Document Type
Article
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.
Digital Object Identifier (DOI)
Publication Info
Published in Computational and Systems Biology, Volume 6, 2017.
APA Citation
Evans, M. V., Dallas, T. A., Han, B. A., Murdock, C. C., & Drake, J. M. (2017). Data-driven identification of potential Zika virus vectors. eLife, 6. https://doi.org/10.7554/elife.22053
Rights
©Copyright Evans et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.