DES RAP Book is an open resource and website for building discrete-event simulation (DES) models within a reproducible analytical pipline (RAP), supporting the healthcare simulation community.
The resource demonstrates practical, code-based workflows and tools to help researchers and practitioners develop, validate, and share DES models in Python (SimPy) and R (simmer), ensuring models are reproducible.
Check it out at: https://pythonhealthdatascience.github.io/des_rap_book/.
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Researchers, analysts, practitioners, and students in simulation modeling - especially those in healthcare and operations research.
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Anyone using Python or R who is seeking practical guidance on best practices for reproducibility, with many of the sections (e.g. environments, version control, documentation, testing) being broadly relevant to any research software and data science projects.
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Accessible to a range of experience levels. The material is designed to be approachable, though some familiarity with Python or R, and basic command line usage, is recommended. Prior experience with simulation modeling is also helpful.
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Getting started: introduction to reproducibility and open-source.
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Building models: Structured guidance on model inputs, implementation, experimentation, and analysis with clear, reproducible code examples in Python and R. This includes recommendations for experimentation, output analysis, and verification and validation.
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Best practices: Code packaging, environment management, version control, linting, testing, and documentation for robust and transparent workflows.
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Reporting and collaboration: Generating tables/figures, licensing, sharing, peer review, archiving, citation, and changelogs.
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Visit the DES RAP Book website for all tutorials and resources (also viewable locally as described in
CONTRIBUTING.md). -
The example model repositories linked from the book are:
This resource has been developed as part of the project STARS: Sharing Tools and Artefacts for Reproducible Simulations in healthcare.
The project tackles the challenges of sharing, reusing, and reproducing discrete event simulation (DES) models in healthcare. Our goal is to create open resources using the two most popular open-source languages for DES: Python and R.
We have been developing tutorials, code examples, and tools to help researchers and practitioners develop, validate, and share DES models more effectively.
For more information on this project, check out the STARS project website.
If you this book supports your work, please cite our paper:
Heather A, Monks T, Harper A et al. Reproducible analytical pipelines for healthcare discrete‑event simulation: An open guide and worked examples [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:68 (https://doi.org/10.3310/nihropenres.14296.1)
You may choose to also cite the software repository or archived version:
- Repository details are also provided in the
CITATION.cfffile in this repository or via the "Cite this repository" button on GitHub. - Archived version of this work on Zenodo: https://doi.org/10.5281/zenodo.17094155.
This site uses W3C's Web Accessibility Initiative (WAI) Easy Checks as a lightweight accessibility framework. Please see this GitHub issue for a record of which checks are currently met and any known limitations.
If you're interested in contributing (or just viewing this website locally), check out the CONTRIBUTING.md file.
 Amy Heather 🐛 💻 🖋 📖 🎨 🤔 🚇 ✅ |
 Tom Monks 🖋 📓 |
 Nav Mustafee 🖋 📓 |
 Alison Harper 🖋 📓 |
 Fatemeh Alidoost 🖋 📓 |
 Rob Challen 🖋 📓 |
 Tom Slater 🖋 📓 |
This project is supported by the Medical Research Council [grant number MR/Z503915/1] from 1st May 2024 to 31st October 2026.
It is also supported by the National Institute for Health and Care Research (NIHR) under the NIHR Applied Research Collaboration South West Peninsula (Grant Reference Number NIHR200167). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
