Generate Synthetic Data at Tunable Cluster Volume

episodic_synthetic_cases() injects six outbreaks in total - enough to show every detector working, and few enough that a demo dashboard reads like a real morning's work. That is the wrong shape for tuning a configuration against (e.g. deciding how many dossiers your board can realistically review per month). This function fills that gap: on top of the same baseline and the same six outbreaks, it adds many independent case clusters for one chosen pathogen, at a rate you control, so you can see how detection volume responds as you adjust n_bumps_per_month or your own configuration. Every case it adds carries a PT-VOL-* patient key so it is always identifiable as synthetic tuning data, never mistaken for anything else.

Usage

episodic_synthetic_cases_calibration(
  start_date = end_date - 5 * 365,
  end_date = Sys.Date(),
  pathogen = "Clostridioides difficile",
  n_bumps_per_month = 3,
  seed = 1
)

Arguments

start_date, end_date

The window to generate over; defaults as in episodic_synthetic_cases().

pathogen

Which pathogen to generate the extra clusters for. Defaults to Clostridioides difficile, a plausible example of an endemic pathogen that produces frequent clusters at a busy institution.

n_bumps_per_month

Average number of independent case clusters generated per calendar month. Raise or lower this to see how detection volume responds.

seed

RNG seed, for reproducible runs.

Value

A data frame satisfying episodic_check_cases(), including everything episodic_synthetic_cases() produces (background baseline, the six demo outbreaks) plus the extra volume.

Examples

cases <- episodic_synthetic_cases_calibration(
  start_date = as.Date("2025-01-01"), end_date = as.Date("2025-06-30"),
  n_bumps_per_month = 4
)
sum(startsWith(cases$patient_key, "PT-VOL-"))
#> [1] 100