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:

Installation

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

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.

How to run

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)

singleresult = runner.runsingle(run=0) represults = runner.runreps() ```

Example altering the model parameters:

``` from simulation import Param, Runner

Modify the number of replications

param = Param(numberofruns=10) runner = Runner(param=param) represults = runner.runreps() ```

Generating the results from the article

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.

Examples

Figure 1

Original:

From this repository:

Figure 3

Original:

From this repository:

Run time and machine specification

The run time for this analysis (notebooks/analysis.ipynb) is 10 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.

Community

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

Citation

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.

Contributors:

Amy Heather - developed the repository.

Tom Monks - peer review of the repository.

Licence

MIT Licence. See LICENSE for details.

Funding

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].