{"type":"ScholarlyArticle","title":"Enhancing Sustainability of Complex Epidemiological Models through a Generic Multilevel Agent-based Approach","authors":[{"name":"Sébastien Picault","slug":"sebastien-picault","given":"Sébastien","family":"Picault","affiliation":"a Bioepar , INRA , Oniris , La Chantrerie , 44307 Nantes , France; b Univ. Lille , CNRS , Centrale Lille , UMR 9189 -CRIStAL , F-59000 Lille , France"},{"name":"Yu-Lin Huang","slug":"yu-lin-huang","given":"Yu-Lin","family":"Huang","affiliation":"a Bioepar , INRA , Oniris , La Chantrerie , 44307 Nantes , France"},{"name":"Vianney Sicard","slug":"vianney-sicard","given":"Vianney","family":"Sicard","affiliation":"a Bioepar , INRA , Oniris , La Chantrerie , 44307 Nantes , France"},{"name":"Pauline Ezanno","slug":"pauline-ezanno","given":"Pauline","family":"Ezanno","affiliation":"a Bioepar , INRA , Oniris , La Chantrerie , 44307 Nantes , France"}],"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.","doi":"10.24963/ijcai.2017/53","url":"https://ijcai.org/proceedings/2017/53","pdf":"https://ijcai.org/proceedings/2017/0053.pdf","markdown":"https://ijcai.org/proceedings/2017/53.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/53","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":53,"pages":{"first":374,"last":380},"keywords":["Multidisciplinary Topics and Applications: Computational Biology and e-Health","Agent-based and Multi-agent Systems: Engineering methods, platforms, languages and tools","Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence"],"videos":[],"references":[{"position":1,"raw":"[Ajelli et al., 2010] M. 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