Enhancing Sustainability of Complex Epidemiological Models through a Generic Multilevel Agent-based Approach

Enhancing Sustainability of Complex Epidemiological Models through a Generic Multilevel Agent-based Approach

S├ębastien Picault, Yu-Lin Huang, Vianney Sicard, Pauline Ezanno

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 374-380. https://doi.org/10.24963/ijcai.2017/53

The development of computational sciences has fostered major advances in life sciences, but also led to reproducibility and reliability issues, which become a crucial stake when simulations are aimed at assessing control measures, as in epidemiology. A broad use of software development methods is a useful remediation to reduce those problems, but preventive approaches, targeting not only implementation but also model design, are essential to sustainable enhancements. Among them, AI techniques, based on the separation between declarative and procedural concerns, and on knowledge engineering, offer promising solutions. Especially, multilevel multi-agent systems, deeply rooted in that culture, provide a generic way to integrate several epidemiological modeling paradigms within a homogeneous interface. We explain in this paper how this approach is used for building more generic, reliable and sustainable simulations, illustrated by real-case applications in cattle epidemiology.
Keywords:
Agent-based and Multi-agent Systems: Engineering methods, platforms, languages and tools
Multidisciplinary Topics and Applications: Computational Biology and e-Health
Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence