Prediction of COVID-19 Using Machine Learning

Abstract: The COVID-19 pandemic has sparked a global increase in study into understanding and predicting the disease's transmission, intensity, and effects. Machine learning (ML) has emerged as a significant tool in this quest, allowing for the analysis of large and complicated datasets to identify patterns and generate accurate predictions. This literature review synthesizes information from 10-15 peer-reviewed publications and review articles that investigate the use of machine learning algorithms in COVID-19 prediction. The algorithms used in the examined studies include Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and hybrid models. These models have been applied to many datasets, such as clinical records and imaging data. Epidemiological data are used to forecast infection rates, disease severity, hospitalization risk, and mortality. The abstract compares the performance of several models, emphasizing the advantages of ensemble and hybrid techniques for increasing prediction accuracy. Data quality, model interpretability, and generalizability are among the issues discussed. The analysis indicates that ML models, particularly those that combine several algorithms and data sources, have great potential for improving public health responses and decision-making during pandemics. Future research directions include the development of real-time predictive systems, integration with existing epidemiology models, and ethical considerations when applying machine learning in healthcare settings.