Infectious diseases are inherently dynamic; that is, they are continually changing in time and space.  While summary characteristics (e.g. prevalence) may remain stationary over time, this arises from the balance of rates that govern the continual processes of transmission, infection, and recovery (or removal).  More commonly, these dynamic processes generate fluctuations in time and space, and non-linear phenomena that imply that the future state of the system cannot necessarily be predicted with a simple extrapolation from the current state.  These non-linear dynamics present some challenges in terms of estimation and prediction, but may also take advantage of these dynamics to design effective strategies for control.  Mathematical and computational models have been critical tools in exploring the interaction of dynamics and control.  Here we review various applications of disease dynamics to the monitoring and evaluation of infectious disease management. We first discuss the general applications of disease dynamics and then present lessons from specific classes of dynamic phenomena.