Infectious disease has a long history with our human beings. These diseases have been well known from the common flu in Spring to the most recent one such as the COVID-19 etc. People always want to find a cure such as vaccine to them. However, as the diseases’ viruses have thousands or millions of mutation in their DNA or RNA, it is therefore difficult to predict the next popular species that may be spread among people in the coming year. Mathematically, the combinatoric plus the optimization techniques may be the most feasible way to solve the problem. In the present thesis, this author suggests an innovate comparative research method that includes research group (HKLam regression) and the control group (other regressions) to the infected virus such as COVID-19 or common flu etc. The aforementioned method is used to determine these virus(es)’s convergence (/or) divergence mutations such that we may further genetic edit them to obtain several feasible vaccine candidates for further investigation in human beings etc. In other words, we are trying to artificially developed a tailor made “cow-pox” for these infected viruses and hence may be save millions of our lives.
Keywords: COVID-19, Convergence, common flu, divergence infective virus, innovate comparative research method
