Data-driven identification of potential Zika virus vectors
- University of Georgia, United States
- 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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Data and Code to reproduce Evans et al 2016Publicly available at figshare.
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Author details
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.
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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Data-driven identification of potential Zika virus vectors
eLife 6:e22053.
https://doi.org/10.7554/eLife.22053
