Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic

Area of Science:

• Computer Science
• Medical Informatics
• Public Health

Background:

• The COVID-19 pandemic has severely impacted global healthcare systems.
• Modern technologies, including machine learning (ML), are crucial for combating the disease.
• There is a growing need to understand ML's role in managing the pandemic.

Purpose of the Study:

• To highlight the significant role of machine learning approaches in addressing the COVID-19 pandemic.
• To analyze and describe the latest literature on ML applications for COVID-19.
• To identify key challenges and future trends in ML for pandemic response.

Main Methods:

• A comprehensive literature search was conducted across major scientific databases (IEEE Xplore, PubMed, Google Scholar, Research Gate, Scopus).
• Relevant studies on machine learning applications for COVID-19 were systematically analyzed.
• Identified applications were categorized and described based on their contribution to pandemic management.

Main Results:

• Four distinct applications of ML methods in combating COVID-19 were identified.
• ML aids physicians in decision-making and helps policymakers in strategic planning.
• ML tools contribute to identifying potentially infected individuals and tracking disease spread.

Conclusions:

• Machine learning serves as a powerful tool for analyzing, screening, tracking, and forecasting COVID-19.
• ML techniques are significantly supporting the healthcare system in managing the pandemic.
• Continued research and implementation of ML are recommended for effective pandemic preparedness and response.