RII Track-2 FEC: Leveraging Big Data to Improve Prediction of Tick-Borne Disease Patterns and Dynamics

RII Track-2 FEC：利用大数据改进对蜱传疾病模式和动态的预测

• 批准号: 2019609
• 负责人: Xiaogang Ma
• 金额: $ 583.07万
• 依托单位: Regents of the University of Idaho
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019609&HistoricalAwards=false
• 关键词: RII; Track; FEC; Leveraging; Big

Tick-borne diseases (TDs) account for a staggering 94% of human illnesses due to vector-borne diseases in the U.S. The mission of this project is to assimilate disparate datasets with spatio-temporal, environmental and human predictors and to leverage cyberinfrastructure and data science to enhance forecasting of TDs in the western US. The core members of this project are from universities in three EPSCoR jurisdictions: University of Idaho, University of Nevada, Reno, and Dartmouth College (New Hampshire). The collaboration will build capacity across traditional boundaries of research and practice, with an aim to change the way people tackle TDs. Building upon the best practices and standards for open data, the findability and reusability of the assimilated datasets will be improved to enable new analyses and findings. Accordingly, the contributions of this project will have broad and sustained impacts on TD, a public health issue of national importance. With the early-career faculty mentoring activities, this project will increase the pool of academics and practitioners in a collaborative network for improved prediction and informed response to TDs in the western US. The digital games and demos released by the project will help improve the awareness of TDs among the general public. The efforts of this project will also support underserved and largely rural populations at high risk of TDs. All the training programs, including postdoc and graduate student positions, will give priority to women and underrepresented minority groups. Through the national Big Data innovation ecosystem, this project will add a new community of practice via shared deliverables, datasets and complementary knowledge to improve monitoring and forecasting of TDs across US and the world. This project will contribute to NSF’s big ideas on Harnessing the Data Revolution and Growing Convergence Research through data-intensive research for improved prediction of TDs. The central scientific hypothesis is that, climate change will increase the prevalence of TDs throughout the western US, both through altering the geographic and seasonal distributions of ticks as well as interacting factors of environment, ecology, socioeconomics, and human behavior. The project team will collect and develop application-level datasets, knowledge graphs, tools, and innovative data science methods to advance the understanding of factors, patterns, and risks for TDs in the western US. The research includes three focused scientific objectives: (1) An advanced framework for TD research: Sparse data collection and FAIR framework, workflow provenance, and algorithms for a data life cycle; (2) Identify the changing patterns in tick importation routes, pathogens, and TD dynamics in the West; and (3) Develop spatio-temporal models of tick dynamics that link TDs to climate, environment and socioeconomic factors. The team will incorporate expertise in complementary disciplines to generate enriched open data, promote innovation and capacity in big data analytics, and develop training, education and outreach programs for sustained impact. Through the teamwork, the research will produce fresh understanding of the interacting factors in TD dynamics. Resources and mentoring to support early-career professionals will build towards sustained productivity. We will bring state-of-the-art knowledge and skills to postdocs, students and other practitioners to nurture a new workforce. This collaborative project will engage academic, state, federal and local partners to create a connected and smart network to tackle TDs.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.

