Machine learning in front of statistical methods for prediction spread SARS-CoV-2 in Colombia

TL;DR

This study compares traditional statistical models and machine learning techniques for predicting COVID-19 spread in Colombia, analyzing their accuracy and proposing prevention scenarios.

Contribution

It introduces the application of Polynomial Regression alongside SEIR and Logistic Regression models for COVID-19 prediction in Colombia.

Findings

Polynomial Regression showed lower propagation error.

Optimal models reduced statistical biases.

Prevention scenarios provided insights into disease control.

Abstract

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.