Accelerate beds by first dividing countries into two groups:
For countries with 18 or more beds for all years after 2018, business as usual is returned.
For countries which have less than 18 beds for any of the years after 2018 (inclusive), 18 beds is targeted by 2025
Usage
accelerate_target_beds(
df,
ind_ids = billion_ind_codes("uhc"),
scenario_col = "scenario",
value_col = "value",
start_year = 2018,
default_scenario = "default",
bau_scenario = "historical",
scenario_name = "acceleration_target",
...
)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
Name of the column containing indicator value in
df.- start_year
Year from which the acceleration scenario begins, inclusive.
- default_scenario
name of the default scenario.
- bau_scenario
name of scenario to be used for business as usual. Default is
historical.- scenario_name
name of scenario
- ...
additional parameters to be passed to scenario function
See also
Target scenario
accelerate_target_anc4(),
accelerate_target_art(),
accelerate_target_bp(),
accelerate_target_doctors(),
accelerate_target_dtp3(),
accelerate_target_fh(),
accelerate_target_fpg(),
accelerate_target_fp(),
accelerate_target_hwf(),
accelerate_target_itn(),
accelerate_target_nurses(),
accelerate_target_pneumo(),
accelerate_target_tb(),
accelerate_target_uhc_sanitation(),
accelerate_target_uhc_tobacco()