  During the ongoing COVID-19 pandemic, mathematical models of epidemic
spreading have emerged as powerful tools to produce valuable predictions of the
evolution of the pandemic, helping public health authorities decide which
intervention policies should be implemented. The study of these models --
grounded in the systems theory and often analyzed using control-theoretic tools
-- is an extremely important research area for many researchers from different
fields, including epidemiology, engineering, physics, mathematics, computer
science, sociology, economics, and management. In this survey, we review the
history and present the state of the art in the modeling, analysis, and control
of epidemic dynamics. We discuss different approaches to epidemic modeling,
either deterministic or stochastic, ranging from the first implementations of
scalar systems of differential equations to describing the epidemic spreading
at the population level, and to more recent models on dynamic networks, which
capture the spatial spread and the time-varying nature of human interactions.
