Volume 2, 2019
Statistical Inference in Copula Models and Markov Processes, Case Studies and Insights
|Number of page(s)||8|
|Section||Mathematics - Applied Mathematics|
|Published online||03 July 2019|
Classification of autochthonous dengue virus type 1 strains circulating in Japan in 2014
Department of Mathematics, Federal University of Technology, Av. Monteiro Lobato, s/n – Km 04, Campus Ponta Grossa, Ponta Grossa, CEP 84016-210 Paraná, Brazil
2 Department of Statistics, University of Campinas, Sergio Buarque de Holanda, 651, Campinas, CEP 13083-859 São Paulo, Brazil
* Corresponding author: email@example.com
Accepted: 13 May 2019
In this paper, we classify by representativeness the elements of a set of complete genomic sequences of Dengue Virus Type 1 (DENV-1), corresponding to the outbreak in Japan during 2014. The set is coming from four regions: Chiba, Hyogo, Shizuoka and Tokyo. We consider this set as composed of independent samples coming from Markovian processes of finite order and finite alphabet. Under the assumption of the existence of a law that prevails in at least 50% of the samples of the set, we identify the sequences governed by the predominant law (see [1, 2]). The rule of classification is based on a local metric between samples, which tends to zero when we compare sequences of identical law and tends to infinity when comparing sequences with different laws. We found that the order of representativeness, from highest to lowest and according to the origin of the sequences is: Tokyo, Chiba, Hyogo, and Shizuoka. When comparing the Japanese sequences with their contemporaries from Asia, we find that the less representative sequence (from Shizuoka) is positioned in groups considerably far away from that which includes the sequences from the other regions in Japan, this offers evidence to suppose that the outbreak in Japan could be produced by more than one type of DENV-1.
Key words: Classification of stochastic samples / Metric between stochastic processes
© M.T.A. Cordeiro et al., Published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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