A HABITAT MODEL FOR DISEASE VECTOR AEDES AEGYPTI IN THE TAMPA BAY AREA, FLORIDA
A HABITAT MODEL FOR DISEASE VECTOR AEDES AEGYPTI IN THE TAMPA BAY AREA, FLORIDA
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DOI：
10.2987/22-7109
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发表时间：
2023-06-01
影响因子：
1
通讯作者：
Carney, Ryan M.
中科院分区：
文献类型：
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作者：
Uelmen Jr, Johnny A.;Mapes, Connor D.;Carney, Ryan M.
在美国本土，佛罗里达州拥有独特的热带和亚热带气候、蚊媒的丰富性和多样性以及人类旅行的高频率。这些因素促使该州成为全国外来蚊媒疾病的零地带，埃及伊蚊传播的病毒在当地传播就证明了这一点，包括 2022 年爆发的登革热和 2016 年爆发的寨卡病毒。由于治疗选择有限，综合病媒管理是减轻这些虫媒病毒的关键部分。了解感兴趣的蚊子种群存在的时间和地点对于监测和控制工作至关重要，并且不同地理尺度的栖息地预测通常依赖于生态位模型。然而，大多数这些模型通常是与学术机构合作创建的，需要资源，否则这些资源可能过于耗时或难以有效复制和使用蚊虫控制项目。这些资源可能包括密集的计算要求、不定期获得的数据的高时空分辨率和/或统计分析的专业知识。因此，我们的研究旨在与蚊子控制机构合作，生成可操作的蚊子丰度模型。鉴于佛罗里达州蚊媒疾病传播的威胁日益增加，我们的分析方法针对最近的伊蚊。坦帕湾地区埃及伊蚊数量丰富。我们研究的解释变量：1) 是公开可用的，2) 使用时几乎不需要预处理，3) 是与 Ae 相关的已知因素。埃及伊蚊生态。在我们的 4 个最终模型中，没有一个模型需要评估的 36 个预测变量中的 5 个以上 (13.9%)。与之前的文献类似，最强的预测因子始终是 3 周和 4 周的温度和降水滞后，紧随其后的是 2 个环境预测因子中的一个：土地利用/土地覆盖或标准化差异植被指数。令人惊讶的是，我们的 4 个最终模型中有 3 个包含一个或多个社会经济或人口预测因素。一般来说，陷阱收集和/或公民科学观察的样本量越大，模型预测和验证的信心就会越高。然而，考虑到各个司法管辖区陷阱收集的差异，单个县模型而不是多县企业集团模型可能会产生更强的模型拟合度。最终，我们希望我们的评估结果能够更准确、更精确地监测和控制伊蚊。佛罗里达州及其他地区的埃及伊蚊。
Within the contiguous USA, Florida is unique in having tropical and subtropical climates, a great abundance and diversity of mosquito vectors, and high rates of human travel. These factors contribute to the state being the national ground zero for exotic mosquito-borne diseases, as evidenced by local transmission of viruses spread by Aedes aegypti, including outbreaks of dengue in 2022 and Zika in 2016. Because of limited treatment options, integrated vector management is a key part of mitigating these arboviruses. Practical knowledge of when and where mosquito populations of interest exist is critical for surveillance and control efforts, and habitat predictions at various geographic scales typically rely on ecological niche modeling. However, most of these models, usually created in partnership with academic institutions, demand resources that otherwise may be too time-demanding or difficult for mosquito control programs to replicate and use effectively. Such resources may include intensive computational requirements, high spatiotemporal resolutions of data not regularly available, and/or expert knowledge of statistical analysis. Therefore, our study aims to partner with mosquito control agencies in generating operationally useful mosquito abundance models. Given the increasing threat of mosquito-borne disease transmission in Florida, our analytic approach targets recent Ae. aegypti abundance in the Tampa Bay area. We investigate explanatory variables that: 1) are publicly available, 2) require little to no preprocessing for use, and 3) are known factors associated with Ae. aegypti ecology. Out of our 4 final models, none required more than 5 out of the 36 predictors assessed (13.9%). Similar to previous literature, the strongest predictors were consistently 3-and 4-wk temperature and precipitation lags, followed closely by 1 of 2 environmental predictors: land use/land cover or normalized difference vegetation index. Surprisingly, 3 of our 4 final models included one or more socioeconomic or demographic predictors. In general, larger sample sizes of trap collections and/or citizen science observations should result in greater confidence in model predictions and validation. However, given disparities in trap collections across jurisdictions, individual county models rather than a multicounty conglomerate model would likely yield stronger model fits. Ultimately, we hope that the results of our assessment will enable more accurate and precise mosquito surveillance and control of Ae. aegypti in Florida and beyond.