README: +case study desc

Author: TomMonks

README.md

@@ -6,27 +6,39 @@
 
 #  Towards Sharing Tools and Artifacts for **Reproducible** Simulation **(v1.5)**: a JuypterLite template for `simpy` models
 
-## Overview
+## 1. Overview
 
 The materials and methods in this repository support work towards developing the S.T.A.R.S healthcare framework version 1.5 (**S**haring **T**ools and **A**rtifacts for **R**eproducible **S**imulations in healthcare).  The code and written materials here are a work in progress to demonstrate the application of S.T.A.R.S' version to sharing a `simpy` discrete-event simuilation model and associated research artifacts. 
 
 The model will run on a users browser without the need to install any components.  This is achieved using Web Assembly technology i.e. [JupterLite](https://github.com/jupyterlite/jupyterlite) and [xeus-python](https://github.com/jupyter-xeus/xeus-python).  A model notebook is downloaded to the users local machine and all dependencies are pre-installed via conda-forge. The model then lives in the browsers cache. The user can make changes to the model or create new files and these are persisted (until the browser cache is cleared).  
 
-### Use case
+### 1.1. Use case
 
 * A researcher wishes to share a runnable version of a simulation model with their publication (e.g. written in `simpy`).  The code allows others to replicate the simulation results, tables and charts in a paper and allows others to reuse the model.
 * The researcher wants the model to be immediately usable. Users should not need to install python, `simpy` or any dependencies.
 * The researcher either wants to reduce load on online open science compute infrastructure (e.g. mybinder.org) or does not want to rely on it. 
 * Users may want to use a version of their own data due to governance, ethics or other reasons cannot upload this to a remote instance of the model.
 * Loading the model is as simple as clicking a URL.
 
-### Credits ✨
+### 1.2. Credits ✨
 
-We would like to thank the [JupterLite](https://github.com/jupyterlite/jupyterlite) and [xeus-python](https://github.com/jupyter-xeus/xeus-python) developers for making this work possible. This discrete-event simulation focussed repository was based on the learning materials and template provided by [Jupyterlite xeus-python demo](https://github.com/jupyterlite/xeus-python-demo) and [tutorial given at PyData 2023](https://www.youtube.com/watch?v=WXRslU9D3bo) by Jeremy Tuloup.
+> We would like to thank the [JupterLite](https://github.com/jupyterlite/jupyterlite) and [xeus-python](https://github.com/jupyter-xeus/xeus-python) developers for making this work possible. This discrete-event simulation focussed repository was based on the learning materials and template provided by [Jupyterlite xeus-python demo](https://github.com/jupyterlite/xeus-python-demo) and [tutorial given at PyData 2023](https://www.youtube.com/watch?v=WXRslU9D3bo) by Jeremy Tuloup.
+
+## 2. The example model included
+
+The `simpy` model is adapted from [Monks and Harper (2023)](https://github.com/pythonhealthdatascience/stars-simpy-example-docs)
+
+> Monks, T., & Harper, A. (2023). Towards Sharing Tools and Artifacts for Reusable Simulation: example enhanced documentation for a simpy model. (v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.10054063
+
+Full documentation of this model is available in our [JupyterBook](https://pythonhealthdatascience.github.io/stars-simpy-example-docs)
+
+In summary, we adapt a textbook example from Nelson (2013): a terminating discrete-event simulation model of a U.S based treatment centren summary the model. The example is based on exercise 13 from Nelson (2013) page 170.
+
+> *Nelson. B.L. (2013). [Foundations and methods of stochastic simulation](https://www.amazon.co.uk/Foundations-Methods-Stochastic-Simulation-International/dp/1461461596/ref=sr_1_1?dchild=1&keywords=foundations+and+methods+of+stochastic+simulation&qid=1617050801&sr=8-1). Springer.*
 
 ## ✨ Try it in your browser ✨
 
-https://jupyterlite.github.io/xeus-python-demo/notebooks/?path=demo.ipynb
+https://pythonhealthdatascience.github.io/stars-simpy-jupterlite/notebooks/?path=01_urgent_care_model.ipynb
 
 ## ≠ How does it compare to the Pyodide kernel?