Apply a ascertainment bias to the observed case counts.

Usage

sim_apply_ascertainment(df = i_sim_count_data, fn_asc = ~1, seed = Sys.time())

Arguments

df

a count dataframe from e.g. sim_poisson_model() or sim_summarise_linelist() - a dataframe with columns:

statistic (character) - An identifier for the statistic, whether that be infections, admissions, deaths
count (positive_integer) - Positive case counts associated with the specified time frame
time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`

Minimally grouped by: statistic (and other groupings allowed).

fn_asc

a function that takes a single input vector t and returns a probability of ascertainment, e.g. ~ stats::rbeta(.x, 20, 80) or ~ rbeta2(.x,prob=<probability>,kappa=<dispersion>). or cfg_weekly_proportion_rng()

seed

a RNG seed

Value

a dataframe with original column, and count column modified to include ascertainment bias.

Examples

with_defaults("2025-01-01" ,"1 day", {
  dplyr::tibble(
    statistic = "incidence",
    time=as.time_period(1:10,"1 day"),
    count=rep(100,10)
  ) %>%
  dplyr::group_by(statistic) %>%
  sim_apply_ascertainment(~ ifelse(.x<=5,0.1,0.9))
})