stisim_vddx_zim

Analyses of diagnostics for discharging STIs in Zimbabwe

Requires Starsim v2 and STIsim v1.

Installation

1. Create a new virtual environment: conda create -n stisim python=3.11 -y; conda activate stisim
2. Install requirements: pip install -r requirements.txt
3. To check that it works, you can run python model.py

Analysis overview

Zimbabwe’s national guidelines for STI management recommend that high-risk women presenting with vaginal discharge syndrome (VDS) are prescribed antibiotics for gonorrhea (NG), chlamydia (CT), trichomoniasis (TV), and bacterial vaginosis (BV). The performance of this approach depends on rates of care-seeking and implementation of treatment guidelines. This repository contains scripts to analyze the efficiency and impact of syndromic management on STI transmission, and the potential impact of an NG/CT/TV point-of-care (POC) test for women with VDS.

Repository structure

• data contains all the input data used for the model, as well as the zimbabwe_sti_data.csv file that contains the data we calibrate to.
• results contains result files - these are generated by running the four run_*.py scripts outlined below, and the are consumed by running the plot_*.py scripts.

Model files

• hiv_model.py and model.py contain an HIV model and a co-transmitting STI-HIV model, respectively. These can be run directly, but more often they will be called by a run_*.py script in the process of some analysis.
• analyzers.py contain some analyzers used within the analyses
• interventions.py contains interventions, specifically syndromic management and a hypothetical point-of-care (POC) diagnostic
• utils.py contains utilities
• process_ihme_data.py is needed to process a large result file (not in the repo) and output the data file data/zimbabwe_sti_data.csv used for calibration

Files to run to produce results

1. Run run_hiv_calibration.py to calibrate the HIV model.
2. Run run_calibration.py to calibrate the STI model. This produces the following files:
   • results/zim_sti_calib_stats_treat*.df
   • results/zim_sti_par_stats_treat*.df Once re-generated, these can be directly updated in the repo via git. The calibration also produces a much larger file, which should not be added to the repo but which can be inspected if required.
3. Run run_plot_data.py to process the outputs of the calibration and turn them into the format used for creating plots of the calibrated model. This produces the following files:
   • results/epi_df_treat*.df
   • results/hiv_df_treat*.df
   • results/sw_df_treat*.df
4. Run run_syndromic_scens.py to run the scenarios that assess the impact of a POC diagnostic. This produces the following files:
   • results/overtx.obj
   • results/synd_health.obj
   • results/synd_treat.obj

Files to run to plot results

• plot_calibrations.py consumes the result files:
  • results/zim_sti_calib_stats_treat*.df
  • results/zim_sti_par_stats_treat*.df and produces the plots shown in the supplementary materials
• plot_fig2_epi.py consumes the result files:
  • results/epi_df_treat*.df
  • results/sw_df_treat*.df and produces Figure 2 in the paper
• plot_fig3_pars.py consumes the result files:
  • results/zim_sti_calib_stats_treat*.df
  • results/zim_sti_par_stats_treat*.df and produces Figure 3 in the paper
• plot_fig4_overtreatment.py consumes the result files:
  • results/overtx.obj
  • results/synd_health.obj
  • results/synd_treat.obj and produces Figure 4 in the paper
• plot_sims.py is called by plot_calibrations.py