9. Configuration file parameters for the VIMC core project and the DRC case study - HopkinsIDD/gavi_vimc_cholera GitHub Wiki

A typical config file for the VIMC core project would look like the following (taking the testing run for COD as an example):

runname: '202310gavi-4'
country: 'COD'
scenario: 'ocv1-ocv2-default'
clean: TRUE
incid:
  num_samples: 100
  redraw: FALSE
  use_country_incid_trend: FALSE
vacc:
  targeting_strategy: affected_pop
  num_skip_years: 3
  ndoses: two
setting:
  incidence_rate_trend: FALSE
  outbreak_multiplier: FALSE
  random_seed: 103

The following are the definitions for all the parameters:

runname: the unique name (touchstone) that's specific to each version of the Montagu server from which we download the demographic data
country: the 3-digit ISO code for a certain country
scenario: either 'ocv1-default', 'ocv1-ocv2-default' or 'no-vaccination' depending on which scenario to simulate
clean: whether to delete the pre-existing final model output when new files with the same names are being generated during a new simulation
incid:
- - num_samples: how many stochastic runs to simulate/how many layers of raster files to carry around and get calculated each time
- - redraw: whether to re-generate and renew the initial multi-layer (#num_samples) incidence rate rasters
- - use_country_incid_trend: whether to use the country-level incidence rate trend for this specific country (the config option will not be used if incidence_rate_trend is FALSE)
vacc:
- - targeting_strategy: it can be 'threshold_unconstrained', 'affected_pop', or 'incidence' depending on the type of the model; for the surveillance project, however, it will be 'affected_pop', or 'incidence', each representing ranking the admin units by the size of cholera infection affected population or by the cholera incidence rate
- - num_skip_years: it's usually 3, meaning how many years the same administration unit should wait until its next vaccination campaign
- - ndoses: the number of vaccine doses, "zero" corresponds to the cholera-no vaccination scenario on montagu, "one" corresponds to the cholera-ocv1-default vaccination scenario on montagu, and "two" corresponds to the cholera-ocv1-ocv2-default vaccination scenario on montagu
setting:
- - incidence_rate_trend: whether to use the temporal incidence rate trend
- - outbreak_multiplier: whether to use the spatial-temporal outbreak multiplier
- - random_seed: the random seed, it is set to 103 for all scenarios and settings (can be changed by modifying set_all_parameters.R)

A typical config file for the DRC Case Study would look like the following:

runname: '202310gavi-4'
country: 'COD'
scenario: 'ocv1-ocv2-default'
clean: TRUE
campaign_cov: 0.965
incid:
  num_samples: 100
  redraw: FALSE
  use_country_incid_trend: FALSE
vacc:
  targeting_strategy: affected_pop
  num_skip_years: 5
  ndoses: two
custom:
  use_montagu_coverage: FALSE
  output_years: c(2020, 2036)
  targeting_filename: input_data/drc_custom_targeting_2024_2026.rds
  coverage_filename: input_data/drc_custom_coverage_2024_2026.csv
  use_custom_shapefile: TRUE
  shapefile_filename: input_data/shapefiles/DRC_custom_shapefile/custom_shapefile.rds
  country_shapefile_filename: input_data/shapefiles/DRC_custom_shapefile/country_shapefile.rds
setting:
  incidence_rate_trend: FALSE
  outbreak_multiplier: FALSE
  random_seed: 103

The following are the definitions for all the parameters:

runname: the unique name (touchstone) that's specific to each version of the Montagu server from which we download the demographic data
country: the 3-digit ISO code for a certain country
scenario: either 'ocv1-ocv2-default' or 'no-vaccination' depending on which scenario to simulate
clean: whether to delete the pre-existing final model output when new files with the same names are being generated during a new simulation
campaign_cov: the proportion of the population in each admin unit that gets vaccinated
incid:
- - num_samples: how many stochastic runs to simulate/how many layers of raster files to carry around and get calculated each time
- - redraw: whether to re-generate and renew the initial multi-layer (#num_samples) incidence rate rasters
- - use_country_incid_trend: whether to use the country-level incidence rate trend for this specific country (the config option will not be used if incidence_rate_trend is FALSE)
vacc:
- - targeting_strategy: it can be 'custom', 'affected_pop', or 'random' where 'custom' represents a scenario where we feed into the model a custom targeting table (so we don't use the targeting steps in the pipeline), affected_pop' represents ranking the admin units by the size of cholera infection affected population, and 'random' represents ranking admin units randomly
- - num_skip_years: it's usually 5, meaning how many years the same administration unit should wait until its next vaccination campaign
- - ndoses: this is set to "two" to represent a two-dose vaccination campaign
custom:
- - use_montagu_coverage: whether to use montagu coverage or a custom coverage table
- - output_years: which years to run the model for, requires a TL and a TR
- - targeting_filename: the file name for the custom targeting table, only used when targeting_strategy is 'custom'
- - coverage_filename: the file name for the custom coverage table, only used when targeting_strategy is 'affected_pop' or 'random'
- - use_custom_shapefile: whether to use custom shapefiles instead of the automatically downloaded GADM shapefiles
- - shapefile_filename: the file name for the custom shapefile with sub-national admin units (health zones for DRC)
- - country_shapefile_filename: the file name for the custom country-level shapefile
setting:
- - incidence_rate_trend: whether to use the temporal incidence rate trend
- - outbreak_multiplier: whether to use the spatial-temporal outbreak multiplier
- - random_seed: the random seed, it is set to 103 for all scenarios and settings (can be changed by modifying set_all_parameters.R)