Predictive Modeling of COVID-19 Outbreaks Using Machine Learning Algorithms

Abstract

The COVID-19 pandemic revealed the necessity of effective forecasting and management methods for controlling the transmission and effects of infectious diseases. Machine learning (ML) techniques have emerged as a promising tool to predict the outbreak, spread, and severity of coronavirus infections. This paper the use of ML techniques to predict the course of the COVID-19 pandemic, focusing on epidemic spread, case severity, and mortality rates. Time series analysis, regression models, and compartmental models like SIR (Susceptible-Infected-Recovered) are employed to forecast the number of infections and hospitalizations. Additionally, classification algorithms are utilized to predict patient outcomes based on demographic and medical factors. The study also examines the potential of NLP (Natural Language Processing) for analyzing public sentiment and social behaviors that may impact the virus's transmission. Lastly, optimization and reinforcement learning models are considered for efficient resource allocation and vaccine distribution strategies. Despite the challenges posed by incomplete or inconsistent data and the evolving nature of the virus, ML models offer valuable insights to inform public health policies, enhance early warning system and optimize health care responses.