Accelerate anc4 by first dividing countries into those with reported data and those without.
For countries without reported data, the acceleration scenario_col is the same as business as usual.
For countries with reported data, scenarios with both a fixed target of 95% by 2030 and a applying the AROC of the top 10 performing countries with at least 4 reported/estimated values are tried, with the easiest to achieve of the two selected. The selected scenario is then compared against the business as usual scenario for reported data, and the best of the two chosen as the acceleration scenario.
Usage
accelerate_anc4(
df,
ind_ids = billion_ind_codes("uhc"),
scenario_col = "scenario",
default_scenario = "default",
bau_scenario = "historical",
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.
- 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
UHC acceleration scenarios
accelerate_art(),
accelerate_beds(),
accelerate_bp(),
accelerate_doctors(),
accelerate_dtp3(),
accelerate_fh(),
accelerate_fpg(),
accelerate_fp(),
accelerate_hwf(),
accelerate_itn(),
accelerate_nurses(),
accelerate_pneumo(),
accelerate_tb(),
accelerate_uhc_sanitation(),
accelerate_uhc_tobacco()