Models and methods for the statistical analysis of recurrent events can be useful to make inference on epidemic processes. In this talk, we introduce a new class of parametric dynamic models for recurrent event processes. Models in this class are an extension of the self-exciting processes, and can include internal and external covariates. An important feature of them is that they dynamically adapt to the change points. We discuss the estimation of model parameters and asymptotic properties of the estimators. We present the results of a simulation study conducted to investigate the finite sample properties of the estimators. The methods are illustrated by analyzing a real-world dataset from a study of infectious disease epidemiology.