US-China Collab: Harnessing Big Data to understand and predict diversity and transmission of human- and animal-infected avian influenza viruses in China

中美合作：利用大数据了解和预测中国人类和动物感染的禽流感病毒的多样性和传播

• 批准号: 1911955
• 负责人: Xiangming Xiao
• 金额: $ 250万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911955&HistoricalAwards=false
• 关键词: US; China; Collab; Harnessing; Big

In the past two decades, highly pathogenic avian influenza viruses have infected poultry in many countries in the world, including China and the USA, which resulted in losses of billions of dollars in the poultry sector. According to the World Health Organization (WHO), a subset of these viruses has also caused symptoms or deaths to thousands of people since 2013. As these viruses persist, evolve, and spread, economic losses and health concerns to the agricultural, wildlife, and human communities are growing. Data and knowledge on the diversity and transmission of these viruses in epicenters such as China remain scattered, limited, and incomplete, which substantially hinders development of advanced capacity towards prediction and forecasting of diversity and transmission dynamics of avian influenza viruses. This project assembles an international and multi-disciplinary team from three US institutions (University of Oklahoma, U.S. Geological Survey Patuxent Wildlife Research Center, St. Jude Children's Research Hospital) and three institutions from China (China CDC, China Agricultural University, and Sun Yat-sen University), as well as the WHO Collaborating Center for Reference and Research on Influenza (China), and the WHO Collaborating Center for Studies on Ecology of Influenza in Animals (USA). The investigators will use both the One-Health (environmental-animal-human health) framework and Big Data approaches to advance research in ecology and evolution of influenza viruses and strengthen the capacity for international stakeholders to tackle critical issues in surveillance and pandemic preparedness. The project will train postdoctoral researchers and graduate students in interdisciplinary research skills for studying disease ecology and epidemiology. Through crowdsourcing, citizen science, and outreach activities this project will also educate non-academic stakeholders and the public on ecology and evolution of infectious diseases, which may lead to changes in human behaviors that could reduce the transmission and spillover of avian influenza viruses. The long-term goal of this collaborative work is to better understand, predict, and forecast the diversity and transmission of avian influenza viruses under four sets of specific aims and tasks. First, this project will use the One-Health framework to identify and document driving factors of avian influenza viruses at the human-animal-environment interface since the early 1980s in China, and Big Data approaches will be harnessed to improve disparate geospatial datasets of avian influenza viruses and discover driving factors of spill-over. These large datasets range from avian influenza virus genomic data to satellite-based landscape and wild bird migration data. Collation and synthesis of these data could substantially reduce the spatial and temporal uncertainties in our understanding and modeling of the transmission of avian influenza viruses. Second, this project will use phylogenetic and phylogeographic models to investigate the evolution of avian influenza viruses, which will help us better understand and predict their diversity. Third, this project will combine statistical and mathematical models to better understand and predict transmission dynamics of avian influenza viruses over time and space. Fourth, the research team will work with China CDC and other stakeholders to incorporate model results to support disease surveillance, control, and prevention. Data-models will be assimilated to assess the past and current avian influenza virus surveillance plans and guide the design of future surveillance plans. Simulations under various disease control scenarios will help assess outcomes and effectiveness of control measures on diversity and transmission of avian influenza viruses, which would assist decision makers and stakeholders in their efforts to tackle challenging issues in management of infectious diseases and public health.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.

