  In this research, we study the propagation patterns of epidemic diseases such
as the COVID-19 coronavirus, from a mathematical modeling perspective. The
study is based on an extensions of the well-known
susceptible-infected-recovered (SIR) family of compartmental models. It is
shown how social measures such as distancing, regional lockdowns, quarantine
and global public health vigilance, influence the model parameters, which can
eventually change the mortality rates and active contaminated cases over time,
in the real world. As with all mathematical models, the predictive ability of
the model is limited by the accuracy of the available data and to the so-called
\textit{level of abstraction} used for modeling the problem. In order to
provide the broader audience of researchers a better understanding of spreading
patterns of epidemic diseases, a short introduction on biological systems
modeling is also presented and the Matlab source codes for the simulations are
provided online.
