Analysis and methodology of inhibiting COVID-19 spread on a university campus

Most of the states in the U.S. are slowly transitioning back to "normal", and educational institutions must weigh in the decision of maintaining the quality of the courses while protecting the health of students in the academic years ahead. We are interested in investigating the circumstances that would help schools stay open during COVID-19, creating safe educational conditions under such a severe situation. Our goal is to move a certain number of courses online to achieve a satisfactory infection rate most efficiently. At the same time, we attempt to maximize the number of face-to-face classroom experiences as most students prefer attending courses on campus over attending them online. In our model, we introduce three parameters to evaluate the risk of every course and determine the most suitable set of courses to be converted online. The parameters include Degree Centrality, Closeness Centrality, and Betweenness Centrality. Those parameters are aggregated in a rectified value. We describe the methodology of our approach and future work, in which we will conduct simulation and sensitivity analyses. © 2021 Copyright for this paper by its authors.