DES RAP Book

Self-paced training resource that teaches you how to design, implement, and share discrete-event simulation (DES) models in Python and R as part of a reproducible analytical pipeline. It combines a step-by-step guide with complete example repositories that you can adapt for your own projects.

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.

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/.