Mathematical modeling has been a powerful tool for policy makers struggling with COVID-19 to help predict how targeted actions can impact infection rates, minimize risk of exposure, increase recovery rates, And much more. Fifteen seniors who took the main mathematics synthesis course this spring put their modeling skills to the test to help policymakers evaluate past policies and predict future outcomes.
The course was taught by Associate Professor Kara Maki and Associate Professor Michael Cromer, but the School of Mathematical Sciences faculty also brought in outside experts to help guide the work. Jennifer Schneider, Eugene H. Fram Chair in Applied Critical Thinking and Professor in the Department of Civil Engineering Technologies, Environmental Management and Safety, and Suronda Gonzalez, Executive Director of Upstate New York College Collaboration, were our clients during the semester.
The two clients advised universities on COVID-19 policies and asked students to examine three central issues: what conclusions can be drawn from past experiences; how can colleges and universities prepare for the fall semester? and what is the impact of COVID on the community? Using the SEIR epidemic model, which is commonly used to predict how many people are susceptible, exposed, infected, and recovered from viral outbreaks, students delved into the issues and presented their findings at the end of the semester to clients. .
“The students have done a spectacular job and I am very happy,” said Schneider, who heads the Collaboratory for Resiliency and Recovery. “These are difficult questions and it can be difficult to put the data together, but they produced some really interesting results. Every policy is a compromise, but they did a good job of helping to analyze the benefits and costs. “
One group looked at unemployment caused by COVID-19. The group noted that generally, when unemployment rises, wages fall, resulting in lower demand. This in turn leads to lost profits for companies and the whole chain has an aggravating effect which leads to increased unemployment. But interestingly, they found that unemployment caused by the pandemic had not created a snowball effect, which the group said could be due to the government artificially raising incomes. personal with things like stimulus checks or subsidized costs to businesses through initiatives like the Paycheck Protection Program.
Jordan Kiel, a third-year computer math student from Mount Prospect, Ill., Was part of that group and said he enjoys working for clients and leveraging their expertise to solve a practical problem.
“They have a different body of knowledge than ours, especially since they’ve been working on things related to COVID for much longer,” Kiel said. “It was really cool because they have experience with COVID, they could tell us about little things to look for.”
Chris Piccoli, a fourth-year computer math student from Pittsburgh, Pa., Worked on a project that looked at which policies were having the most effect in limiting the spread of COVID-19. He said that in addition to honing his mathematical modeling skills, the project pushed him to improve his coding, communication and leadership skills. But he joked that he had gained knowledge that he hopes he won’t have to apply for in the future.
“I now know all of these things about a horrible event that happened and I hope I never have to use it again,” Piccoli said.