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Networks of information token recurrences derived from genomic sequences may reveal hidden patterns in epidemic outbreaks: A case study of the 2019-nCoV coronavirus.

journal contribution
posted on 21.06.2020 by Markus Luczak-Roesch
Profiling the genetic evolution and dynamic spreading of viruses is a crucial task 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.

History

Preferred citation

(2020). Networks of information token recurrences derived from genomic sequences may reveal hidden patterns in epidemic outbreaks: A case study of the 2019-nCoV coronavirus. medRxiv, 2020.02.07.20021139-. https://doi.org/10.1101/2020.02.07.20021139

Journal title

medRxiv

Publication date

11/02/2020

Pagination

2020.02.07.20021139

Publisher

Cold Spring Harbor Laboratory

Publication status

Published

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