Malaria remains an endemic disease in many developing countries around the world. For several decades, various malaria control strategies have been developed and implemented in many countries. Many fields like medicine, biology, physics, mathematics, and computer sciences are used to set up these strategies. Mathematical models have proven to be very useful in understanding malaria progression and dynamics. Mainly, these models are based on differential equations and take into account the clinical and biological characteristics of patients and vectors. They made it possible to study the parts of the population which pass from a state of exposure to the disease to different possible states as a result. These mathematical models in general consider global estimates of the evolution of the epidemic based on unspecified percentages of the population. Individuals are considered fairly evenly. However, the health status of each individual as well as their own daily activities can have a particular influence on the epidemic evolution. The objective of this paper is to propose a model based on a daily evolution of the individual’s condition, which depends on their mobility and the characteristics of the territory they visit. Thus, mobility data of a single person moving from one area to another is used to calculate disease progression and predict outcomes. We implement our solution and demonstrate its effectiveness through empirical experiments. The results show how promising the model is in providing possible information on the failure of disease control in the Kédougou region.