALSE
)

# Three answers to "how many cases were there?".
get_latest_reported_cases(flu) # everything reported
#> # A tibble:  3 × 6
#> # Data type: "count-cumulative"
#> # Frequency: Event: `days` | Report: `days`
#>   onset        visit         .event_num .report_num       n .delay
#>   <date>       <date>             <dbl>       <dbl>   <int>  <dbl>
#>   [event_date] [report_date]      [...]       [...] [cases]  [...]
#> 1 2021-01-04   2021-01-05             0           1       2      1
#> 2 2021-01-05   2021-01-06             1           2       2      1
#> 3 2021-01-06   2021-01-07             2           3       1      1
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2021-01-08 | Event date: "onset" | Report date: "visit"
#> # ────────────────────────────────────────────────────────────────────────────────
get_latest_confirmed(flu) # only the positives
#> # A tibble: 3 × 2
#>   onset          n
#>   <date>     <dbl>
#> 1 2021-01-04     1
#> 2 2021-01-05     2
#> 3 2021-01-06     0
get_net_confirmed(flu) # positives minus withdrawals
#> # A tibble: 3 × 2
#>   onset          n
#>   <date>     <dbl>
#> 1 2021-01-04     0
#> 2 2021-01-05     2
#> 3 2021-01-06    -1

# And the same question asked at an earlier point in the process: what was
# confirmed by the first result to come back, and within one day of report.
get_initial_confirmed(flu)
#> # A tibble: 3 × 2
#>   onset          n
#>   <date>     <dbl>
#> 1 2021-01-04     0
#> 2 2021-01-05     0
#> 3 2021-01-06     0
get_nth_confirmed(flu, delay = 1)
#> # A tibble: 3 × 2
#>   onset          n
#>   <date>     <dbl>
#> 1 2021-01-04     1
#> 2 2021-01-05     2
#> 3 2021-01-06     0