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