In addition to disrupting about every aspect of normal life, the COVID-19 epidemic has brought unprecedented attention to the importance of mathematical modeling and data analysis. The tools needed to understand and predict this epidemic run the gamut from differential equations and large simulations, with methods coming from statistics and applied mathematics. Data are noisy and complicated, and raise many questions about the challenges of counting cases, tracking their sources, understanding viral spread, and quantifying stresses on the health care system and the economy.
We will access the vast quantity of available data, and use them to study the spread and genetics of this virus. Recent studies have shown that the spike protein, that lives on the outside of the virus and is critical for it to enter cells, has mutated in ways that might affect its ability to infect people.
Our SRI team will take an interdisciplinary approach to this aspect of the pandemic. Students will learn the skills needed to download and visualize genetic data using R and python, link these data with fundamental mathematical models of epidemiology, evolution, and the physics of viral entry. Working in teams, we'll investigate hypotheses about the causes consequences of viral evolution, and learn to effectively communicate and display these results to audiences ranging from scientists and decision-makers to the general public.