Study of Certain Corona Family Related Viruses based on Percentage Nucleotide Concentration and Golden Ratios and a Novel Sonic Attack Technique to Deactivate all Mutating Viruses

A set of 130 complete genome data of Corona type viruses are analyzed and classified based on their Percentge Concentrations of Nucleotides and Golden Ratios. The purpose of this study is to extract finer and hidden details about the evolutionary nature of Corona type viruses. It was found that all Corona type viruses evolve by random mutation but with their basic structural and fuctional genetic property maintained in the system biological sense. This has already been verified by extracting common digital spatial and spectral patterns during a study on “Pairwise Spatial Correlation of SARS-Corona Viruses” and on “Pairwise Spectral Correlation of SARS-Corona Viruses”. The scope of this paper is limited to the study of a set of 130 virus metadata in terms of certain quantificational measures such as (i) Golden Ratios (GR) and (ii) Percentage Concentrations of Nucleotides (pA, pT, pG and pC), and classification of those viruses based on these measures. Despite many drugs, vaccines and immunity boosters that move around the world with the idea of bringing down the pandemic state of affairs, the unpredictable ramification due to structural and genetic functional variations of Corona type viruses causes a serious concern, more specifically, a doubt/fear whether God has created these viruses or Man has created them. However, one should continue doing research in developing various diagnostic tools and therapeutic measures. Dismembering virus structures with ‘sonic’ frequencies that resonate with the natural frequencies of RNA forms and dismember them is one way of looking into solving this problem. This paper advocates a novel technique of generating pure tones as bursts and explores the possibilities of breaking the virus RNA sequences that are present inside the body or in the environment.

The authors declare no conflict of interest.

Not applicable

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.