Prediction of the supportive vaccine type of the COVID-19 for public health

Abstract

The novel corona virus SARS-Cov-2 caused the COVID-19 pandemic and mostly deteriorated the respiratory system. This paper aims to predict the supportive vaccine type (RNA, Non-replicating viral vector, DNA, Inactivated, Protein subunit) of COVID-19 for the human being by the rough set's novel process. The Rough set is an approach to identify patterns in uncertainty. The vaccine dataset, vaccine name, number of tested cases, age, randomize, and vaccine types of COVID-19 have been taken to overcome this disaster. By the rough set method, the supportive vaccine pattern is predicted, and it is observed that the vaccine based on RNA is highly supported to the human beings compared to the others. Extensive tests explained the Pfizer vaccine (RNA) is 95% effective, Moderna (mRNA) is 94.1%, while the Oxford/AstraZeneca (Non-replicating) one is 62%. It shows that the efficiency obtained by the rough set is accurate. A data-intensive computer-based analysis is given for the medical system. Furthermore, we present an extended record of sources that will support the scientific bioinformatics society to attain various sorts of a database linked to SARS-CoV-2 and advances to associate with COVID-19 treatment.