Simulating hospital acquired infection transmission with the agents assembly ecosystem
- Przemyslaw Holda
- Kajetan Rachwal
- Wiktoria Glowniak
- Marcin Rojek
(2023). ECMS 2023, 37th Proceedings
Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation.
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-79-1 (CD) Communications of the ECMS Volume 37, Issue 1, June 2023, Florence, Italy June 20th – June 23rd, 2023
Przemyslaw holda, Kajetan rachwal, Wiktoria glowniak, Marcin rojek (2023). Simulating Hospital Acquired Infection Transmission With The Agents Assembly Ecosystem, ECMS 2023, Proceedings Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation. doi:10.7148/2023-0480
While agent-based models and simulations materialize in multiple areas, the existing simulation-focused agent platforms require in-depth programming knowledge, or are overly simplistic. In this context, the Agents Assembly (AASM) domain-specific language and platform have been recently proposed. The AASM ecosystem is highly scalable, as the software stack allows its straightforward deployment on multiple networked computers (physical or virtual machines). The domain-specific language has been designed to capture key concepts, needed to run agent-based simulations, while hiding their technical aspects. It can be thus stipulated that the AASM may provide a mid-way point between agent researchers and domain specialists. In this context, the aim of this contribution is twofold. First, to outline the reasoning behind, and details of, improvements introduced to the AASM since the original release. Second, show how the AASM can facilitate cooperation with researchers with a medical background. Here, the simulation models of the behavior of the Clostridium difficile bacteria in a hospital environment have been jointly conceptualized and experimentally explored. The developed model was focused on capturing features that reduce the spread of the bacteria. While the proposed scenarios are relatively simple, they illustrate the ease with which the AASM can be used to capture real-life phenomena.