We attempted to reproduce the outputs from eight Python and R DES models, as described in:

Heather, A., Monks, T., Harper, A., Mustafee, N., & Mayne, A. (2025). On the reproducibility of discrete-event simulation studies in health research: an empirical study using open models. arxiv.https://doi.org/10.48550/arXiv.2501.13137.

We are developing a book which demonstrates how to create DES models in Python (SimPy) and R (simmer) which are reproducible - following the recommendations from our assessment above - and also, form a reproducible analytical pipeline. This is illustrated using a simple template model and an applied stroke model.

We conducted a systematic review of how healthcare DES models (any software/language) are currently shared in the literature.

We identified common barriers to model reuse, including lack of documentation and standardised repositories.

Key takeaway: There is a clear need for better frameworks and tools to support reusable simulation models.

Monks, T., & Harper, A. (2023). Computer model and code sharing practices in healthcare discrete-event simulation: a systematic scoping review. Journal of Simulation, 19(1), 108–123.https://doi.org/10.1080/17477778.2023.2260772

Following this review, we developed the STARS framework, which provides essential and optional guidance to help make shared models more reusable. This is described in:

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

This paper included three examples of the STARS framework implemented in Python:

Subsequently, we have been developing similar examples in R, as well as looking at webassembly:

We applied the STARS framework to an oncology cost-effectiveness model developed by PenTAG and collaborates for the NICE Pathways Pilot appraisal.

Tom was involved in developing the Strengthening The Reporting of Empirical Simulation Studies (STRESS) guidelines. They are a 20-item checklist (with guidelines for DES, agent-based simulation, and system dynamics) which outline the details authors should include about their model in their reports/publications.

Monks, T., Currie, C. S. M., Onggo, B. S., Robinson, S., Kunc, M., & Taylor, S. J. E. (2018). Strengthening the reporting of empirical simulation studies: Introducing the STRESS guidelines. Journal of Simulation, 13(1), 55–67.https://doi.org/10.1080/17477778.2018.1442155.

We are currently undergoing a review and update of the STRESS guidelines.

We investigated the ability to replicate Discrete-Event Simulation models reported in the literature using iterative prompting of Perplexity.AI's foundational model:

Monks, T., Harper, A., & Heather, A. (2025). Unlocking the Potential of Past Research: Using Generative AI to Reconstruct Healthcare Simulation Models. arxiv.https://doi.org/10.48550/arXiv.2503.21646

There is a tutorial on discrete-event simulation for the Operational Research Society Simulation Workshop 2025:

Monks, T., Harper, A., Heather, A., Mayne, A., & Mustafee, N. (2025). Building healthcare discrete-event simulation models in free and open source software: an introductory tutorial. Proceedings of the Operational Research Society Simulation Workshop 2025 (SW25).https://doi.org/10.36819/SW25.004.

Amy was shortlisted for the GW4 Open Research Prize for her work on assessing the computational reproducibility of published models. The slides from her presentation at the prize ceremony are here:

We used a topic modelling approach to examine and map the computational simulation literature applied to operational-level orthopedics services, combined with a structured analysis of the papers. The repository below serves as supplementary material for the paper:

Mapping Applications of Computer Simulation in Orthopedic Services: A Topic Modeling Approach.