Computational modeling of epidemiological count data using Non-Homogeneous Poisson Processes and functional data

Computational modeling of epidemiological count data using Non-Homogeneous Poisson Processes and functional data

Blog Article

In this work, we Yellow Dock introduce a novel methodology for modeling 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 infectious disease with viral HYALURONIC ACID NIGHT CREME 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.