《MedRixv,2月11日,Networks of information token recurrences derived from genomic sequences may reveal hidden patterns in epidemic outbreaks: A case study of the 2019-nCoV coronavirus.》

  • 来源专题:COVID-19科研动态监测
  • 编译者: xuwenwhlib
  • 发布时间:2020-02-12
  • Networks of information token recurrences derived from genomic sequences may reveal hidden patterns in epidemic outbreaks: A case study of the 2019-nCoV coronavirus.

    Markus Luczak-Roesch

    doi: https://doi.org/10.1101/2020.02.07.20021139

    Abstract

    Profiling the genetic evolution and dynamic spreading of viruses is a crucial when responding to epidemic outbreaks. We aim to devise novel ways to model, visualise and analyse the temporal dynamics of epidemic outbreaks in order to help researchers and other people involved in crisis response to make well-informed and targeted decisions about from which geographical locations and time periods more genetic samples may be required to fully understand the outbreak. Our approach relies on the application of Transcendental Information Cascades to a set of temporally ordered nucleotide sequences and we apply it to real-world data that was collected during the currently ongoing outbreak of the novel 2019-nCoV coronavirus. We assess information-theoretic and network-theoretic measures that characterise the resulting complex network and suggest touching points and temporal pathways that are of interest for deeper investigation by geneticists and epidemiologists.

    *注,本文为预印本论文手稿,是未经同行评审的初步报告,其观点仅供科研同行交流,并不是结论性内容,请使用者谨慎使用.

  • 原文来源:https://www.medrxiv.org/content/10.1101/2020.02.07.20021139v1
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