RII Track-2 FEC: Marshalling Diverse Big Data Streams to Understand Risk of Tick-Borne Diseases in the Great Plains

RII Track-2 FEC：整理不同的大数据流以了解大平原蜱传疾病的风险

• 批准号: 1920946
• 负责人: Townsend Peterson
• 金额: $ 392.12万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1920946&HistoricalAwards=false
• 关键词: RII; Track; FEC; Marshalling; Diverse

Tick-borne diseases, including Lyme disease, Rocky Mountain spotted fever, and others, are increasingly appreciated as a significant public health concern worldwide, and an increasing concern in the Great Plains in particular. However, a detailed understanding of these diseases, how they are acquired, where are the high-risk areas, and how might they best be mitigated, has remained surprisingly opaque. This project, a collaboration between University of Kansas, Kansas State University, Pittsburgh State University, Oklahoma State University, the University of Oklahoma, the University of Oklahoma, Norman campus, and the University of Central Oklahoma, represents a broad-scope, highly interdisciplinary, integrated, and data-intensive effort to illuminate these questions across two states, Kansas and Oklahoma in the Great Plains. Major elements of the project include assembling detailed, large-scale datasets on the occurrences of different tick species, the genomes of the ticks and the pathogens, and environmental variation across the region, as well as marshaling new artificial-intelligence tools to permit rapid and accurate tick identifications by non-experts. Project scientists will use ecological niche modeling and mathematical population modeling approaches to assess and predict transmission of the major tick-borne pathogens, and create and test the automated identification tools. The project will foster what can be termed "big data literacy" via a series of workshops and courses, as well as online data resources. Perhaps most importantly, the project will involve numerous undergraduate and graduate students in many project tasks, giving them opportunities to learn and explore futures in these and related areas of science. Project students will be recruited as broadly as possible, to represent in particular populations that are not well-represented in science, including minorities, women, and those from families without a tradition of university-level education. Project outcomes will include online, interactive maps of tick-borne disease risk, and online facilities for identification of tick photographs taken by the general public. Junior members of the project team (younger faculty, postdocs, and students) will be mentored and guided by more senior individuals, so as to maximize the probability of their successful advancement in this field. The project team will be guided by an advisory board with broad and international expertise, as well as state-level public health policy experience. At the close of the project, we anticipate a much-improved and considerably more detailed understanding of the diversity and risk of tick-borne diseases across Kansas and Oklahoma.Tick-borne diseases are increasingly recognized as an important public health concern across the United States, including Lyme disease, ehrlichiosis, Rocky Mountain spotted fever, southern tick-associated rash illness, human granulocytic anaplasmosis, babesiosis, and viral infections with Heartland and Bourbon viruses. Knowledge of the spatial distributions of ticks and pathogen species, and the associated spatial risk of transmission of tick-borne diseases in the Great Plains is quite limited. This project marshals several "big data" streams (tick occurrence data, tick and pathogen genomic data, remote sensing data to characterize environments) and novel scientific tools to shed light on geographic patterns and temporal dynamics of risk of infection with the pathogens that cause these diseases. Specifically, this project, a collaboration between University of Kansas, Kansas State University, Pittsburgh State University, Oklahoma State University, the University of Oklahoma, the University of Oklahoma, Norman campus, and the University of Central Oklahoma, will involve field collections of ticks and vertebrates hosting ticks from 12 sites in ecologically distinct regions across Kansas and Oklahoma, which will be tested using genomic tools for a diverse suite of pathogens; the resulting data on tick and pathogen distributions will be the basis for detailed modeling of transmission risk using complementary, cutting-edge tools (correlative niche models, mechanistic population models) to achieve new syntheses of population and range dynamics. This project will explore and develop a first automated tick identification system based on deep-learning approaches, which will feed much more information into the other analyses envisioned for this project in the form of detailed distributional data. This project will also have substantial implications for broadening participation in science, via linking senior and junior scientists (including minority faculty members) in a joint, collaborative, and integrative effort designed to mentor and build confidence and stature among junior team members (faculty, postdocs, graduate students, undergraduate students) and among faculty from teaching-focused institutions. This project will offer various educational opportunities in the areas of big data analytics, big data access, and disease risk mapping, which will be made broadly and openly available to the scientific community. Finally, towards mitigating effects of tick-borne diseases in communities across the southern Great Plains, this project will produce and make broadly available detailed risk maps for each tick-borne disease in the region, and create technology for automating tick identifications, to allow citizen scientists and stakeholders in the general public better access to information on ticks and disease risks that are personally and immediately relevant.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

