A Review Study on Machine Learning Approaches on Coronavirus Big Data

Abstract

Information has the ability for protecting against unexpected
events and controlling crises like CoronaVirus Disease COVID-19
pandemic. Because this pandemic has spread so quickly worldwide, only
technology-driven management of data could give reliable information in
order to help handle the situation. The goal of this research is to look at the
potential of the technologies that are related to big data to control and
regulate the transmission of COVID-19. To collect the important aspects,
a systematic review was conducted using Preferred Reporting Items for
Systematic Reviews and Meta-Analyses PRISMA criteria. The thirtytwo most relevant documents for the qualitative analyses have been
indicated in the present work. This research also identifies 10 potential
data sources and 8 essential big data applications for studying virus
infection trends, virus associations, transmission patterns, and differences
of genetic modifications. Also, it looks at some of the drawbacks of big
data, such as privacy concerns, unethical data use, and data exploitation.
The research's results will offer fresh information to administrators and
policymakers, allowing them to establish data-driven strategies for
addressing and managing the COVID-19 epidemic.