Unraveling the Genomic Mysteries of COVID-19: Using the Power of Phylogeny to Predict the Variants Before They Come Into Existence

There is a notable gap in the taxonomic research of COVID-19. This investigation aimed to address and fill this gap by evaluating the accuracy of different phylogenetic tree methods in displaying the evolutionary history of the virus SARS - CoV 2. This research was motivated by the potential to prevent the recurrent spread of the disease world wide and making the vaccine creation process easier by gaining the ability to predict future virus mutations through the use of phylogenetic analysis. This investigation determined which phylogenetic mechanism (Parsimony, Maximum Likelihood, Neighbor-Joining, and UPGMA) was most accurate in portraying the evolutionary history of COVID-19. The hypothesis that was inferred before beginning this investigation was that the likelihood method of phylogeny would display the evolutionary history of COVID-19 most accurately due its reputation as a phylogenetic method that conducts more thorough studies than other statistical approaches. This investigation was carried out by equally sampling COVID-19 DNA from around the globe and inputting them into software that produces different types of phylogenetic trees. The bootstrap values of the clades of each of the four types of trees produced were then observed to reach a conclusion as to which of them displayed COVID-19 most accurately. The hypothesis ended up being unsupported as it turned out that of the 4 methods tested, the neighbor-joining method proved to be the most accurate. This discovery had significant implications for future research and could be used by scientists to possibly predict new COVID-19 variants before they come into existence.