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

蜱传疾病，包括莱姆病，落基山斑疹热等，越来越被认为是全球范围内的重大公共卫生问题，特别是在大平原地区日益受到关注。然而，对这些疾病的详细了解，它们是如何获得的，高风险区域在哪里，以及如何最好地减轻它们，仍然令人惊讶地不透明。这个项目是堪萨斯大学、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、俄克拉荷马大学诺曼校区和中俄克拉荷马大学之间的合作，代表了一个广泛的、高度跨学科的、综合的和数据密集型的努力，以阐明大平原上的堪萨斯和俄克拉荷马州这两个州的这些问题。该项目的主要内容包括收集有关不同蜱虫物种、蜱虫和病原体的基因组以及该地区环境变化的详细的大规模数据集，以及整理新的人工智能工具，以允许非专家快速准确地识别蜱虫。项目科学家将使用生态位建模和数学种群建模方法来评估和预测主要蜱传病原体的传播，并创建和测试自动识别工具。该项目将通过一系列讲习班和课程以及在线数据资源，促进所谓的“大数据扫盲”。也许最重要的是，该项目将涉及许多本科生和研究生在许多项目任务，让他们有机会学习和探索未来在这些和相关领域的科学。将尽可能广泛地招收项目学生，以代表在科学方面没有充分代表性的特定人群，包括少数民族、妇女和来自没有接受大学教育传统的家庭的人。项目成果将包括蜱虫传播疾病风险的在线互动地图，以及用于识别公众拍摄的蜱虫照片的在线设施。项目团队的初级成员（年轻的教师，博士后和学生）将由更资深的人指导和指导，以最大限度地提高他们在这一领域成功发展的可能性。该项目小组将由一个具有广泛和国际专业知识以及国家级公共卫生政策经验的咨询委员会指导。在项目结束时，我们预计对堪萨斯和俄克拉荷马州蜱传疾病的多样性和风险有更深入和更详细的了解。蜱传疾病越来越被认为是美国重要的公共卫生问题，包括莱姆病，埃立克体病，落基山斑疹热，南方蜱相关皮疹疾病，人粒细胞无形体病，巴贝西虫病，以及心脏地带和波旁病毒的病毒感染。蜱和病原体物种的空间分布，以及在大平原蜱传疾病传播的相关空间风险的知识是相当有限的。该项目汇集了几个“大数据”流（蜱虫发生数据、蜱虫和病原体基因组数据、表征环境的遥感数据）和新的科学工具，以揭示导致这些疾病的病原体感染风险的地理模式和时间动态。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、俄克拉荷马大学诺曼校区和中俄克拉荷马大学之间的合作，将涉及从堪萨斯和俄克拉荷马州生态不同地区的12个地点实地收集蜱虫和脊椎动物宿主蜱虫，这将使用基因组工具对一套不同的病原体进行测试;所得到的蜱虫和病原体分布数据将成为使用互补的尖端工具（相关生态位模型、机械种群模型）对传播风险进行详细建模的基础，以实现种群和范围动态的新综合。该项目将探索和开发第一个基于深度学习方法的自动蜱虫识别系统，该系统将以详细分布数据的形式将更多信息输入到该项目的其他分析中。该项目还将对扩大科学参与产生重大影响，通过将高级和初级科学家（包括少数民族教师）联系起来，共同合作和综合努力，旨在指导和建立初级团队成员（教师，博士后，研究生，本科生）和教师之间的信心和地位。该项目将在大数据分析、大数据访问和疾病风险绘图领域提供各种教育机会，这些机会将广泛公开地提供给科学界。最后，为了减轻蜱传疾病在南部大平原社区的影响，该项目将为该地区的每种蜱传疾病制作并广泛提供详细的风险地图，并创造自动识别蜱虫的技术，让公民科学家和公众利益相关者更好地获得与个人和直接相关的蜱虫和疾病风险信息。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.