News Release

OU researchers award two NSF pandemic prediction and prevention projects

Two groups of researchers at the University of Oklahoma have each received nearly $1 million from the National Science Foundation as part of its Predictive Intelligence for Pandemic Prevention initiative

Grant and Award Announcement

University of Oklahoma

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Two groups of researchers at the University of Oklahoma have each received nearly $1 million grants from the National Science Foundation as part of its Predictive Intelligence for Pandemic Prevention initiative, which focuses on fundamental research and capabilities needed to tackle grand challenges in infectious disease pandemics through prediction and prevention.

To date, researchers from 20 institutions nationwide were selected to receive an NSF PIPP Award. OU is the only university to receive two grants to the same institution.

“The next pandemic isn’t a question of ‘if,’ but ‘when,’” said OU Vice President for Research and Partnerships Tomás Díaz de la Rubia. “Research at the University of Oklahoma is going to help society be better prepared and responsive to future health challenges.”

Next-Generation Surveillance

David Ebert, Ph.D., professor of computer science and electrical and computer engineering in the Gallogly College of Engineering, is the principal investigator on one of the projects, which explores new ways of sharing, integrating and analyzing data using new and traditional data sources. Ebert is also the director of the Data Institute for Societal Challenges at OU, which applies OU expertise in data science, artificial intelligence, machine learning and data-enabled research to solving societal challenges.

While emerging pathogens can circulate among wild or domestic animals before crossing over to humans, the delayed response to the COVID-19 pandemic has highlighted the need for new early detection methods, more effective data management, and integration and information sharing between officials in both public and animal health.

Ebert’s team, composed of experts in data science, computer engineering, public health, veterinary sciences, microbiology and other areas, will look to examine data from multiple sources, such as veterinarians, agriculture, wastewater, health departments, and outpatient and inpatient clinics, to potentially build algorithms to detect the spread of signals from one source to another. The team will develop a comprehensive animal and public health surveillance, planning and response roadmap that can be tailored to the unique needs of communities.

“Integrating and developing new sources of data with existing data sources combined with new tools for detection, localization and response planning using a One Health approach could enable local and state public health partners to respond more quickly and effectively to reduce illness and death,” Ebert said. “This planning grant will develop proof-of-concept techniques and systems in partnership with local, state and regional public health officials and create a multistate partner network and design for a center to prevent the next pandemic.”

The Centers for Disease Control and Prevention describes One Health as an approach that bridges the interconnections between people, animals, plants and their shared environment to achieve optimal health outcomes.

Co-principal investigators on the project include Michael Wimberly, Ph.D., professor in the College of Atmospheric and Geographic Sciences; Jason Vogel, Ph.D., director of the Oklahoma Water Survey and professor in the Gallogly College of Engineering School of Civil Engineering and Environmental Science; Thirumalai Venkatesan, director of the Center for Quantum Research and Technology in the Dodge Family College of Arts and Sciences; and Aaron Wendelboe, Ph.D., professor in the Hudson College of Public Health at the OU Health Sciences Center.

Predicting and Preventing the Next Avian Influenza Pandemic

Several countries have experienced deadly outbreaks of avian influenza, commonly known as bird flu, that have resulted in the loss of billions of poultry, thousands of wild waterfowl and hundreds of humans. Researchers at the University of Oklahoma are taking a unique approach to predicting and preventing the next avian influenza pandemic.

Xiangming Xiao, Ph.D., professor in the Department of Microbiology and Plant Biology and director of the Center for Earth Observation and Modeling in the Dodge Family College of Arts and Sciences, is leading a project to assemble a multi-institutional team that will explore pathways for establishing an International Center for Avian Influenza Pandemic Prediction and Prevention.

The goal of the project is to incorporate and understand the status and major challenges of data, models and decision support tools for preventing pandemics. Researchers hope to identify future possible research and pathways that will help to strengthen and improve the capability and capacity to predict and prevent avian influenza pandemics.

“This grant is a milestone in our long-term effort for interdisciplinary and convergent research in the areas of One Health (human-animal-environment health) and big data science,” Xiao said. “This is an international project with geographical coverage from North America, Europe and Asia; thus, it will enable OU faculty and students to develop greater ability, capability, capacity and leaderships in prediction and prevention of global avian influenza pandemic.”

Other researchers on Xiao’s project include co-principal investigators A. Townsend Peterson, Ph.D., professor at the University of Kansas; Diann Prosser, Ph.D., research wildlife ecologist for the U.S. Geological Survey; and Richard Webby, Ph.D., director of the World Health Organization Collaborating Centre for Studies on the Ecology of Influenza in Animals and Birds with St. Jude Children’s Research Hospital. Wayne Marcus Getz, professor at the University of California, Berkeley, is also assisting on the project.

The National Science Foundation grant for Ebert’s research is set to end Jan. 31, 2024, while Xiao’s grant will end Dec. 31, 2023.


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