Machine Learning in the Battle Against COVID-19: Predictive Models and Future Directions

This paper explores the impactful role of machine learning (ML) in navigating the challenges posed by the COVID-19 pandemic, highlighting its utility in case prediction, virus transmission analysis, and diagnostic improvement. By reviewing supervised, unsupervised, and deep learning models, it demonstrates how ML processes vast datasets to deliver insights critical for enhancing public health responses. Amidst challenges of data quality, the study underscores the necessity for sophisticated data preprocessing. It briefly anticipates future directions, pointing towards the integration of artificial intelligence (AI) with Internet of Things (IoT) technologies for real-time pandemic monitoring, and emphasizes the need for continued research and global cooperation to maximize ML's benefits in future pandemic preparedness.