蜱传疾病（TDs）占美国因病媒传播疾病引起的人类疾病的惊人94%。该项目的使命是将不同的数据集与时空，环境和人类预测因子同化，并利用网络基础设施和数据科学来加强美国西部的TDs预测。该项目的核心成员来自三个EPSCoR管辖区的大学：爱达荷州大学、内华达州大学、里诺大学和达特茅斯学院（新罕布什尔州）。这项合作将跨越传统的研究和实践界限，建立能力，旨在改变人们应对TD的方式。在开放数据的最佳做法和标准的基础上，将改进已同化数据集的可查找性和可重复使用性，以便能够进行新的分析和得出新的结论。因此，该项目的贡献将对TD这一具有国家重要性的公共卫生问题产生广泛和持续的影响。通过早期职业教师指导活动，该项目将增加合作网络中的学者和从业人员，以改善对美国西部TD的预测和知情响应。该计划推出的数码游戏及示范将有助提高公众对TD的认识。这一项目的努力还将支持得不到充分服务和大多数农村人口面临的艾滋病高风险。所有的培训项目，包括博士后和研究生职位，都将优先考虑妇女和代表性不足的少数群体。通过国家大数据创新生态系统，该项目将通过共享的可交付成果、数据集和互补知识增加一个新的实践社区，以改善美国和世界各地的TD监测和预测。 该项目将通过数据密集型研究促进NSF关于利用数据革命和不断增长的融合研究的重大想法，以改善TD的预测。核心科学假设是，气候变化将通过改变蜱虫的地理和季节分布以及环境、生态、社会经济和人类行为的相互作用因素，增加整个美国西部的TD患病率。该项目团队将收集和开发应用级数据集、知识图谱、工具和创新的数据科学方法，以促进对美国西部TD因素、模式和风险的理解。该研究包括三个重点科学目标：（1）TD研究的高级框架：稀疏数据收集和FAIR框架，工作流程起源和数据生命周期的算法;（2）确定西方蜱虫输入途径，病原体和TD动态的变化模式;（3）开发将TD与气候，环境和社会经济因素联系起来的蜱虫动态的时空模型。该团队将整合互补学科的专业知识，以生成丰富的开放数据，促进大数据分析的创新和能力，并制定培训，教育和推广计划以产生持续影响。通过团队合作，本研究将对TD动力学中的相互作用因素产生新的理解。支持早期职业专业人员的资源和指导将有助于实现持续的生产力。我们将为博士后、学生和其他从业人员带来最先进的知识和技能，以培养新的劳动力。该合作项目将吸引学术界、州、联邦和地方合作伙伴共同创建一个互联的智能网络来解决TD问题。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Single-cell RNA sequencing data imputation using similarity preserving network

使用相似性保留网络进行单细胞 RNA 测序数据插补

• DOI: 10.1109/kse53942.2021.9648794
• 发表时间: 2021
• 期刊: 13th International Conference on Knowledge and Systems Engineering (KSE
• 作者: Tran, Duc;Nguyen, Hung;Harris, Frederick C.;Nguyen, Tin
• 通讯作者: Nguyen, Tin

Meteorological data source comparison-a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation.

• DOI: 10.1007/s10661-023-11283-w
• 发表时间: 2023-06-12
• 期刊: Environmental monitoring and assessment
• 影响因子: 3

Parallel computing for Fast Spatiotemporal Weighted Regression

• DOI: 10.1016/j.cageo.2021.104723
• 发表时间: 2021-03-11
• 期刊: COMPUTERS & GEOSCIENCES
• 影响因子: 4.4
• 作者: Que, Xiang;Ma, Chao;Chen, Qiyu
• 通讯作者: Chen, Qiyu

An efficient multiple scanning order algorithm for accumulative least-cost surface calculation

一种高效的多扫描顺序累积最小成本曲面计算算法

• DOI: 10.1080/13658816.2022.2052885
• 发表时间: 2022
• 期刊: International Journal of Geographical Information Science
• 影响因子: 5.7
• 作者: Yao, Yuanzhi;Shi, Xun;Wang, Zekun
• 通讯作者: Wang, Zekun

An Application for Simulating Patient Handoff Using 360 Video and Eye Tracking in Virtual Reality

在虚拟现实中使用 360 度视频和眼动追踪来模拟患者交接的应用程序

• DOI: 10.29007/82j6
• 发表时间: 2002
• 期刊: EPiC Series in Computing
• 作者: Lewis, Christopher;Diaz-Juarez, Sven;Anbro, Steven;Szarko, Alison;Houmanfar, Ramona;Crosswell, Laura;Rebaleati, Michelle;Starmer, Luka;Harris, Frederick
• 通讯作者: Harris, Frederick