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
基本信息
- 批准号:1911955
- 负责人:
- 金额:$ 250万
- 依托单位:
- 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:
- 关键词:
项目摘要
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.
在过去的二十年里,高致病性禽流感病毒感染了世界上许多国家的家禽,包括中国和美国,这导致家禽业损失数十亿美元。据世界卫生组织(WHO)称,自2013年以来,这些病毒的一个子集也导致数千人出现症状或死亡。随着这些病毒的持续存在、进化和传播,农业、野生动物和人类社区的经济损失和健康问题正在增加。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,这严重阻碍了预测和预报禽流感病毒多样性和传播动态的先进能力的发展。该项目汇集了来自三个美国机构的国际和多学科团队(俄克拉荷马州大学、美国地质调查局帕塔克森特野生动物研究中心、圣裘德儿童研究医院)和来自中国的三家机构(中国疾病预防控制中心、中国农业大学和中山大学)以及世卫组织流感参考与研究合作中心(中国),和世卫组织动物流感生态学研究合作中心(美国)。研究人员将使用“一个健康”(环境-动物-人类健康)框架和大数据方法来推进流感病毒的生态学和进化研究,并加强国际利益相关者解决监测和大流行病准备中关键问题的能力。该项目将培养博士后研究人员和研究生在疾病生态学和流行病学研究方面的跨学科研究技能。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了解传染病的生态学和演变,这可能导致人类行为的改变,从而减少禽流感病毒的传播和溢出。这项合作工作的长期目标是根据四套具体目标和任务,更好地了解、预测和预报禽流感病毒的多样性和传播。首先,该项目将使用“一个健康”框架来识别和记录自20世纪80年代初以来中国人-动物-环境界面上的禽流感病毒驱动因素,并将利用大数据方法来改进禽流感病毒的不同地理空间数据集并发现溢出的驱动因素。这些大型数据集的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徙数据。这些数据的整理和综合可以大大减少我们对禽流感病毒传播的理解和建模中的空间和时间不确定性。其次,本项目将利用系统发育和地理分布模型来研究禽流感病毒的进化,这将有助于我们更好地了解和预测其多样性。第三,该项目将结合联合收割机统计和数学模型,以更好地了解和预测禽流感病毒在时间和空间上的传播动态。第四,研究团队将与中国疾控中心和其他利益相关者合作,将模型结果纳入疾病监测、控制和预防。数据模型将被吸收,以评估过去和目前的禽流感病毒监测计划,并指导未来监测计划的设计。在各种疾病控制情景下的模拟将有助于评估控制措施对禽流感病毒多样性和传播的效果和有效性,该奖项将协助决策者和利益相关者努力解决传染病和公共卫生管理中的挑战性问题。该奖项反映了NSF的法定使命,并通过利用基金会的智力价值和能力进行评估,被认为值得支持更广泛的影响审查标准。
项目成果
期刊论文数量(21)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 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
- 通讯作者:Z. Meng;Jinwei Dong;Erle C. Ellis;G. Metternicht;Yuanwei Qin;Xiao-Peng Song;Sara Löfqvist;R. Garrett;Xiaopeng Jia;Xiangming Xiao
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:
- 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
- 通讯作者:Jilin Yang;Xiangming Xiao;R. Doughty;Miaomiao Zhao;Yao Zhang;P. Köhler;Xiaocui Wu;C. Frankenberg-C.-Frankenb
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:
- 影响因子:27.6
- 作者:Xiaoxin Zhang;M. Brandt;Xiaowei Tong;P. Ciais;Y. Yue;Xiangming Xiao;Wenmin Zhang;Kelin Wang
- 通讯作者:Xiaoxin Zhang;M. Brandt;Xiaowei Tong;P. Ciais;Y. Yue;Xiangming Xiao;Wenmin Zhang;Kelin Wang
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:
- 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
Improved maps of surface water bodies, large dams, reservoirs, and lakes in China
- DOI:10.5194/essd-14-3757-2022
- 发表时间:2022-08
- 期刊:
- 影响因子:11.4
- 作者:Xinxin Wang;Xiangming Xiao;Yuanwei Qin;Jinwei Dong;Jihua Wu;Bo Li-
- 通讯作者:Xinxin Wang;Xiangming Xiao;Yuanwei Qin;Jinwei Dong;Jihua Wu;Bo Li-
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Xiangming Xiao其他文献
