accelerate_yellow_fever: Accelerate yellow_fever

In gpw13/billionaiRe: Calculate the WHO Triple Billions

Accelerate yellow_fever

Description

accelerate_yellow_fever_campaign() accelerate yellow fever campaign by adding planned yellow fever campaigns to the provided values in df. When a value is reported for a year and country, then this value is kept, even after 2018. Some planned values are provided only for the denominator. For some planned campaigns only the denominator is provided. When this is the case, the numerator is calculated by taking the best historical vaccination coverage achieved, or if not available by taking the best historical coverage across all countries in 2018.

Usage

accelerate_yellow_fever_campaign(
  df,
  ind_ids = billion_ind_codes("hep"),
  scenario_col = "scenario",
  value_col = "value",
  start_year = 2018,
  end_year = 2025,
  years_best_performance = 2015:2018,
  default_scenario = "default",
  scenario_name = "acceleration",
  ...
)

accelerate_yellow_fever_routine(df, scenario_name = "acceleration", ...)

Arguments

df	Data frame in long format, where 1 row corresponds to a specific country, year, and indicator.
ind_ids	Named vector of indicator codes for input indicators to the Billion. Although separate indicator codes can be used than the standard, they must be supplied as a named vector where the names correspond to the output of billion_ind_codes().
scenario_col	Column name of column with scenario identifiers. Useful for calculating contributions on data in long format rather than wide format.
value_col	Column name of column with indicator values.
start_year	Base year for contribution calculation, defaults to 2018.
end_year	End year(s) for contribution calculation, defaults to 2019 to 2025.
years_best_performance	vector of years with the years in which the best performance should be found.
default_scenario	name of the default scenario.
scenario_name	name of scenario
...	additional parameters to to pass to recycle_data

Details

Planned campaigns are the planned campaigns targets provided by WHO technical programs based on member states planification.

accelerate_yellow_fever_routine() accelerate routine by aiming at a +20% percent change between 2015 and 2025 AROC.

Runs:

• scenario_aroc(df, aroc_type = "percent_change", percent_change = 20, baseline_year = 2015, target_year = 2025, small_is_best = FALSE)

Value

data frame with acceleration scenario binded to df. scenario_col is set to acceleration

See Also

HEP acceleration scenarios accelerate_cholera_campaign(), accelerate_detect(), accelerate_espar(), accelerate_measles_routine(), accelerate_meningitis_campaign(), accelerate_polio_routine()

gpw13/billionaiRe documentation built on Sept. 27, 2024, 10:05 p.m.