Data Analytics and Assessment of the Drugs for COVID-19

The new Coronavirus, (A.K.A. COVID-19) created a global crisis that impacted the world not only in health perspectives but in most aspects of life. Many teams across the globe from difference, science, and health domains continue as of the time of writing this paper to find proper treatments for COVID-19. In this scope, our effort in this paper is to follow a data-analytic approach and summarize candidate COVID-19 treatments, their mechanism of action and side effects, and research progress in this regard. In addition, we provide a mathematical model to measure the transmission of the disease. We also conducted a comparison analysis between those candidate treatments. Our analysis indicated that among the most popular terms in research articles and social networks, there is a mixture between the different drug treatments, versus their classifications and commercial names. While research publications may reflect states, public, and private sector efforts on the different alternatives of treatments, popularity in social networks are less structured and may combine a mixture of treatments, their categories, and also manufacturing and marketing names. Many of COVID-19 treatments evolve from earlier Coronavirus treatments.