Spatial-temporal consistency between gross primary productivity and solar-induced chlorophyll fluorescence of vegetation in China during 2007-2014
2007-2014年中国植被总初级生产力与日照叶绿素荧光时空一致性
- DOI:
10.1016/j.scitotenv.2018.05.245 - 发表时间:
2018 - 期刊:
- 影响因子:9.8
- 作者:
Jun Ma;Xiangming Xiao;Yao Zhang;Russell Doughty;Bangqian Chen;Bin Zhao - 通讯作者:
Bin Zhao
Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia
东南亚 GIMMS NDVI3g、GIMMS 和 MODIS 得出的总初级生产力比较
- DOI:
10.3390/rs6032108 - 发表时间:
2014-03 - 期刊:
- 影响因子:5
- 作者:
Hiroaki Kondo;Yunfen Liu;Takashi Hirano;Xiangming Xiao - 通讯作者:
Xiangming Xiao
Contribution of urban ventilation to the thermal environment and urban energy demand: Different climate background perspectives
城市通风对热环境和城市能源需求的贡献:不同气候背景视角
- DOI:
10.1016/j.scitotenv.2021.148791 - 发表时间:
2021 - 期刊:
- 影响因子:9.8
- 作者:
Jun Yang;Yichen Wang;Bing Xue;Yunfei Li;Xiangming Xiao;Jianhong Xia;Baojie He - 通讯作者:
Baojie He
Comparison of venetoclax and ivosidenib/enasidenib for unfit newly diagnosed patients with acute myeloid leukemia and IDH1/2 mutation: a network meta-analysis
Venetoclax 与 ivosidenib/enasidenib 用于不适宜初诊急性髓系白血病 IDH1/2 突变患者的比较:一项网络荟萃分析
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:1.8
- 作者:
Lida Wang;Jiwu Song;Xiangming Xiao;Dianfang Li;Tianmeng Liu;Xiaopo He - 通讯作者:
Xiaopo He
Assessing summer thermal environment in humid cities: Local climate zone perspective
评估潮湿城市的夏季热环境:地方气候区视角
- DOI:
10.1016/j.isci.2025.112758 - 发表时间:
2025-07-18 - 期刊:
- 影响因子:4.100
- 作者:
Junjie Wang;Jun Yang;Xiangming Xiao;Yi Bai;Qiyue Zou;Baojie He - 通讯作者:
Baojie He
Xiangming Xiao的其他文献
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{{ truncateString('Xiangming Xiao', 18)}}的其他基金
Conference: USA-UK-China-Israel Workshop on Frontiers in Ecology and Evolution of Infectious Diseases
会议:美国-英国-中国-以色列生态学和传染病进化前沿研讨会
- 批准号:
2406564 - 财政年份:2024
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$ 250万 - 项目类别:
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I-Corps: An integrated tool for crop ecosystem monitoring, analysis, and prediction
I-Corps:作物生态系统监测、分析和预测的综合工具
- 批准号:
2306392 - 财政年份:2023
- 资助金额:
$ 250万 - 项目类别:
Standard Grant
PIPP Phase 1: International Center for Avian Influenza Pandemic Prediction and Prevention
PIPP 第一阶段:国际禽流感大流行预测和预防中心
- 批准号:
2200310 - 财政年份:2022
- 资助金额:
$ 250万 - 项目类别:
Standard Grant
US-China Workshop on Frontiers in Ecology and Evolution of Infectious Diseases; January 10-12, 2018, Shenzhen, China
中美传染病生态学和进化前沿研讨会;
- 批准号:
1817884 - 财政年份:2017
- 资助金额:
$ 250万 - 项目类别:
Standard Grant
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