Dr. Xiunan Wang served as lead author for a recent paper published in the prestigious SIAM Journal on Applied Mathematics, in which she proposed a novel discrete inverse method for estimating the transmission rates of infectious diseases. Photo by Angela Foster.
University of Tennessee at Chattanooga Department of Mathematics Assistant Professor Xiunan Wang is drawing international recognition for her work in mathematical modeling to forecast the spread of infectious diseases.
Wang, who joined UTC in August 2021 and specializes in mathematical biology, has been garnering significant attention in North America and abroad. Her innovative research was recently highlighted in an article by the Society for Industrial and Applied Mathematics (SIAM), one of the three major U.S. math organizations with international memberships.
She has served as lead author for a recent paper published in the prestigious SIAM Journal on Applied Mathematics, in which she proposed a novel discrete inverse method for estimating the transmission rates of infectious diseases.
“Her research is getting a good deal of press coverage, including outlets like the CBC in Canada and BioSpace in the United States,” said Dr. Chris Cox, head of the UTC Department of Mathematics. “To get an article published in one of the SIAM journals is a big deal.”
Originally from Tieling, a city in northeastern China, Wang said she developed her passion for applied mathematics early on during her undergraduate studies at Southwest University in Chongqing, China—where she earned a bachelor’s degree in mathematics and applied mathematics and a master’s degree in applied mathematics.
“In my second year, I attended a mathematical modeling contest and won a prize,” she recalled. “I collaborated with a team of my classmates and found it very interesting.”
Wang then traveled to Canada to continue her education. She received her Ph.D. in applied mathematics from Memorial University of Newfoundland in 2017 and then spent one year as a postdoctoral researcher at the University of Western Ontario.
She started her postdoc fellowship at the University of Alberta in September 2018 and was there nearly three years before coming to UTC.
“At the University of Alberta, I broadened my research area, including not only epidemiology but also ecology,” she said, “because there are some deeper connections between epidemiology and ecology.”
Dr. Xiunan Wang, who joined the UTC faculty in 2021, specializes in mathematical biology.
Wang’s research took on new importance in 2020 with the onset of the COVID-19 pandemic.
“We had an urgent need to forecast how many people are infected each day in the United States or Canada,” she said. “We developed a hybrid method combining the strengths of differential equations and machine learning to forecast how many people were infected each day.”
The forecast, called the “discrete inverse method,” served as a bridge between differential equations and machine learning.
She collaborated with University of Alberta Professor Hao Wang, the Canada Research Chair (Tier 1) in Mathematical Biosciences and director of the Interdisciplinary Lab for Mathematical Ecology and Epidemiology, and applied the method to forecast transmission rates of various infectious diseases—including COVID-19, influenza and measles.
The researchers showed that the properties of transmissibility and number of infections can be quite different.
“Usually the data on the website, given by the CDC or some health department, is about how many people are infected each day, but they never tell the value of the transmission rates,” Wang said. “The value of transmission rates are not observable and cannot be recorded—and that is where we, as mathematicians, can help.
“The forecast is quite accurate. Hopefully that can provide some guidance for policymakers to design some control measures for the disease.”
Ironically, Wang’s arrival at UTC was delayed by a year due to pandemic-related visa issues.
“Maybe it was not good for me to have delays to start here,” Wang said, “but the good thing is … the one more year that I stayed at the University of Alberta, I learned new methods, including machine learning. That was the first time that I had access to it.”
Wang has quickly made her mark since arriving on campus, Cox said.
“Her contributions have been recognized both locally and internationally and she has published 24 peer-reviewed papers,” Cox said. “To have 24 papers published in refereed journals … referee journals have very high standards. For many faculty, that’s a lifetime of work. To be honest with you, that’s about how many papers I have published.”
Cox has 40 years of higher education experience.
“But it’s more than just the research. Xiunan’s done a great job teaching and the students like her a lot. They recognize that she cares about them and she’s just a very warm individual,” Cox continued. “She’s basically firing on all cylinders for what we expect of a faculty member. We are very fortunate to have Xiunan at UTC.”
Deborah Barr, administrative specialist for the Department of Mathematics, echoed those sentiments.
“Being an only child, she lights up when we talk about her family and culture,” Barr said. “She is incredibly observant and intelligent. She always brings a thoughtful perspective to our conversations and I love how her curious, research-driven mind shines.”
Learn more
Discrete Inverse Method for Extracting Disease Transmission Rates from Accessible Infection Data
