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
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 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.
Curious about contributing? Check out the contributing guidelines to learn how you can help.
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.
MIT Licence. See LICENSE for details.
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].