A new open training resource has been launched to help analysts and researchers build transparent and reproducible healthcare simulation models.
The DES RAP Book, created by Amy Heather from Peninsula Collaboration for Health Operational Research and Data Science (PenCHORD) within PenARC, provides a step-by-step guide to developing discrete-event simulation (DES) models using Python and R as part of a reproducible analytical pipeline (RAP).
The online book is freely available and designed as a self-paced learning resource for analysts, researchers, and students working in healthcare and operational research.
Discrete-event simulation is widely used to model healthcare systems, such as patient flows through clinics or hospitals, and to test how different service configurations might affect demand, waiting times, or capacity. By embedding these models within reproducible analytical pipelines, the workflow becomes transparent, automated, and repeatable, allowing results such as tables and figures to be regenerated directly from the underlying code and data.
Alongside the book, the resource includes four complete example repositories demonstrating how to implement reproducible DES models in both Python and R. These can be adapted by researchers developing their own models.
The resource has already been used in real-world modelling work. Analysts at The Strategy Unit used the guidance while developing an open-source renal capacity model to support planning for Kidney Replacement Therapy services.
Lucy Morgan, Analytics Manager at The Strategy Unit, said:
“The DES RAP Book was a valuable resource in our transition to open-source DES modelling. It provided us with a step-by-step structure to follow and guidance on best practice for writing code, tests and documentation for our package. Our model is being used in the Midlands, and beyond, for understanding future demand and capacity within Kidney Replacement Therapy.”
The DES RAP Book was developed as part of the STARS project (STARS: Sharing Tools and Artefacts for Reproducible Simulations in Healthcare) and is supported by the Medical Research Council [grant number MR/Z503915/1].
The book was reviewed by subject-matter experts and PhD students on the STARS project team:
- Associate Prof. Tom Monks– Peninsula Collaboration for Health Operational Research and Data Science, University of Exeter Medical School, UK.
- Prof. Nav Mustafee – Centre for Simulation, Analytics, and Modelling, University of Exeter Business School, UK.
- Fatemeh Alidoost – Centre for Simulation, Analytics, and Modelling, University of Exeter Business School, UK.
- Dr. Alison Harper – Centre for Simulation, Analytics, and Modelling, University of Exeter Business School, UK.
- Tom Slater – Department of Mathematics and Statistics, University of Exeter, UK
- Dr. Rob Challen – School of Engineering, Mathematics and Technology, University of Bristol, UK.
Author
