Package: mudnester 0.7.2

mudnester: Surveillance Data Cleaning and Preparation for Public Health

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 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. Both wide format (one row per person with dose columns, from 'starling::murmuration()') and long format (one row per dose) are accepted. 'corncrake()' returns both a point-corrected count and uncertainty bounds wherever they can be derived, including the inverse relationship between a severity-anchored factor and its reference rate's own bounds. Built for Australian public health surveillance practice but not specific to it -- see individual function documentation for notes on non-Australian use (e.g. Northern Hemisphere season boundaries).

Authors:Nicolas Smoll [aut, cre], Moderna [fnd]