Data-driven identification of potential Zika virus vectors
Data-driven identification of potential Zika virus vectors
复制标题
DOI：
10.7554/elife.22053
复制
发表时间：
2017-02-28
期刊：
影响因子：
7.7
通讯作者：
Drake, John M.
中科院分区：
文献类型：
--
作者：
Evans, Michelle V.;Dallas, Tad A.;Drake, John M.
寨卡病毒是一种新出现的病毒，其快速传播引起了极大的公共卫生问题。关于传播的知识仍然不完整，特别是关于尚未引入的地理区域的潜在传播。为了识别寨卡病毒的未知载体，我们开发了一个数据驱动的模型，通过载体-病毒特征组合将载体物种和寨卡病毒联系起来，这些特征组合在连接黄病毒及其蚊子载体的生态网络中赋予了关联的倾向。我们的模型预测，35种物种可能能够传播该病毒，其中7种在美国大陆发现，包括致倦库蚊和Cx。pipiens。我们建议，实证研究优先考虑这些物种，以确认预测的载体能力，使正确识别的人群在美国境内的传播风险。
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.