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STIsim

tests PyPI

STIsim is an agent-based modeling framework in which users can design and configure simulations of co-circulating sexually-transmitted diseases. STIsim uses the Starsim architecture, and belongs to the Starsim model suite which also includes Covasim, HPVsim, and FPsim.

Requirements

Python 3.9-3.14.

Installation

STIsim is most easily installed via PyPI: pip install stisim. This will also install HIVsim.

STIsim can also be installed locally. To do this, clone first this repository, then run pip install -e . (don't forget the dot at the end!).

Usage and documentation

STIsim is still in the early stages of its development as a standalone software package, and therefore is still lacking complete documentation. We are working on a user guide and tutorials, but in the meantime, the best ways to learn about the model are: 1. Read the articles that have been published about analyses using STIsim (see references below) 2. Email us: info@starsim.org 3. Check on the documentation currently available at https://docs.stisim.org.

References

Publications using STIsim include:

  1. Reduction in overtreatment of gonorrhoea and chlamydia through point-of-care testing cmpared with syndromic management for vaginal discharge: a modelling study for Zimbabwe (2026). Stuart RM, Newman LM, Manguro G, Dziva Chikwari C, Marks M, Peters RPH, Klein D, Snyder L, Kerr C, Rao DW. Sex Transm Infect https://doi.org/10.1136/sextrans-2025-056646. Preprint: https://doi.org/10.21203/rs.3.rs-8843262/v1

  2. Point-of-care testing to strengthen sexually transmitted infection case management in resource-constrained settings (2026). Peters RPH, Manguro G, Ong'wen PA, Mdingi MM, Applegate TL, Stuart R, Harding-Esch EM, Manabe YC, Ndowa F, Van Der Pol B. Sex Transm Infect, https://doi.org/10.1136/sextrans-2025-056833.

Contributing

Questions or comments can be directed to info@starsim.org, or on this project's GitHub page.

See .github/workflows/README.md for details on publishing new releases of STIsim.

Disclaimer

The code in this repository was developed by IDM, the Burnet Institute, and other collaborators to support our joint research on flexible agent-based modeling. We've made it publicly available under the MIT License to provide others with a better understanding of our research and an opportunity to build upon it for their own work. We make no representations that the code works as intended or that we will provide support, address issues that are found, or accept pull requests. You are welcome to create your own fork and modify the code to suit your own modeling needs as permitted under the MIT License.