oes limiting population movement during a pandemic really help to minimize the spread of disease? Ph.D. candidate Keoni Castellano is using math to tackle that question.
His research under mentor Rachidi Salako, an assistant professor in the Department of Mathematical Sciences, explores epidemic models through analysis using mathematical, statistical, and computational tools to study the spread of disease. He specifically looks at the effects of population movement — or limiting movement — on disease persistence.
“It is cool to be able to translate mathematical results, which can often be very abstract, into more tangible outcomes related to the universe around us,” says Castellano. “Mathematics can be used in all sorts of ways to solve different problems.”
Castellano is already the lead author on a major publication in a Q1 journal, which refers to the most prestigious and highly cited journals, and has presented at Auburn and Virginia Tech universities. He is co-founder of the UNLV student chapter of the Society for Applied and Industrial Mathematics. Clearly, Castellano already is making a mark in the world of mathematics early in his career.
What excites you about the research you are doing?
I find it amazing that math can be used in all sorts of different places and solve all sorts of different problems. I know there are a lot of people that like mathematics for the pure enjoyment of the subject, but I have always been intrigued about the way math can be used to give more information about the world that we live in.
In the case of my research, I like how we can use mathematical analysis to poke and prod at a mathematical infectious disease model to gain insight into how infectious diseases behave.
How could your research help provide insight into public health policy in a future pandemic?
The statistician George Box once said, “All models are wrong, but some are useful.” The idea behind this quote is that models are built on assumptions that often have to be justified. Because of these assumptions, the models don’t always give the complete picture over how the real world operates. This is especially true with epidemic models, such as the ones that I study.
Our mathematical results will never be able to predict how a disease will spread with 100% accuracy. However, we do assist in gaining deeper insight into the factors that play a role in how epidemics start and how we can end them once they start. In the future, I would like to be able to incorporate real-world data with our math models to demonstrate if what we see mathematically is also seen in actual epidemics.