Mixing patterns in a global influenza A virus network using whole genome comparisons

Adrienne E. Breland, Mehmet H. Gunes, Karen A. Schlauch, Frederick C. Harris

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Approximating 'real' disease transmission networks through genomic sequence comparisons among pathogenic isolates is increasingly feasible with the current growth in genomic sequence data. Here, we derive a network from over 4,200 globally distributed influenza A virus isolates based on alignment-free sequence comparisons. We then employ network mixing pattern analysis to examine transmission probabilities between isolates from different global regions, host types, subtypes and collection years. While we can not use our results to describe the complete global network of influenza A virus, we present a novel analytical process. In addition, we describe some of the characteristics of this subset of currently available data. Most notable results are the high levels of inter regional links and the important role that avian species seem to play in non human global transmission.

    Original languageEnglish
    Title of host publication2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010
    Pages190-197
    Number of pages8
    DOIs
    StatePublished - 2010
    Event2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010 - Montreal, QC, Canada
    Duration: 2 May 20105 May 2010

    Publication series

    Name2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010

    Conference

    Conference2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010
    Country/TerritoryCanada
    CityMontreal, QC
    Period2/05/105/05/10

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