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Merge pull request #29 from pythonhealthdatascience/amy
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‎README.md

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## 📌  Introduction
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<!--TODO: Add links to STARS, relevant publication, and https://github.com/pythonhealthdatascience/rap_des/ -->
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This repository provides a template for building discrete-event simulation (DES) models in Python.
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😊 **Simple:** Easy-to-follow code structure using [SimPy](https://simpy.readthedocs.io/en/latest/). Implements a simple M/M/s queueing model in which patients arrive, wait to see a nurse, have a consultation with the nurse and then leave. Follows a modular structure: uses object-oriented programming, with the simulation implemented through classes.
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* ["Levels of RAP" framework](https://nhsdigital.github.io/rap-community-of-practice/introduction_to_RAP/levels_of_RAP/) from the NHS RAP Community of Practice (`docs/nhs_rap.md`).
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* Recommendations from [Heather et al. 2025](https://doi.org/10.48550/arXiv.2501.13137) "*On the reproducibility of discrete-event simulation studies in health research: an empirical study using open models*" (`docs/heather_2025.md`).
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🚀 **Extendable:** This template adapts from Sammi Rosser and Dan Chalk (2024) ["HSMA - the little book of DES"](https://github.com/hsma-programme/hsma6_des_book). The book includes additional advanced features that can be used to extend the model in this template, including:
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🚀 **Extendable:** This template is partly adapted from Sammi Rosser and Dan Chalk (2024) ["HSMA - the little book of DES"](https://github.com/hsma-programme/hsma6_des_book). The book includes additional advanced features that can be used to extend the model in this template, including:
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* Multiple activities
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* Branching paths
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The overall run time will vary depending on how the template model is used. A few example implementations are provided in `notebooks/` and the run times for these were:
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* `analysis.ipynb` - 23s
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* `choosing_parameters.ipynb` - 22s
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* `choosing_cores.ipynb` - 21s
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* `choosing_replications.ipynb` - 11s
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* `choosing_warmup.ipynb` - 2s
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* `generate_exp_results.ipynb` - 0s
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<!--TODO: Add test times -->
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<!--TODO: Update run times -->
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These times were obtained on an Intel Core i7-12700H with 32GB RAM running Ubuntu 24.04.1 Linux.
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Researcher details:
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| Contributor | Role | ORCID | GitHub |
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| --- | --- | --- | --- |
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| Amy Heather | Author | [![ORCID: Heather](https://img.shields.io/badge/ORCID-0000--0002--6596--3479-brightgreen)](https://orcid.org/0000-0002-6596-3479) | https://github.com/amyheather |
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| Tom Monks | Code review | [![ORCID: Monks](https://img.shields.io/badge/ORCID-0000--0003--2631--4481-brightgreen)](https://orcid.org/0000-0003-2631-4481) | https://github.com/TomMonks |
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| Contributor | ORCID | GitHub |
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| --- | --- | --- |
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| Amy Heather | [![ORCID: Heather](https://img.shields.io/badge/ORCID-0000--0002--6596--3479-brightgreen)](https://orcid.org/0000-0002-6596-3479) | https://github.com/amyheather |
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| Tom Monks | [![ORCID: Monks](https://img.shields.io/badge/ORCID-0000--0003--2631--4481-brightgreen)](https://orcid.org/0000-0003-2631-4481) | https://github.com/TomMonks |
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<br>
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‎docs/hsma_changes.md

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# Changes from the HSMA model
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This template model is adapted from Sammi Rosser and Dan Chalk (2024) HSMA - the little book of DES (https://github.com/hsma-programme/hsma6_des_book) (MIT Licence). It also uses an exponentional distribution class from Monks (2021) sim-tools: fundamental tools to support the simulation process in python (https://github.com/TomMonks/sim-tools) (MIT Licence).
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This template model is based on a few sources - including: Sammi Rosser and Dan Chalk (2024) HSMA - the little book of DES (https://github.com/hsma-programme/hsma6_des_book) (MIT Licence).
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This page explains some of the differences between the models in the HSMA book, and this template.
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‎images/replications_algorithm.drawio

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‎images/replications_algorithm.png

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‎images/replications_statistics.png

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