N.C. A&T Professor Part of Team Using Big Data & Artificial Intelligence to Advance Disease Prevention

Suzanne O'Regan

Greensboro, N.C. (Nov. 21, 2017) – Through a new $2 million National Science Foundation grant, North Carolina Agricultural and Technical State University mathematics professor Dr. Suzanne O’Regan, is part of a team of scientists from the Cary institute of Ecosystem Studies and the University of Georgia harvesting the power of machine learning to forecast outbreaks of zoonotic disease.

Each year more than a billion people become infected from Ebola, Zika, SARS, and other pathogens acquired from wildlife, livestock, and other animals. Prevention relies on the ability to predict when and where pathogens are likely to make the leap from animals to people.

O’Regan explains, “By using data that is global in scale, we are seeking to reveal generalizable features of ‘good’ disease carriers. Over 50 life history features are being incorporated into models for most mammal groups.” This includes data on animals’ physical characteristics, metabolic and reproductive rates, range of diet, and timing of daily activity – whether the animal is primarily active during the day, at night, or at dawn and dusk.

Barbara Han, a disease ecologist at the Cary Institute, is leading the five-year study. She explains, “We want to help shift society from a reactive to a proactive approach to managing zoonotic disease. Instead of responding to outbreaks, let’s try to stop them from happening in the first place. Using big data as a potential surveillance tool is an exciting new step toward prevention.”

Funding will enable the team to bring together information on pathogens, potential animal hosts, and environmental factors known to facilitate disease transmission, with the goal of developing innovative methods of mapping when and where the next major zoonotic disease outbreak might occur.

Phase one of the study involves building predictive statistical models that will help the researchers identify traits common among animals that carry disease, and pathogens and parasites that cross the species barrier.

The second subproject will investigate how diseases move dynamically within a system. Once the traits of hosts, pathogens, and their environments – and the relationships among them – are known, the team will incorporate these into mathematical models to reveal how disease dynamics might play out in animal populations over time.

The team also plans to use the models and techniques developed in this project to respond to zoonotic disease outbreaks that might occur during the study.