在过去的二十年中，包括中国和美国在内的世界许多国家中感染了高度致病的禽流感病毒，这导致家禽部门损失了数十亿美元。根据世界卫生组织（WHO）的说法，这些病毒的一部分也自2013年以来也引起了数千人的症状或死亡。由于这些病毒持续，发展和传播，因此对农业，野生动植物和人类社区的经济损失和健康问题正在增长。关于这些病毒在中国等震中的多样性和传播的数据和知识仍然分散，有限和不完整，这实质上阻碍了对鸟类流感病毒多样性和传播动态的预测和预测的高级能力的发展。 This project assembles an international and multi-disciplinary team from three US institutions (University of Oklahoma, U.S. Geological Survey Patuxent Wildlife Research Center, St. Jude Children's Research Hospital) and three institutions from China (China CDC, China Agricultural University, and Sun Yat-sen University), as well as the WHO Collaborating Center for Reference and Research on Influenza (China), and the WHO Collaborating Center for Studies on Ecology of Influenza in Animals （美国）。研究人员将同时使用一项健康（环境 - 人类健康）框架和大数据方法来推进对流感病毒的生态学研究和进化的研究，并增强国际利益相关者解决监视和大流行准备方面的关键问题的能力。该项目将培训博士后研究人员和研究生跨学科研究技能，以研究疾病生态学和流行病学。通过众包，公民科学和外展活动，该项目还将教育非学术利益相关者和公众关于传染病的生态和进化，这可能导致人类行为的变化，从而减少禽流感病毒的传播和溢出。这项协作工作的长期目标是更好地理解，预测和预测在四组特定目标和任务下的禽流感病毒的多样性和传播。首先，该项目将使用单身卫生框架来识别和记录自1980年代初在中国以来人体环境环境界面上禽流感病毒的驱动因素和驱动因素，并将利用大数据方法来改善溢油剂溢油剂和发现动力驱动因素的不同地理空间数据集。这些大数据集范围从禽流感病毒基因组数据到基于卫星的景观和野生鸟类迁移数据。这些数据的整理和合成可以大大减少我们对禽流感病毒传播的理解和建模时的空间和时间不确定性。其次，该项目将使用系统发育和植物地理学模型来研究禽流感病毒的演变，这将有助于我们更好地理解和预测其多样性。第三，该项目将结合统计和数学模型，以更好地理解和预测随时间和空间的禽流感病毒的传播动态。第四，研究小组将与中国疾病预防控制中心和其他利益相关者合作，将模型结果纳入支持疾病监测，控制和预防。数据模型将被吸收，以评估过去和当前的禽流感病毒监测计划，并指导未来监视计划的设计。在各种疾病控制场景下的模拟将有助于评估控制措施的结果和有效性对禽流感病毒的多样性和传播的有效性，这将有助于决策者和利益相关者努力解决在传染病管理和公共卫生管理方面的挑战性问题。这项奖项反映了NSF的法规和经过评估的构成范围的范围。

项目成果

Post-2020 biodiversity framework challenged by cropland expansion in protected areas

• DOI: 10.1038/s41893-023-01093-w
• 发表时间: 2023-03
• 期刊: Nature Sustainability
• 影响因子: 27.6
• 作者: Z. Meng;Jinwei Dong;Erle C. Ellis;G. Metternicht;Yuanwei Qin;Xiao-Peng Song;Sara Löfqvist;R. Garrett;Xiaopeng Jia;Xiangming Xiao

A large but transient carbon sink from urbanization and rural depopulation in China

• DOI: 10.1038/s41893-021-00843-y
• 发表时间: 2022-02
• 期刊: Nature Sustainability
• 影响因子: 27.6
• 作者: Xiaoxin Zhang;M. Brandt;Xiaowei Tong;P. Ciais;Y. Yue;Xiangming Xiao;Wenmin Zhang;Kelin Wang

TROPOMI SIF reveals large uncertainty in estimating the end of plant growing season from vegetation indices data in the Tibetan Plateau

• DOI: 10.1016/j.rse.2022.113209
• 发表时间: 2022-10
• 期刊: Remote Sensing of Environment
• 影响因子: 13.5
• 作者: Jilin Yang;Xiangming Xiao;R. Doughty;Miaomiao Zhao;Yao Zhang;P. Köhler;Xiaocui Wu;C. Frankenberg-C.-Frankenb

Mapping Eucalyptus plantation in Guangxi, China by using knowledge-based algorithms and PALSAR-2, Sentinel-2, and Landsat images in 2020

• DOI: 10.1016/j.jag.2023.103348
• 发表时间: 2023-06
• 期刊: Int. J. Appl. Earth Obs. Geoinformation
• 作者: Chenchen Zhang;Xiangming Xiao;Liangcheng Zhao;Yuanwei Qin;R. Doughty;Xinxin Wang;Jinwei Dong;Xuebin Yang

Rebound in China’s coastal wetlands following conservation and restoration

中国滨海湿地保护和恢复后的恢复

• DOI: 10.1038/s41893-021-00793-5
• 发表时间: 2021
• 期刊: Nature Sustainability
• 影响因子: 27.6
• 作者: Xinxin Wang;Xiangming Xiao;Xiao Xu;Zhenhua Zou;Bangqian Chen;Yuanwei Qin;Xi Zhang;Jinwei Dong;Diyou Liu;Lianghao Pan;Bo Li
• 通讯作者: Bo Li