title: Data-driven identification of potential Zika virus vectors.
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, includingCulex quinquefasciatusandCx. 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.
authors.affiliation: Odum School of Ecology, University of Georgia, Athens, United States.|Center for the Ecology of Infectious Diseases, University of Georgia, Athens, United States.
authors.forename: Michelle V
authors.identifier: 0000-0002-5628-0502
authors.initials: MV
authors.lastname: Evans
authors.affiliation: Odum School of Ecology, University of Georgia, Athens, United States.|Department of Environmental Science and Policy, University of California-Davis, Davis, United States.
authors.forename: Tad A
authors.identifier: 0000-0003-3328-9958
authors.initials: TA
authors.lastname: Dallas
authors.affiliation: Cary Institute of Ecosystem Studies, Millbrook, United States.
authors.forename: Barbara A
authors.identifier: 0000-0002-9948-3078
authors.initials: BA
authors.lastname: Han
authors.affiliation: Odum School of Ecology, University of Georgia, Athens, United States.|Center for the Ecology of Infectious Diseases, University of Georgia, Athens, United States.|Department of Infectious Disease, University of Georgia, Athens, United States.|Center for Tropical Emerging Global Diseases, University of Georgia, Athens, United States.|Center for Vaccines and Immunology, University of Georgia, Athens, United States.|River Basin Center, University of Georgia, Athens, United States.
authors.forename: Courtney C
authors.initials: CC
authors.lastname: Murdock
authors.affiliation: Odum School of Ecology, University of Georgia, Athens, United States.|Center for the Ecology of Infectious Diseases, University of Georgia, Athens, United States.|River Basin Center, University of Georgia, Athens, United States.
authors.forename: John M
authors.identifier: 0000-0003-4646-1235
authors.initials: JM
authors.lastname: Drake
mesh_terms: D000818:Animals; D018562:Disease Transmission, Infectious; D017753:Ecosystem; D015233:Models, Statistical; D000072138:Mosquito Vectors / Q000821:virology*; D014481:United States; D000071244:Zika Virus / Q000502:physiology*; D000071243:Zika Virus Infection / Q000635:transmission*
keywords: Zika virus; computational biology; ecology; machine learning; mosquito-borne disease; systems biology; virus