Stochastic Comparison Between the Original SARS-CoV 2 Genetic Structure and SARS-CoV 2 - P.1 Variant

Jesús E. García (), V. A. González-López () and G. H. Tasca
Additional contact information
Jesús E. García: University of Campinas, Campinas, Department of Statistics
V. A. González-López: University of Campinas, Campinas, Department of Statistics
G. H. Tasca: University of Campinas, Campinas, Department of Statistics

Chapter Chapter 5 in Quantitative Methods in Demography, 2022, pp 63-75 from Springer

Abstract: Abstract This article is intended to explore the behavior of complete genetic sequences of SARS-CoV 2 - P.1 variant, in comparison with the original sequence of SARS-CoV 2, accession number MN908947 (see López and Tasca (4open, 3:13, 2020) and Wu et al. (Nature 579:265–269, 2020)). The sequences are taken in FASTA format, they will be considered as samples of stochastic processes. Based on this interpretation, stochastic tools derived from Partition Markov Models Garc ı́a and González-López (Entropy 19(4):160, 2017) are applied, specifically from the G-model, introduced in Garc ı́a et al. (4open 3:13, 2020) , to obtain the representation of the behavior of each sequence. From these models, it is confirmed that the structure of the P.1 variant of the virus shows similar traits to the original one. Moreover, by means of a BIC-based metric and the G-model, it is possible to determine that the original sequence of SARS-CoV 2 (MN908947) and the set of sequences of SARS-CoV 2 - P.1 variant differ in certain transition probabilities, which allows identifying exactly the state that produces the discrepancy. We also represent this discrepancy graphically.

Keywords: Partition Markov models; Bayesian information criterion; Metric between processes (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-3-030-93005-9_5

Ordering information: This item can be ordered from
http://www.springer.com/9783030930059

DOI: 10.1007/978-3-030-93005-9_5

Access Statistics for this chapter

More chapters in The Springer Series on Demographic Methods and Population Analysis from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().