Predicting Abundances of Aedes mcintoshi, a primary Rift Valley fever virus mosquito vector
Predicting Abundances of Aedes mcintoshi, a primary Rift Valley fever virus mosquito vector
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DOI：
10.1371/journal.pone.0226617
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发表时间：
2019-12-17
期刊：
影响因子：
3.7
通讯作者：
Sang, Rosemary
中科院分区：
文献类型：
--
作者：
Campbell, Lindsay P.;Reuman, Daniel C.;Sang, Rosemary
裂谷热病毒（RVFV）是一种蚊媒人畜共患虫媒病毒，对整个非洲和阿拉伯半岛的牲畜和人类健康以及经济造成重要影响。气候和植被监测指导裂谷病毒预报模型和预警系统；然而，这些方法是按月预测的，需要在更精细的时间尺度上预测主要矢量的丰度。在肯尼亚，一个重要的主要裂谷热病毒媒介是麦金托什伊蚊。我们使用零膨胀负二项回归和多模型平均方法与地理参考Ae。mcintoshi对蚊子进行计数，并遥感气候和地形变量，以预测肯尼亚和索马里西部何时何地蚊子数量最多。这些数据支持了采样点500 m范围内的最小湿度指数丰度、采样前0 ~ 14天的累积降水量以及与采样前3周相似的地表温度升高值的积极影响。随着土壤中粘土含量的减少，蚊子结构零计数的可能性增加。2002年至2016年9月1日至1月25日期间对整个研究区的未采样地点进行的每周回顾性预测预测，在2006-2007年动物流行病期间，除肯尼亚的两个地区外，多个疫源地在裂谷热病毒暴发之前就出现了高丰度。此外，模式预测支持高Ae的可能性。麦金托什丰富的索马里，独立于肯尼亚。模型预测的丰度在2015-2016年期间很低，当时没有发生记录在案的疫情，尽管几个监测系统发出了警告。2018年裂谷热病毒暴发前的模型预测表明，沿索马里边境的肯尼亚瓦吉尔县的裂谷热病毒丰度升高，但裂谷热病毒活动发生在预测的高Ae焦点以西。mcintoshi丰度。
Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic arbovirus with important livestock and human health, and economic consequences across Africa and the Arabian Peninsula. Climate and vegetation monitoring guide RVFV forecasting models and early warning systems; however, these approaches make monthly predictions and a need exists to predict primary vector abundances at finer temporal scales. In Kenya, an important primary RVFV vector is the mosquito Aedes mcintoshi. We used a zero-inflated negative binomial regression and multimodel averaging approach with georeferenced Ae. mcintoshi mosquito counts and remotely sensed climate and topographic variables to predict where and when abundances would be high in Kenya and western Somalia. The data supported a positive effect on abundance of minimum wetness index values within 500 m of a sampling site, cumulative precipitation values 0 to 14 days prior to sampling, and elevated land surface temperature values similar to 3 weeks prior to sampling. The probability of structural zero counts of mosquitoes increased as percentage clay in the soil decreased. Weekly retrospective predictions for unsampled locations across the study area between 1 September and 25 January from 2002 to 2016 predicted high abundances prior to RVFV outbreaks in multiple foci during the 2006-2007 epizootic, except for two districts in Kenya. Additionally, model predictions supported the possibility of high Ae. mcintoshi abundances in Somalia, independent of Kenya. Model-predicted abundances were low during the 2015-2016 period when documented outbreaks did not occur, although several surveillance systems issued warnings. Model predictions prior to the 2018 RVFV outbreak indicated elevated abundances in Wajir County, Kenya, along the border with Somalia, but RVFV activity occurred west of the focus of predicted high Ae. mcintoshi abundances.