This repository is an example accompanying the DES RAP Book — an open educational resource on reproducible discrete-event simulation (DES) in Python and R. The book demonstrates best practices for building, validating, and sharing DES models within a reproducible analytical pipeline (RAP). The pydesrap_stroke model illustrates how these principles can be applied to a real-world healthcare capacity planning problem.

This repository provides a reproducible analytical pipeline (RAP) that implements a real-life stroke service capacity planning model in python. It serves as a practical example, fully reproducible and aligned with RAP principles, based on the following study:

Monks T, Worthington D, Allen M, Pitt M, Stein K, James MA. A modelling tool for capacity planning in acute and community stroke services. BMC Health Serv Res. 2016 Sep 29;16(1):530. doi: 10.1186/s12913-016-1789-4. PMID: 27688152; PMCID: PMC5043535.

Model diagram:

Clone the repository locally:

git clone https://github.com/pythonhealthdatascience/pydesrap_stroke.git
cd pydesrap_stroke

Set up the Python environment using conda (recommended):

conda env create --file environment.yaml
conda activate pydesrapstroke

There is also a requirements.txt file which can be used to set up the environment with virtualenv, but this won't fetch a specific version of Python - so please note the version listed in environment.yaml.

The simulation code is provided as a package within simulation/. There are notebooks executing the model and analysing the results in notebooks/.

To run the model with base parameters once or with replications:

from simulation import Param, Runner

param = Param()
runner = Runner(param=param)

single_result = runner.run_single(run=0)
rep_results = runner.run_reps()

Example altering the model parameters:

from simulation import Param, Runner

# Modify the number of replications
param = Param(number_of_runs=10)
runner = Runner(param=param)
rep_results = runner.run_reps()

The original study used Simul8. Each of the outputs from that article have been replicated in this repository using Python:

• Figure 1. Simulation probability density function for occupancy of an acute stroke unit.
• Figure 3. Simulated trade-off between the probability that a patient is delayed and the no. of acute beds available.
• Table 2. Likelihood of delay. Current admissions versus 5% more admissions.
• Table 3. Results of pooling of acute and rehab beds.
• Supplementary Table 1. Likelihood of delay. Current admissions versus No Complex neurological patients.
• Supplementary Table 3. Likelihood of delay. Current admissions versus ring fenced acute stroke beds.

To generate these, simply execute notebooks/analysis.ipynb.

Figure 1

Original:

From this repository:

Figure 3

Original:

From this repository:

The run time for this analysis (notebooks/analysis.ipynb) is 38 seconds. This was on an Intel Core i7-12700H, 32GB RAM, Ubuntu 24.04.1.

The other notebooks generate results for tests and illustrate other functionality (e.g. importing parameters from csv, running with logs), and these just take a second or two.

For this applied example, please cite either:

Heather, A. Monks, T. (2025). Stroke capacity planning model: python DES RAP. GitHub. https://github.com/pythonhealthdatascience/pydesrap_stroke.

Heather, A. Monks, T. (2025). Stroke capacity planning model: python DES RAP. Zenodo. https://doi.org/10.5281/zenodo.15574906.

A CITATION.cff file is also provided.

Amy Heather - developed the repository.

Tom Monks - peer review of the repository.

MIT Licence. See LICENSE for details.

Curious about contributing? Check out the contributing guidelines to learn how you can help.

This project was developed as part of the project STARS: Sharing Tools and Artefacts for Reproducible Simulations. It is supported by the Medical Research Council [grant number MR/Z503915/1].