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

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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.