BACKGROUND: University students have higher average number of contacts than the general population. Students returning to university campuses may exacerbate COVID-19 dynamics in the surrounding community.
METHODS: We developed a dynamic transmission model of COVID-19 in a mid-sized city currently experiencing a low infection rate. We evaluated the impact of 20,000 university students arriving on September 1 in terms of cumulative COVID-19 infections, time to peak infections, and the timing and peak level of critical care occupancy. We also considered how these impacts might be mitigated through screening interventions targeted to students.
RESULTS: If arriving students reduce their contacts by 40% compared to pre-COVID levels, the total number of infections in the community increases by 115% (from 3,515 to 7,551), with 70% of the incremental infections occurring in the general population, and an incremental 19 COVID-19 deaths. Screening students every 5 days reduces the number of infections attributable to the student population by 42% and the total COVID-19 deaths by 8. One-time mass screening of students prevents fewer infections than 5-day screening, but is more efficient, requiring 196 tests needed to avert one infection instead of 237.
INTERPRETATION: University students are highly inter-connected with the surrounding off-campus community. Screening targeted at this population provides significant public health benefits to the community through averted infections, critical care admissions, and COVID-19 deaths.
Bibliographical noteFunding Information:
This work was supported in part by the Gordon and Betty Moore Foundation through Grant GBMF9634 to Johns Hopkins University to support the work of the Society for Medical Decision Making COVID-19 Decision Modeling Initiative (co-PIs: Cipriano and Enns) and by a Western University Catalyst Research Grant (PI: Cipriano [Grant #R5171A06]). LEC is supported by the David G. Burgoyne Faculty Fellowship. GSZ is supported by the J. Allyn Taylor and Arthur H. Mingay Chair in Management Science. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
© 2021 Cipriano et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Mass Screening
- Models, Theoretical
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't