An Agent Based Model for Studying the Impact of Rainfall on Rift Valley Fever Transmission at Ferlo (Senegal) Python Ndekou Tandong Paul 1(B ) , Alassane Bah 2 , Papa Ibrahima Ndiaye 3 , and Jacques Andr´ e Ndione 4 1 Department of Mathematics and Computer Science, Cheikh Anta Diop University, Dakar, Senegal pppython@yahoo.fr 2 ESP, Department of Mathematics and Computer Science, Cheikh Anta Diop University, Dakar, Senegal alassane.bah@gmail.com 3 Department of Mathematics, Alioune Dione University, Bambey, Senegal papaibra.ndiaye@uadb.edu 4 Centre de suivi Ecologique, Dakar, Senegal jacques-andre.ndione@cse.sn Abstract. In this paper, we created a conceptual model UML (unified modelling language) showing interactions between animals, mosquitoes, environment, and climate factors. The UML static model was used to build an agent-based model that helps to study the impact of rainfall vari- ability on the number of infected hosts after the outbreak of Rift valley fever. Several simulations were done on the multi-agent CORMAS plat- form. The different results showed continued growth in infections during the rainy season. A sensitivity analysis taking into account the delayed rains and dry spells have allowed us to know a clear vision about the rate of infected animals due to the rainfall variability. This work defines a framework for studying the impact of climatic changes on vector-borne diseases. 1 Introduction Rift valley Fever is a disease transmitted by Aedes vexans or Culex poicilipes mosquitoes. This disease cannot be eradicated without the expertise of epidemi- ologists taking into account climate factors. For a better understanding of mecha- nisms of outbreak and propagation, the modeling of local and global transmission should be made for the further study of this disease. A brief overview has been done on recent studies that have shown that: In the 2000s, a study on the exis- tence of vector-borne diseases has led to first isolate the pathogen agent of Rift Valley fever [1, 2] in Ferlo. The environmental hypothesis has been the subject of multiple studies with the use of remote sensing data as an indicator of viral activity in Kenya [3, 9]. In 2005, Mondet et al. [4, 5] have shown links between c  Springer International Publishing AG, part of Springer Nature 2018 O. Gervasi et al. (Eds.): ICCSA 2018, LNCS 10961, pp. 281–290, 2018. https://doi.org/10.1007/978-3-319-95165-2_20