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Cost-effectiveness of wastewater-based environmental surveillance for SARS-CoV-2 in Blantyre, Malawi and Kathmandu, Nepal: a model-based study

This code is released in support of the above manuscript, which is currently under review. The full citation will be added upon acceptance.

Structure

All code used to generate model-based results is in this repository. The structure is as follows:

  • The Python library code is in covasim_es.
  • Script files used to run and process the simulations for Malawi are in malawi_scripts. The main file used to generate the results is sweep_lines.py, but other scripts are included for completeness. These scripts are computationally intensive, requiring roughly 140 core-hours on a high-performance compute cluster. Do not try to run on your laptop without first setting debug = True.
  • Equivalent scripts for Nepal are in nepal_scripts.
  • Scripts used to produce the figures in the manuscript are in figures.
  • The pre-generated data files loaded by the scripts are stored in results. These are in compressed binary format, but can be loaded as pandas dataframes (and then exported to Excel if desired) using Sciris (specifically, sc.load()).

Installation

  1. Ensure you have Python installed (if you haven't installed Python already, the easiest is to use Anaconda; an out-of-the-box system Python installation is unlikely to work).

  2. In a terminal (or Windows command prompt), type pip install -e . to install (note the "." at the end of the command, this is critical!).

  3. To test your installation, import covasim_es should work from a Python prompt.

If you're using R, you need to have R installed. You also need to install reticulate (which allows R to communicate with Python): install.packages("reticulate"). Note: Even if you're using R, you still need to follow the first two steps to install the Python package.

Usage

The code is provided on an as-is basis and there may be practical challenges in getting it to run on a particular system (see note about compute requirements above). However, it should at least be possible to regenerate the figures by running the scripts in the figures folder. All scripts should take only seconds to run, except for fig1_timeseries.py (which takes 5-60 minutes to run, depending on your laptop).

Disclaimer

The code in this repository was developed by IDM, PATH, MLW, and ENPHO. We've made it publicly available under the MIT License to provide others with a better understanding of our research and an opportunity to build upon it for their own work. We make no representations that the code works as intended or that we will provide support, address issues that are found, or accept pull requests. You are welcome to create your own fork and modify the code to suit your own modeling needs as permitted under the MIT License.