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

在过去二十年中，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，导致家禽业损失数十亿美元。据世界卫生组织（世卫组织）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。在中国等疫情中心，关于这些病毒的多样性和传播的数据和知识仍然分散、有限和不完整，这大大阻碍了对禽流感病毒多样性和传播动态进行预测和预报的先进能力的发展。该项目汇集了来自美国三家机构（俄克拉何马大学、美国地质调查局帕图克森特野生动物研究中心、圣裘德儿童研究医院）和中国三家机构（中国疾病预防控制中心、中国农业大学和中山大学）以及世界卫生组织流感参考与研究合作中心（中国）和世界卫生组织动物流感生态学研究合作中心（美国）的国际多学科团队。研究人员将使用One-Health（环境-动物-人类健康）框架和大数据方法来推进流感病毒生态学和进化研究，并加强国际利益攸关方解决监测和大流行防范方面关键问题的能力。该项目将培养研究疾病生态学和流行病学的博士后和研究生的跨学科研究技能。通过众包、公民科学和外联活动，该项目还将教育非学术利益攸关方和公众了解传染病的生态学和进化，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外溢。这项合作工作的长期目标是在四套具体目标和任务下更好地了解、预测和预测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-环境界面上禽流感病毒的驱动因素，并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。这些大型数据集包括从禽流感病毒基因组数据到基于卫星的景观和野鸟迁徙数据。整理和综合这些数据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，本项目将使用系统发育和系统地理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，该项目将结合统计和数学模型，以更好地了解和预测禽流感病毒随时间和空间的传播动态。第四，研究小组将与中国疾控中心和其他利益攸关方合作，将模型结果纳入疾病监测、控制和预防。将吸收数据模型，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。各种疾病控制情景下的模拟将有助于评估禽流感病毒多样性和传播控制措施的成果和有效性，这将有助于决策者和利益攸关方努力解决传染病和公共卫生管理方面的挑战性问题。该奖项反映了美国国家科学基金会的法定使命，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。

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.