DES RAP Book

Self-paced training resource on building, implementing, and sharing discrete-event simulation models in Python and R within a reproducible analytical pipeline, combining a step-by-step guide with complete example repositories.

I worked on this from December 2024 to April 2026. I developed the materials, and they were reviewed by Tom Monks, Alison Harper, Fatemeh Alidoost, Rob Challen, Tom Slater and Nav Mustafee.

This work is described in our publication in the NIHR Open Research journal.

Book

The book is available at: https://pythonhealthdatascience.github.io/des_rap_book/

Examples

The book is accompanied by four worked examples:

• Python M/M/s model - https://github.com/pythonhealthdatascience/pydesrap_mms
• R M/M/s model - https://github.com/pythonhealthdatascience/rdesrap_mms
• Python stroke model - https://github.com/pythonhealthdatascience/pydesrap_stroke
• R stroke model - https://github.com/pythonhealthdatascience/rdesrap_stroke

STARS

This book was developed as part of the project STARS: Sharing Tools and Artefacts for Reproducible & Reusable Simulations in healthcare. I have created a website where you can find out more about this project: https://pythonhealthdatascience.github.io/stars/.