Comparative Analysis of SEIR/SIR/SIS Models and Machine Learning in the Prediction of COVID-19, Honduras (#2471)

Authors

Martinez Claros, Brennedy

Ventura Martinez, Celene

Osorto, Henry

Abstract

This study aims to compare traditional mathematical models used to analyze the spread of infectious diseases with machine learning methods, using COVID-19 as a case study. Classical models such as SIR, SEIR, and SIS help describe the progression of an epidemic; however, they typically rely on fixed parameters and struggle to adapt to real-time changes. In contrast, machine learning models including Polynomial Regression, SVM, and Random Forest are capable of processing large datasets, detecting more complex patterns, and adjusting their predictions as new information becomes available. For this analysis, historical data from the World Health Organization (WHO), adapted to national records, were used, and the performance of each model was evaluated using metrics such as RMSE and R². Overall, the results showed that machine learning models provided a better fit and greater adaptability, making them a valuable option for anticipating and controlling future epidemic outbreaks.