  Studies in malaria control cover many areas such as medicine, sociology,
biology, mathematic, physic, computer science and so forth. Researches in the
realm of mathematic are conducted to predict the occurrence of the disease and
to support the eradication process. Basically, the modeling methodology is
predominantly deterministic and differential equation based while selecting
clinical and biological features that seem to be important. Yet, if the
individual characteristics matter when modeling the disease, the overall
estimation of the malaria is not done based on the health status of each
individual but in a non-specified percentage of the global population. The goal
of this paper is to propose a model that relies on a daily evolution of the
individual's state, which depends on their mobility and the characteristics of
the area they visit. Thus, the mobility data of a single person moving from one
area to another, gathered thanks to mobile networks, is the essential building
block to predict the outcome of the disease. We implement our solution and
demonstrate its effectiveness through empirical experiments. The results show
how promising the model is in providing possible insights into the failure of
the disease control in the Kedougou region.
