Download This PaperVirus Mutation Direction and a Possible Frequency-Time Model Based on Variants’ Genome Information and Global COVID-19 Epidemic Data
9 Pages Posted: 8 Dec 2022 Last revised: 14 Feb 2023
Date Written: November 7, 2022
Abstract
Objective: To study the mathematical relationship between the image of the prevalence of 2019-nCoV over time and the variation site of viral protein, and to explore the variation direction of COVID-19. Methods: 1. The nucleic acid sequencing results of 2019-nCoV mutants collected by NCBI as of May 13, 2022, were obtained from the National Biotechnology Information Center (NCBI) of the United States. The computer neural network learning technology and MSA multiple sequence alignment were used to establish a phylogenetic tree with Refseq from Wuhan as the "root" of the tree and compared with the real-time phylogenetic tree from Nextrain. The error was less than 2 ‰. Then, in CoVariants, we obtained the prevalence time images of six COVID-19 mutants that caused large-scale infection from 43 countries and regions. Then we selected Nsp6, Orf6, Orf7a and Orf3a and S protein as the independent variables of the parameters and proposed a function expression with parameters according to the Gaussian distribution, Wolfram Mathematica & Matlab & GeoGebra are used to obtain the parameter values under different frequency time images, and the linear method is used to solve the parameter values, and the Q value test method is used to prove that the results are meaningful. Results: The values of four parameters in the equation or their linear expressions with the number of heterotopic sites of specific proteins were obtained. Conclusion: 1. Molecular biological data determine the epidemic rate time image characteristics of the virus; 2. The virus generally develops from low infectivity to high infectivity, and the toxicity generally develops from high toxicity to low toxicity.
Note:
Funding Information: National Natural Science Foundation of China through the Chinesisch-Deutsches Zentrum für Wissenschaftsförderung. Award Number: C-0072. International Joint Research Project of the Institute of Medical Science, the University of Tokyo.
Conflict of Interests: None to declare.
Keywords: COVID-19, Public Health, Mathematics modeling
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