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ELife: Data-driven identification of potential Zika virus vectors

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  • ELife: Data-driven identification of potential Zika virus vectors

    Data-driven identification of potential Zika virus vectors

    • Michelle V Evans
    • Tad A Dallas
    • Barbara A Han
    • Courtney C Murdock
    • John M Drake





    University of Georgia, United States; Cary Institute of Ecosystem Studies, United States



    DOI: http://dx.doi.org/10.7554/eLife.22053

    Published February 28, 2017
    Cite as eLife 2017;10.7554/eLife.22053



    Abstract

    Zika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model predicts that thirty-five species may be able to transmit the virus, seven of which are found in the continental United States, including Culex quinquefasciatus and Cx. pipiens. We suggest that empirical studies prioritize these species to confirm predictions of vector competence, enabling the correct identification of populations at risk for transmission within the United States.

    Data-driven methods predict over 35 mosquitoes are potential vectors of Zika virus, suggesting a larger geographic area and a greater human population is at risk of infection.










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