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A group of five undergraduate and two graduate students at Ģ����Ƶ University are proving that groundbreaking research isn’t limited to professional scientists or graduate theses; it can begin in the classroom.

Led by Associate Professor Jixin Chen, students in the Department of Chemistry and Biochemistry’s Physical Chemistry Laboratory course analyzed global COVID-19 data using a new mathematical model developed from their lab module. Their work has now been accepted for publication in , a peer-reviewed journal from the Nature Publishing Group.

The peer-reviewed paper, “Application and Significance of SIRVB Model in Analyzing COVID-19 Dynamics,” was co-authored by undergraduate students Pavithra Ariyaratne, Jonathan S. Ayyash, Tyler M. Kelley, Terry A. Plant-Collins and Logan W. Shinkle, with graduate students Lumbini P. Ramasinghe and Aoife M. Zuercher alongside Professor Jixin Chen.

The research team introduced the SIRVB model, which adds a “Breakthrough” category to the widely used SIRV (Susceptible, Infected, Recovered, Vaccinated) model. This addition accounts for instances where vaccinated or previously infected individuals still contract the virus, offering a more accurate tool for public health forecasting.

The project began as a data analysis module within the physical chemistry lab, typically spanning four three-hour class sessions. Students used real-time COVID-19 data from the to practice kinetic modeling techniques, bridging chemistry concepts with real-world implications. 

Undergraduate student Pavithra Ariyaratne, one of the paper’s co-authors and a teaching assistant for the course, said the collaboration sharpened students’ skills in research design, literature review, writing and teamwork.

“It was a great learning experience for me personally and professionally,” Ariyaratne said. “Most of our students plan to pursue graduate study, so having experience contributing to peer-reviewed research is incredibly valuable.”

Undergraduate co-author Terry Plant-Collins noted that the data-heavy nature of the project challenged his expectations of what chemistry research could look like.

“Chemistry is such a diverse field. This project helped us see that scientists can work with hands-on experiments or take a more theoretical, data-oriented approach,” he said. “Processing and analyzing such a large dataset really helped prepare us for professional work.”

 Fellow graduate student co-author Lumbini P. Ramasinghe agreed, adding that using real-world pandemic data opened her eyes to how science directly informs policy. 

“By calibrating our model to actual COVID-19 cases and vaccination rates, we created something with real-world value,” Ramasinghe said. “Our SIRVB model could help researchers and policymakers better understand the progression of infectious diseases and prepare for future outbreaks.”

While publishing in a peer-reviewed journal isn’t a standard expectation for undergraduates, this experience shows the value of embedding research opportunities into coursework .

Their article, "," was published online on March 12, 2025, in Scientific Reports, volume 15, issue 1.