ABSTRACT

Avian influenza, often referred to as bird flu, has had many outbreaks in Asia, Africa, Europe, and North America in the past decades, resulting in losses of billions of poultry, thousands of wild waterfowl, and hundreds of humans. As the 1918 Influenza Pandemic revealed the potential of influenza viruses that originate in birds to kill millions of humans, prediction and prevention of the next influenza pandemic is one of the grand challenges in global health. Although significant investment and research have been made in avian influenza virus (AIV) research over the past two decades, the capacity to predict AIV pandemic is still woefully inadequate. There is an urgent and imperative need to fortify the prediction capacity, as AIV risk factors in the human-animal-environment systems (HAES) have changed rapidly in the past few decades and are expected to change at a much faster pace across the world in the next few decades. This Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grant assembles a multi-institutional team to explore the appropriate pathways for establishing an International Center for Avian Influenza Pandemic Prediction and Prevention (ICAIP3). The mission of the center is to tackle the grand challenges in global health with a focus on avian-influenza pandemic prediction and prevention. The broader impacts of this project include increased international partnerships between researchers, stakeholders, and decision makers; development of STEM workforce for international and convergent research and diverse career paths; increased public scientific literacy and public engagement; and increased capacity for avian influenza pandemic prediction and prevention.

Scientifically, this project focuses on four major problems in pandemic prediction and prevention. First, this project investigates the complex and dynamic data problem via the OneHealth (Human-Animal-Environmental Health) approach and the Big Data approach. Researchers review and assess diverse AIV and HAES datasets and identify the pathways and procedures for harnessing the disparate datasets into analysis-ready datasets for modeling and analysis. Second, this project tackles the multi-scale and interwoven model problem via the integrated modeling approach. Researchers review and assess various AIV models and artificial intelligence (AI) and machine learning (ML) algorithms, and then identify appropriate AIV models and AI/ML algorithms for developing new integrated models to predict AIV evolution, spillover, and transmission. Third, this project assesses the decision support system problem via the structure decision making approach. Researchers review and assess various AIV decision support systems and tools (DSST), and then identify appropriate AIV DSST for further development and practical use in the AIV surveillance, pandemic preparedness, and response. Finally, this project identifies appropriate pathways to develop cyberinfrastructure for data-intensive research, communication, and sharing among the community of zoonotic infectious diseases. The center will be focused on prediction and prevention of AIV pandemic in Eurasia and its potential linkage and risk with North America.

This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).

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.

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