Application of data-driven measures for impeding COVID-19 spread at an academic institution

Many U.S. universities are embracing the hybrid teaching modality thanks to the start of the COVID- 19 vaccinations and availability of online teaching tools. This work presents a continuation of our previous research, in which we analyzed and developed a methodology to inhibit COVID-19 spread on a university campus. We simulate the virus spread on campus, comparing SIR and SEIR models, and examine how different course policies can affect the number of infected students. We demonstrate that we can achieve a safer environment on campus by moving a certain number of courses with the highest centrality values. Additionally, we analyze how the student flow rate can help reduce the R0 value representing the metric of how many other people an infected individual could infect. This work also presents the simulation analysis of the opened public places on campus and the application of the sensitivity analysis to develop the most efficient approach determining the exact courses that need to be moved online. We conclude with the recommendations and analysis results. © 2021 Copyright for this paper by its authors.