Data-driven identification of potential Zika virus vectors

  1. Michelle V Evans  Is a corresponding author
  2. Tad A Dallas
  3. Barbara A Han
  4. Courtney C Murdock
  5. John M Drake
  1. University of Georgia, United States
  2. Cary Institute of Ecosystem Studies, United States

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.

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Author details

  1. Michelle V Evans

    Odum School of Ecology, University of Georgia, Athens, United States
    For correspondence
    mvevans@uga.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5628-0502
  2. Tad A Dallas

    Odum School of Ecology, University of Georgia, Athens, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Barbara A Han

    Cary Institute of Ecosystem Studies, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9948-3078
  4. Courtney C Murdock

    Odum School of Ecology, University of Georgia, Athens, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. John M Drake

    Odum School of Ecology, University of Georgia, Athens, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4646-1235

Funding

National Science Foundation (DEB-1640780)

  • Courtney C Murdock

University of Georgia (Presidential Fellowship)

  • Michelle V Evans

National Institutes of Health (U01GM110744)

  • John M Drake

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Oliver Brady, London School of Hygiene & Tropical Medicine, United Kingdom

Version history

  1. Received: October 5, 2016
  2. Accepted: February 13, 2017
  3. Accepted Manuscript published: February 28, 2017 (version 1)
  4. Version of Record published: March 8, 2017 (version 2)

Copyright

© 2017, Evans et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Michelle V Evans
  2. Tad A Dallas
  3. Barbara A Han
  4. Courtney C Murdock
  5. John M Drake
(2017)
Data-driven identification of potential Zika virus vectors
eLife 6:e22053.
https://doi.org/10.7554/eLife.22053

Share this article

https://doi.org/10.7554/eLife.22053

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