tick传播的疾病，包括莱姆病，洛矶山脉斑点发烧等，越来越多地赞赏全球重要的公共卫生关注，尤其是大平原上的人们越来越关注。但是，对这些疾病的详细理解，如何获得，高风险地区在哪里以及如何最好地减轻它们，这仍然令人惊讶地不透明。该项目是堪萨斯大学，堪萨斯州立大学，匹兹堡州立大学，俄克拉荷马州立大学，俄克拉荷马大学，俄克拉荷马大学，俄克拉荷马大学，诺曼大学校园和俄克拉荷马州中央大学，代表着一个广泛的，高度的跨学科，跨学科，整合，集成的努力，以及两个州的努力。该项目的主要要素包括组装详细的，大规模的数据集，涉及不同tick物种的发生，tick虫和病原体的基因组以及整个地区的环境变化，以及提供新的人工智能工具，以允许非专家的快速而准确的tick识别。项目科学家将使用生态利基建模和数学种群建模方法来评估和预测主要的tick传播病原体的传播，并创建和测试自动化识别工具。该项目将通过一系列研讨会和课程以及在线数据资源来促进可以称为“大数据素养”的内容。也许最重要的是，该项目将涉及许多项目任务中的众多本科生和研究生，从而为他们提供了学习和探索这些科学领域的未来的机会。项目学生将尽可能广泛地招募，以代表特定的人群，这些人群在科学方面没有很好的代表，包括少数民族，妇女以及没有大学级教育传统的家庭的人群。项目成果将包括在线，tick虫疾病风险的互动地图，以及在线设施，以识别公众拍摄的tick照片。项目团队的初级成员（年轻的教职员工，博士学位和学生）将受到更多高级个人的指导和指导，以最大程度地提高他们在该领域取得成功的可能性。该项目团队将由具有广泛和国际专业知识以及州级公共卫生政策经验的顾问委员会指导。在项目结束时，我们预计对整个堪萨斯州和俄克拉荷马州的tick传播疾病的多样性和风险的多样性和风险有更加详细的了解。巴贝斯病，以及心脏地带和波旁病毒的病毒感染。了解壁虱和病原体物种的空间分布以及大平原上tick传播疾病的相关空间风险非常有限。该项目将几个“大数据”流提供（tick出现数据，tick和病原体基因组数据，遥感数据以表征环境）以及新颖的科学工具，以阐明地理模式和带有导致这些疾病的病原体感染风险的时间动态。具体而言，该项目是堪萨斯大学，堪萨斯州立大学，匹兹堡州立大学，俄克拉荷马州立大学，俄克拉荷马大学，俄克拉荷马大学，俄克拉荷马大学，诺曼大学校园和俄克拉荷马州中央大学的合作多种病原体套件；最终的有关tick和病原体分布的数据将是使用互补的，最先进的工具（相关利基模型，机械种群模型）对传输风险进行详细建模的基础，以实现人口和范围动态的新合成。该项目将基于深入学习方法探索并开发第一个自动化的刻度标识系统，该系统将以详细的分配数据的形式将更多信息提供给该项目所设想的其他分析。该项目还将通过将高级和初级科学家（包括少数群体的教职员工）联系起来，在共同的，协作和综合的努力中，旨在指导和建立初级团队成员（教职员工，研究生，本科生，本科生）以及来自教学领域的教职员工的联合，建立信心和地位。该项目将在大数据分析，大数据访问和疾病风险映射领域提供各种教育机会，该领域将为科学界广泛而公开地提供。最后，为了减轻整个大平原社区中tick传播疾病的影响，该项目将为该地区的每种tick虫疾病提供广泛的详细风险图，并创建技术，以使公民科学家和疾病中的公民相关者和疾病的疾病范围内的信息和疾病的范围更加有用。通过使用基金会的知识分子和更广泛影响的评论标准来通过评估来支持。

项目成果

Selection of sampling sites for biodiversity inventory: Effects of environmental and geographical considerations

• DOI: 10.1111/2041-210x.13869
• 发表时间: 2022-04
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: C. Nuñez-Penichet;M. Cobos;Jorge Soberón;Tomer Gueta;N. Barve;V. Barve;Adolfo G. Navarro‐Sigüenza;A. Peterson

Predicting the potential distribution of Amblyomma americanum (Acari: Ixodidae) infestation in New Zealand, using maximum entropy-based ecological niche modelling.

• DOI: 10.1007/s10493-019-00460-7
• 发表时间: 2020-02
• 期刊: Experimental & applied acarology
• 影响因子: 2.2
• 作者: Raghavan RK;Heath ACG;Lawrence KE;Ganta RR;Peterson AT;Pomroy WE
• 通讯作者: Pomroy WE

Assessing variability of optimum air temperature for photosynthesis across site-years, sites and biomes and their effects on photosynthesis estimation

• DOI: 10.1016/j.agrformet.2020.108277
• 发表时间: 2021-03
• 期刊: Agricultural and Forest Meteorology
• 影响因子: 6.2
• 作者: Q. Chang;Xiangming Xiao;R. Doughty;Xiaocui Wu;Wenzhe Jiao;Yuanwei Qin

Identification of Rickettsia spp. and Babesia conradae in Dermacentor spp. Collected from Dogs and Cats Across the United States.

• DOI: 10.1089/vbz.2021.0047
• 发表时间: 2021-12
• 期刊: Vector borne and zoonotic diseases (Larchmont, N.Y.)
• 作者: Duncan KT;Grant A;Johnson B;Sundstrom KD;Saleh MN;Little SE
• 通讯作者: Little SE

Flash drought identification from satellite-based land surface water index

• DOI: 10.1016/j.rsase.2022.100770
• 发表时间: 2022-04
• 期刊: Remote Sensing Applications: Society and Environment
• 作者: J. Christian;J. Basara;L. Lowman;Xiangming Xiao;Daniel Mesheske;Yuting Zhou