6. Run the model - HopkinsIDD/gavi_vimc_cholera GitHub Wiki

This instruction applies to the VIMC Core and Surveillance projects

1. First, we modify scripts/set_all_parameters.R to export different parameters that are essential to different projects. See the configuration file parameters section for the definitions of these parameters.

2. Next, we directly run scripts/write_configs.R to generate all of the model configuration files.

3. Next, we run Rscript scripts/run_country_incid_crop.R -c configs/<run-name>/<scenario-name>/.../<config-name> to save country-level cropped cholera incidence rasters to the input_data/incidence directory. If you have previously run models with this setup, it is possible you may already have generated all of the necessary files. It will do no harm to rerun this script anyways to be sure. This step may be parallelized for multiple configuration files, and you can find the Shell script at sbatch_scripts/ to submit multiple countries at the same time. It's worth noting that for different VIMC core projects, different touchstones will be used, you may wanna refer to VIMC Montagu API to make sure first. What's more, two scenario names campaign-default and no-vaccination work for both projects.

4. Next, we run Rscript scripts/run_model.R -c configs/<run-name>/<scenario-name>/.../<config-name> to produce primary model outputs. This step may also be parallelized for multiple configuration files using the Shell script that can be found at sbatch_scripts/. This would be the final step for the simulation processes of the surveillance project.

5. Finally, if this if for the VIMC core project, we need to process all of the model outputs and generate the stochastic parameter and burden template files by running scripts/write_final_outputs.R.

This set of instructions applies to the DRC Case Study, which uses the pipeline for the VIMC Core model

1. If the following files do not exist:

• input_data/drc_custom_coverage_2024_2026.csv, the custom coverage table for the DRC case study
• input_data/drc_custom_targeting_2024_2026.rds, the custom targeting table for the DRC case study
• input_data/shapefiles/DRC_custom_shapefile/custom_shapefile.rds, the health zone shapefile for DRC
• input_data/shapefiles/DRC_custom_shapefile/country_shapefile.rds, a country-level shapefile for DRC

then run the following scripts:

• scripts/create_custom_targeting_table.R to create the custom targeting table
• scripts/custom_targeting_to_coverage.R to create the custom coverage table
• input_data/shapefiles/DRC_custom_shapefile/process_custom_shapefile.R to generate the shapefiles required

2. Then modify scripts/set_all_parameters.R to export different parameters that are essential to different projects. See the configuration file parameters section for the definitions of these parameters.

3. Next, directly run scripts/write_configs.R to generate all of the model configuration files.

4. Next, run Rscript scripts/run_country_incid_crop.R -c configs/<run-name>/<scenario-name>/.../<config-name> to save country-level cropped cholera incidence rasters to the input_data/incidence directory.

5. Finally, run Rscript scripts/run_model.R -c configs/<run-name>/<scenario-name>/.../<config-name> to produce the required model outputs.