  Disease transmission is studied through disciplines like epidemiology,
applied mathematics, and statistics. Mathematical simulation models for
transmission have implications in solving public and personal health
challenges. The SIR model uses a compartmental approach including dynamic and
nonlinear behavior of transmission through three factors: susceptible,
infected, and removed (recovered and deceased) individuals. Using the Lambert W
Function, we propose a framework to study solutions of the SIR model. This
demonstrates the applications of COVID-19 transmission data to model the spread
of a real-world disease. Different models of disease including the SIR, SIRmp
and SEIRpqr model are compared with respect to their ability to predict disease
spread. Physical distancing impacts and personal protection equipment use are
discussed with relevance to the COVID-19 spread.

Populations and Evolution;Physics and Society