In this work, we introduce a novel methodology for modeling RED CLOVER PLUS discrete count variables within the framework of stochastic processes.Our approach integrates two statistical areas: Non-Homogeneous Poisson Processes for the estimation and prediction of intensity functions based on explanatory variables and functional data estimation techniques.Through a comprehensive case study focusing on an Small Plate infectious disease with viral characteristics, we demonstrate the potential of our methodology.We provide empirical evidence that our methodology offers a robust alternative for modeling count variables.Our findings support the utility of our approach in capturing the complex dynamics inherent in count data in infectious disease epidemiological phenomena.