This research project uses location data gathered from WiFi access points on campus to model the mobility patterns of students in order to inform the planning of educational activities that can minimize the transmission risk.
The first aim is to understand the general mobility patterns of students on campus to identify physical spaces associating with a high-risk of transmission. For example, we can extract insights from WiFi data about which locations are the busiest during which time of the day, how much time was typically spent at each location, and how do these mobility patterns change over time. The second aim is to understand how students share the same physical spaces on campus (e.g. attending a lecture, meeting in the same room, sharing the same dorm). Students are presumably in a close proximity when they are connected to the same WiFi access point. We model a student-to-student network from their co-location activities and use its network centrality measures as proxies of transmission risk (i.e. students in the center of a network would have a higher chance of getting exposed to COVID-19 than those in the periphery). We then correlate network centrality measures with academic information (e.g. class schedule, course enrollment, study major, year of study, gender, ethnicity) to determine whether certain features of the academic record are related to transmission risk. For example, we can identify which groups of students are more vulnerable to potential infections by associating with a high network centrality. Insights from this research project will inform the University of Michigan’s strategies for the recovery of educational activities post-pandemic with empirical evidence of students’ mobility pattern on campus as well as factors that associate with a high-risk of transmission.