  An analytical study of the disease COVID-19 in Colombia was carried out using
mathematical models such as Susceptible-Exposed-Infectious-Removed (SEIR),
Logistic Regression (LR), and a machine learning method called Polynomial
Regression Method. Previous analysis has been performed on the daily number of
cases, deaths, infected people, and people who were exposed to the virus, all
of them in a timeline of 550 days. Moreover, it has made the fitting of
infection spread detailing the most efficient and optimal methods with lower
propagation error and the presence of statistical biases. Finally, four
different prevention scenarios were proposed to evaluate the ratio of each one
of the parameters related to the disease.
