Modeling has become a tool capable of guiding public policies, especially in the area of health. Specifically, modeling in epidemiology makes it possible to follow the evolution of infections and to understand the behavior of viruses. Unfortunately, the traditional SIR models and the statistical prediction models commonly used suffer from the lack of accurate information and the unavailability of a large amount of data, also they do not take into account the interactions within the population. This paper proposes a hybrid SIR model which takes into consideration the spatio-temporal dynamics of individuals. The model is based on the discrete stochastic diffusion equations. To build the equation system, the 2D diffusion equations are coupled to the human displacement probability law pattern through a discretization made by the finite volume method for complex geometries. Beyond the health consequences it causes, COVID-19 (coronavirus) is a test case used to validate the proposed model. We used the case of a developed country before confinement to fit to the chosen displacement pattern, and to analyze the sensitivity of the parameters of the model taking into account the accuracy of the statistics provided.