World leaders don’t have a crystal ball to help them predict how the pandemic will look in the coming months, but a team of UCF data scientists are working to provide the next best thing.

A group of faculty and graduate students from the departments of Statistics and Data Science and Computer Science  have advanced with 47 other teams to phase two of the Pandemic Response Challenge.

The competition gauges teams’ accuracy predicting COVID-19 infection rates and their ability to grade the efficiency of government policies like screening international travelers and school shutdowns. Teams’ submission could also guide decisions on the quantity and location of vaccine distribution — as well as land them among the winners, who will be awarded $500,000 in prizes by the host, XPRIZE. The organization is a non-profit that promotes technology development through public competitions.

The UCF group’s  methodology — a combination of the latest artificial intelligence and deep-learning models — advanced them through phase one, which started in November last year and ended earlier this month. By Feb. 3, they will complete phase two by submitting intervention plan that may be able to assist regional governments, communities and organizations in reducing impacts from the pandemic.

Predictions based on hard numbers minimizes guesswork and maximizes impact, says Shunpu Zhang, Department of Statistics and Data Sciences chair and professor.

“That’s what big data analytics can solve,” Zhang says. “If you dig deeper through seemingly messy data, you can find some truths.”

Winners will be selected on how close their predictions match against real outcomes and will be announced on Feb. 26.

Contributing expertise from the College of Engineering and Computer Science is Associate Professor Liqiang Wang. The value of the research lies in distinguishing association from causation, Wang says. For instance, determining if there is there a link between a dip in cases and canceling public events or a spike in the numbers and opening up public transportation.

Last year, the UCF team, which also includes graduate students Dongdong Wang and Timothy Sumner, used AI and deep-learning models to conduct COVID-19 case predictions months before entering the competition. The data science group has diligently continued to apply their expertise in this area and  submits their numbers weekly to the Centers for Disease Control and Prevention for inclusion in government forecasts. They are currently pursuing more accurate ways to measure average data.

By Kyle Martin, UCF Today