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• W2526589030 abstract "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." @default.
• W2526589030 created "2016-10-07" @default.
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• W2526589030 date "2016-09-27" @default.
• W2526589030 modified "2023-09-23" @default.
• W2526589030 title "Data-driven identification of potential Zika virus vectors" @default.
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• W2526589030 doi "https://doi.org/10.1101/077966" @default.
• W2526589030 hasPublicationYear "2016" @default.
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