In this paper, we identify the similarities and discrepancies of the new variant of SARS-COV 2 virus: B.1.1.529, known as Omicron, first reported to WHO from South Africa, and the previous four variants of concern of SARS-CoV 2 virus: B.1.1.7, B.1.351, B.1.617.2 and P.1, first reported to WHO from UK, South Africa, India, and Brazil, respectively. The comparison is made using sequences in FASTA format coming from the GISAID base. [1] investigates the similarities/differences between sequences coming from B.1.1.7, B.1.351, B.1.617.2, and P.1. The virus' sequences were considered as samples of finite memory Markov processes acting in A={a,c,g,t} (genomic alphabet), and due to the heterogeneity of the sequences belonging to the same variant, in [1] are identified the most representative sequences for each variant, through the notions introduced in [2] and [3]. In this paper, we search for similarities/discrepancies between the Omicron variant and the previous variants of concern. To answer this question, we compare the Multiple Partition Markov Model ([4]) found in [1] for the four previous variants using the most representative sequences with the model obtained for the Omicron variant. Taking into consideration that in [1] was found that the four previous variants differ in a specific part of the state space. In other words, [1] indicates the stochastic reasons for the discrepancies between the previous four variants of concern. Then now, we use all these findings to characterize the Omicron variant.

[1] García, JE., González-López, V.A., Tasca, G.H. Multiple Partition Markov Model for B.1.1.7, B.1.351, B.1.617.2, and P.1 Variants of SARS-CoV 2 Virus (pre-print)

[2] Fernández M, García JE, Gholizadeh R, González-López VA (2020) Sample selection procedure in daily trading volume processes. Mathematical Methods in the Applied Sciences, 43(13), 7537-7549. https://doi.org/10.1002/mma.5705

[3] García JE, Gholizadeh R, González-López VA (2018) A BIC-based consistent metric between Markovian processes. Applied Stochastic Models in Business and Industry, 34(6), 868-878. https://doi.org/10.1002/asmb.2346

[4] Cordeiro MTA, García JE, González‐López VA, Mercado Londoño SL (2020) Partition Markov model for multiple processes. Mathematical Methods in the Applied Sciences, 43(13), 7677-7691. https://doi.org/10.1002/mma.6079