## 2 Results
### Information Entropy of SARS-CoV-2 Genomes  
In order to test this assumption, we needed to examine the time evolution of the IE of a genome that undergoes frequent mutations in a short period of time. A system that perfectly fits this requirement is a virus genome, and we examined the RNA sequence of the novel SARS-CoV-2 virus, which emerged in December 2019, resulting in the current COVID-19 pandemic. The reference RNA sequence of SARS-CoV-2, representing a sample of the virus collected early in the pandemic in Wuhan, China, in December 2019 (MN908947) [7], has 29,903 nucleotides, so N = 29,903. For this reference sequence, we computed the Shannon IE using relation (1) and previously developed software, GENIES [8; 9], designed to study the genetic mutations using [PERSON]'s IE [6]. The value obtained represents the reference Shannon IE at time zero before any mutation occurs. Using the NCBI virus database, we searched and extracted a number of SARS-CoV-2 variants sequenced at various locations around the globe at different times, starting from January 2020 to October 2021. Our search was centered on complete genome sequences containing the same number of nucleotides as the reference sequence. We also carefully selected variants that displayed an incremental number of SNP mutations, and we computed the Shannon IE corresponding to each variant. The full data set, including genome data references/links, collection times, number of SNP mutations, and the Shannon IE value of each genome are shown in Table 1.  
Figure 1 shows the computed IE of each SARS-CoV-2 variant as a function of its number of SNP mutations. Remarkably, the data indicate that the Shannon IE of the RNA  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Genome** & **Ref.** & **SNPs** & **Collection Time** & **Location** & **Shannon IE** \\ \hline MN908947 & [7] & 0 & December 2019 & China & 1.9570243 \\ \hline LC542809 & [10] & 4 & March 2020 & Japan & 1.9569197 \\ \hline MT956916 & [11] & 6 & May 2020 & Spain & 1.9569468 \\ \hline MT956915 & [12] & 7 & May 2020 & Spain & 1.9569230 \\ \hline MW466798 & [13] & 9 & July 2020 & South Korea & 1.9569327 \\ \hline OM098415 & [14] & 15 & May 2021 & China & 1.9568149 \\ \hline OM098426 & [15] & 16 & May 2021 & China & 1.9568117 \\ \hline MW679504 & [16] & 18 & February 2021 & USA & 1.9568451 \\ \hline MW294011 & [17] & 19 & October 2020 & Ecuador & 1.9567058 \\ \hline OK246811.1 & [18] & 21 & March 2021 & USA/PA & 1.9568012 \\ \hline OK246805 & [19] & 22 & March 2021 & USA/PA & 1.9567543 \\ \hline MW679505 & [20] & 25 & February 2021 & USA & 1.9566630 \\ \hline MW735975 & [21] & 26 & February 2021 & USA/OH & 1.9565714 \\ \hline MW695383 & [22] & 27 & December 2020 & USA/VA & 1.9865744 \\ \hline OK546282.1 & [23] & 32 & April 2021 & USA/NE & 1.9565675 \\ \hline OK104651.1 & [24] & 40 & August 2021 & Egypt & 1.9564591 \\ \hline OL351371.1 & [25] & 49 & October 2021 & Egypt & 1.9562614 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tabulated results of the analysis performed on selected SARS-CoV-2 variants sequenced at various locations around the globe, over a period of 22 months.  
genomes decreases linearly with their corresponding number of mutations. The larger the number of mutations, the lower the corresponding Shannon IE value.  
All analyzed variants displayed the universal behavior of lowering the IE value relative to that of the original genome. The data in Figure 1 were selected to highlight this observation. Since the general consensus is that genetic mutations are random processes, one should not detect any correlation between the evolution of the genetic mutations and any other parameter.