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| RII Track-2 FEC: Leveraging Big Data to Improve Prediction of Tick-Borne Disease Patterns and Dynamics | |
| 项目编号 | 2019609 |
| Xiaogang Ma | |
| 项目主持机构 | Regents of the University of Idaho |
| 开始日期 | 2020-09-01 |
| 结束日期 | 08/31/2024 |
| 英文摘要 | 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. |
| 资助机构 | US-NSF |
| 项目经费 | $3,496,232.00 |
| 项目类型 | Cooperative Agreement |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/211621 |
| 推荐引用方式 GB/T 7714 | Xiaogang Ma.RII Track-2 FEC: Leveraging Big Data to Improve Prediction of Tick-Borne Disease Patterns and Dynamics.2020. |
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