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Updated: Jan 11, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
The evolutionary battle: data-driven analysis of SARS-CoV-2 virulence
Kaiqian Yin1, Yifei Wang1, Xinzhu Meng1
1College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, PR China.
None:
Combining perspectives of infectious disease dynamics and evolutionary adaptive dynamics, we reveal the evolving patterns of SARS-CoV-2 virulence with the use of data-driven analysis from 22 January 2020 to 21 October 2022 and focus primarily on the Wild-type virus, D614G, Alpha, Delta, Omicron and XBB. We discuss conditions between transmission rate () and the sum of natural death rate (), mortality rate due to disease (), and recovery rate () for virus substitution and coexistence. (i) When mutant y replaces the resident virus x; (ii) when the mutant coexists with the resident virus and they are homogeneous virus. Further we adopt a segmented classification method to find that the transmission rate is gradually increasing and the mortality rate due to disease shows a significant increase at first, with the increasing virulence of the virus, there is a trend of gradual decline in the later period of the epidemic. Additionally, it turns out that reducing personnel mobility is conducive to the retention of virulent viruses and the symptoms of the patients tend to abate. To investigate the impact of dynamic changes in infectious disease systems links to SARS-CoV-2 on the evolution of its virulence over time scales, the coupling nesting of evolutionary dynamics and transmission dynamics is carried out. It suggests the results of the adaptive evolution of the virus have been verified and the increased speed of evolutionary adaptation shorts the time for viruses to peak.
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