Review of Big Data Analytics and Machine Learning Models for Contract Tracking and Detecting of COVID-19 Pandemic Cases

The pandemic COVID-19 is an infectious disease discovered first in China on December 19 and spread very fast over countries in the world where millions of people are infected. This is a deadly virus, which affects each facet of everyday lives. The novel leading Big Data applications have provoked in several areas are utilized in outbreak prediction, tracking of virus spread and prevent by the diagnosis of COVID-19. In addition, the techniques of Machine Learning (ML) have been employed commonly for various domains, which are already a huge market to ML-aided diagnostic systems in COVID-19 monitoring, predicting of virus spread and diagnosis or treatment of COVID-19 to determine the potential cure. Hence, this research focused on maintaining its significance in leading to the outbreak of COVID-19 and in mitigating the serious possessions of COVID-19. Initially, this paper has presented an outline of COVID-19 followed by the application of big data and ML towards fighting against COVID-19. Subsequently, it highlights the problems and challenges related to advance d solutions which help in finding out the advantage and disadvantages of recent techniques for controlling an efficient contract tracking and generating an outbreak of the COVID-19 situation. In this paper correlation matrix tool is proposed to identify the disease with minimal features. Only the test is being used to evaluate the condition because symptoms are many and inaccurate. The detection of disease is much enhanced by combining a machine learning predictive model with a correlation matrix tool. A correlation matrix is a technique that is used in the analysation of certain attributes. The correlation value between the feature values is determined, which improves the accuracy of the output.