💫 Towards Sharing Tools and Artifacts for Reusable Simulation: deploying a simpy model as a web app

The materials in this rep are part of the ongoing STARS 2.0 study. An early version of the materials were part of STARS 1.0. The code and written materials here demonstrate the application of STARS 1.0 to sharing a simpy discrete-event simulation model and associated research artifacts.

Overview

The materials and methods in this repository support work towards developing the STARS healthcare framework (Sharing Tools and Artifacts for Reusable Simulations in healthcare). The code and written materials here demonstrate the application of S.T.A.R.S' version 1 to sharing a simpy discrete-event simuilation model and associated research artifacts.

• All artifacts in this repository are linked to study researchers via ORCIDs;
• Model code is made available under the MIT license;
• Python dependencies are managed through conda;
• The code builds a streamlit web application that can be used to run the model (web app);
• The materials are deposited and made citatable using Zenodo;
• The models are sharable with other researchers and the NHS without the need to install software.

Author ORCIDs

Citation

Please cite this model as:

Monks, T., Harper, A., & Heather, A. (2024). Towards Sharing Tools and Artifacts for Reusable Simulation: deploying a simpy model as a web app (v3.0.1). Zenodo. https://doi.org/10.5281/zenodo.11102678

bibtex @software{stars_streamlit_example, author = {Monks, Thomas and Harper, Alison and Heather, Amy}, title = {{Towards Sharing Tools and Artifacts for Reusable Simulation: deploying a `simpy` model as a web app}}, month = june, year = 2024, publisher = {Zenodo}, version = {v3.0.1}, doi = {10.5281/zenodo.11102678}, url = {https://doi.org/10.5281/zenodo.11102678} }

The model used in this example is based on Nelson (2013). Please credit their work if you use the model.

Nelson, B. (2013). Foundations and methods of stochastic simulation: a first course. London: Springer.

@book{nelson2013, title = "Foundations and methods of stochastic simulation: a first course", author = "Nelson, Barry", year = 2013, publisher = "Springer", address = "London" }

If you use this model, we would also appreciate a citation to our paper describing the work:

Monks, T., Harper, A., & Mustafee, N. (2024). Towards sharing tools and artefacts for reusable simulations in healthcare. Journal of Simulation, 1–20. https://doi.org/10.1080/17477778.2024.2347882

```bibtex @article{monkstowards2024, author = {Thomas Monks, Alison Harper and Navonil Mustafee}, title = {Towards sharing tools and artefacts for reusable simulations in healthcare}, journal = {Journal of Simulation}, volume = {0}, number = {0}, pages = {1--20}, year = {2024}, publisher = {Taylor & Francis}, doi = {10.1080/17477778.2024.2347882}, URL = {https://doi.org/10.1080/17477778.2024.2347882}, eprint = {https://doi.org/10.1080/17477778.2024.2347882} }

```

Funding

This work is supported by the Medical Research Council [grant number MR/Z503915/1].

Note: This model is part of the ongoing STARS 2.0 study. An early version of the model presented here was part of STARS version 1.0: independent research supported by the National Institute for Health Research Applied Research Collaboration South West Peninsula. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care.

Case study model

This example is based on exercise 13 from Nelson (2013) page 170.

Nelson. B.L. (2013). Foundations and methods of stochastic simulation. Springer.

We adapt a textbook example from Nelson (2013): a terminating discrete-event simulation model of a U.S based treatment centre. In the model, patients arrive to the health centre between 6am and 12am following a non-stationary Poisson process. On arrival, all patients sign-in and are triaged into two classes: trauma and non-trauma. Trauma patients include impact injuries, broken bones, strains or cuts etc. Non-trauma include acute sickness, pain, and general feelings of being unwell etc. Trauma patients must first be stabilised in a trauma room. These patients then undergo treatment in a cubicle before being discharged. Non-trauma patients go through registration and examination activities. A proportion of non-trauma patients require treatment in a cubicle before being discharged. The model predicts waiting time and resource utilisation statistics for the treatment centre. The model allows managers to ask question about the physical design and layout of the treatment centre, the order in which patients are seen, the diagnostic equipment needed by patients, and the speed of treatments. For example: “what if we converted a doctors examination room into a room where nurses assess the urgency of the patients needs.”; or “what if the number of patients we treat in the afternoon doubled”

Streamlit community cloud deployment of the code

• https://stars-simpy-example.streamlit.app/

A backup and replica of the web app is available here:

• https://treat-sim.streamlit.app

Please note that we have deployed this to the a free tier service. If the app has not been used for a time then you will need to "wake up" the app. Please be patient while it reboots.

Online documentation produced by Jupyter-book

We have a separate artifact that documents the model.

• The documentation can be access at https://pythonhealthdatascience.github.io/stars-simpy-example-docs

Docker container

A containerised version of the model is available from Dockerhub. Follow the link and the instructions provided. Note tht you will need docker installed in order to pull and run the container.

• https://hub.docker.com/r/tommonks01/streamlit_sim

How to create the model interface locally

Alternatively you may wish to create the website on your local machine.

Downloading the code

Either clone the repository using git or click on the green "code" button and select "Download Zip".

bash git clone https://github.com/pythonhealthdatascience/stars-streamlit-example

Installing dependencies

All dependencies can be found in binder/environment.yml and are pulled from conda-forge. To run the code locally, we recommend install mini-conda; navigating your terminal (or cmd prompt) to the directory containing the repo and issuing the following command:

bash conda env create -f binder/environment.yml

To activate the environment issue the following command:

bash conda activate stars_streamlit

Running the interface to the model

In the directory (folder) containing the code issue the following command via the terminal (or cmd prompt/powershell on windows)

bash streamlit run Overview.py

This should open your browser and launch the interface automatically. Alternatively you can navigate to the following URL.

bash http://localhost:8501