How do scientists make sense of the spread of a disease like COVID-19? In this lecture, we explore the mathematical tools that epidemiologists use to understand and predict outbreaks. We’ll look at survival models, which help estimate how long people remain infectious; differential equations, which track population-level dynamics such as infection waves; and network models, which capture the impact of social connections and gatherings on disease spread. Using data from Ohio’s experience with COVID-19, we will see how mathematics guided public health decisions and what lessons we can carry forward to future epidemics.