Clean, prepare, and aggregate surveillance data for public health analysis.
Provides structural data cleaning and standardisation (clean_the_nest()),
age categorisation against ~50 published schemes with publication-ready
labelling (preening()), time-unit aggregation with zero-filling and
seasonal awareness (roost()), joint aggregation of several linked event
dates (e.g. onset, admission, ICU, complication, fatality) into one table of
comparable rate columns (flyway()), under-ascertainment correction via a
stratified, time-varying multiplier factor supplied directly, derived by the
ratio (multiplier) method, or derived by inverting an externally sourced
severity rate (e.g. an infection-fatality-rate anchor) against an observed
severity ratio (corncrake()), comorbidity detection from ICD-10-AM
clinical coding (plumage()), vaccine coverage data construction
(brood()), hash-based de-identification (molting()), and relinking
of previously de-identified data (homing()). brood() produces a
brood_df object supporting two population models: pre-aggregated
denominators (population_model = "pre_aggregated") and record-level cohort
designs (population_model = "cohort"). The cohort model handles single
time-point coverage snapshots, interrupted time series analysis via a
built-in sweep returning monthly coverage rates (time_series = TRUE), and
birth cohort designs with person-time computation. This cohort/time-series
coverage model was applied in Roughan et al. (2026)