Coupling equation based models and agent-based models: example of a multi-strains and switch SIR toy model Nghi Quang Huynh 1 , Tri Nguy en-Huu 2 ,3 ,5 , Arna ud Grignard 5 , Hiep Xuan Huynh 1 , Alexis Drog oul 2 ,4 1 DREAM-CTU/IRD, CICT-CTU, Cantho, Vietnam 2 IRD, Centre Ile-de-F rance, 32 avenue Henri Varagna t, 93140 Bondy , France 3 Facul té des Sciences de Semlalia, Univ ersité Cadi Ayyad, Marr akech, Maroc 4 Univ ersity of Science and Technol ogy of Hanoi, Hanoi, Vietnam 5 IXXI, ENS Lyon, 46 allée d’Italie, 69364 Lyon Cedex 07 Abstract Modeling in ecol ogy or epidemiol ogy gener all y opposes tw o classes of models, Equa tion Based Models and Agent Based Models. Mathema tical models all ow predicting the long- term dynamics of the studied systems. How ev er, the variability betw een individ uals is di fficul t to represen t, wha t makes these more suitable models for larg e and homog eneous popula tions. Mul ti-ag ent models all ow represen ting the attributes and beha vior of each individ ual and theref ore provide a grea ter lev el of detail. In return, these systems are more di fficul t to anal yze. These approaches have often been compared, but rarel y used sim ultaneousl y. We propose a hybrid approach to couple equa tions models and agent-based models, as well as its implemen tation on the modeling pla tf orm Gama [8 ]. We focus on the represen tation of a classical theoretical epidemiol ogical model (SIR model) and we ill ustr ate the construction of a class of models based on it. Receiv ed on 01 March 2016; accepted on 08 July 2016; published on 06 March 2017 Keywords: Equa tion-based model, ag en t-based model, coupling fr amew ork, sim ula tion pla tf orm, epidemiol ogy Copyright © 2017 Nghi Quang Huynh et al., licensed to EAI. This is an open access article distributed under the terms of the Crea tiv e Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reprod uction in any medium so long as the original work is proper ly cited. doi:10.4108/eai.6-3-2017.152334 1. Introduction Agent Based Modeling and Equa tion Based Modeling are tw o common modeling approaches for dynamical systems. Equa tion Based Models (EBMs) usuall y describe the dynamical processes at the gl obal scale (at the popula tion lev el in ecol ogy) while Agent Based Models (ABMs) describe the same processes at the local scale (at the individ ual lev el in ecol ogy). Each approach offers di fferen t adv an tag es and drawbacks. The scale at which the processes are represen ted determines the way the model is constructed: gl obal processes, a small number of par ameters and no individ ual variability for the EBMs; individ ual processes, high lev el of detail ★ Please ensure tha t you use the most up to da te class file available from EAI at http://doc.eai.eu/publications/transactions/ latex/ * Corresponding author . Email: tri.nguyen-huu@ens-lyon.fr for ABMs. EBMs do not take into accoun t individ ual variability , assuming tha t mean fie d approxima tions convenien tl y describe the dynamics at the gl obal lev el. ABMs are relev ant when this individ ual variability has strong e ffect on the dynamics emerging at the global lev el. Additionall y they all ow explicit represen ta tions of the inter action netw ork of individ uals when its topol ogy has consequences on the dynamics of the system and the emerg ence of properties at the global lev el. ABMs also offer the possibility of an easy integr ation of GIS and social netw ork inf orma tion. Apart from conceptual aspects, the comm unity of the modeler has a strong inf uence on which approach will be chosen. A strong knowledg e in ma thema tics is needed to understand and buil d equa tions for the EBM approach. As a coun terpart , ma thema tics offer pow erful tools to anal yse EBMs, providing a lot of in-depth inf orma tion about the dynamics, such as equilibria and 1 EAI Endorsed Transactions on Context-aware Systems and Applications Research Article EAI Endorsed Transactions on Context-aware Systems and Applications 09 2016 - 03 2017 | Volume 4 | Issue 11 | e1