README for reproduction

This page contains a copy of the README produced to support reproduction

COVID-19 Dialysis Service Delivery Model

Model summary

Allen, M., Bhanji, A., Willemsen, J., Dudfield, S., Logan, S., & Monks, T. A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic. PLoS One 15, 8 (2020). https://doi.org/10.1371%2Fjournal.pone.0237628.

This is a discrete-event simulation modeling patient allocation to dialysis units during the pandemic. The patients need to be transported to a dialysis unit several times a week but, during the pandemic, it was required that COVID positive patients were kept seperate from COVID negative patients. The proposed plan was that all COVID negative units were sent to a particular unit (with a second overflow unit). This study tests that plan with a worst-case scenario of COVID spread over 150 days.

Model patient pathway figure from the original study:

Patient pathway figure. Image and caption source: Allen et al. 2020.

Scope of the reproduction

In this assessment, we attempted to reproduced three figures.

Figure 2. “Patient state over time by unit. The patient population progresses through infection over three months (with 80% infected). The bold line shows the median results of 30 trials, and the fainter lines show the minimum and maximum from the 30 trials.” Image and caption source: Allen et al. 2020.

Figure 3. “Progression of patient population through COVID infection, assuming 80% become infected over three months, with 15% mortality. The figure also shows the number of patients not allocated to a dialysis session at any time. The bold line shows the median results of 30 trials, and the fainter lines show the minimum and maximum from the 30 trials.” Image and caption source: Allen et al. 2020.

Figure 4. “Patient displacement. The number of patients displaced from their current unit (left panel) and the additional travel time to the unit of care (right panel) for displaced patients. These results do not include those receiving inpatient care. The patient population progresses through infection over three months (with 80% infected). The bold line shows the median results of 30 trials, and the fainter lines show the minimum and maximum from the 30 trials.” Image and caption source: Allen et al. 2020.

Reproducing these results

Repository overview

├── data
│   └──  ...
├── docker
│   └──  ...
├── output
│   └──  ...
├── sim
│   └──  ...
├── tests
│   └──  ...
├── environment.yaml
├── README.md
└── reproduction.ipynb
  • data/ - Data input to the model.
  • docker/ - Instructions for creation of Docker container.
  • output/ - Output files from the model.
  • sim/ - Model code.
  • tests/ - Test to check that model produces consistent results with our reproduction.
  • environment.yaml - Instructions for creation of Conda environment.
  • README.md - This file!
  • reproduction.ipynb - Notebook which runs model and reproduces items from scope.

Step 1. Set up environment

You’ll first want create an environment with the specified version of Python and the required packages installed. There are a few options…

Option A: Conda environment

Create the environment using this command in your terminal: conda env create -f environment.yaml

You can use this environment in your preferred IDE, such as VSCode. To use the browser-based JupyterLab, activate it using conda activate covid19, and then open JupyterLab by running jupyter-lab

Option B: Docker

You’ll need docker installed on your local machine. You can then obtain the image by either:

  • Pulling a pre-built image from the GitHub container registry by running docker pull ghcr.io/pythonhealthdatascience/covid19:latest, or
  • Building the image locally from the Dockerfile by running docker build --tag covid19 .

To run the image, you should then issue the following commands in your terminal:

  • docker run -it -p 8080:80 --name covid19_docker covid19
  • conda activate covid19
  • jupyter-lab

Then open your browser and go to https://localhost:8080. This will open Jupyterlab within the reproduction/ directory.

Step 2. Running the model

To run the model and produce all three items from the scope, execute the notebook reproduction.ipynb.

To check that your results are consistent with our reproduction, run the command pytest in your terminal. When doing so, ensure your current directory is the reproduction/ folder.

Reproduction specs and runtime

This reproduction was conducted on an Intel Core i7-12700H with 32GB RAM running Ubuntu 22.04.4 Linux.

Expected model runtime (given these specs) is 1m 3s (as recorded within the notebook).

Citation

To cite the original study, please refer to the reference above. To cite this reproduction, please refer to the CITATION.cff file in the parent folder.

License

This repository is licensed under the MIT License.