Assessment of the Effectiveness of Omicron Transmission Mitigation Strategies [...]
Background: Returning universities to full on-campus operations while the COVID-19 pandemic is ongoing has been a controversial discussion in many countries. The risk of large outbreaks in dense course settings is contrasted by the benefits of in-person teaching. Transmission risk depends on a range of parameters, such as vaccination coverage and efficacy, number of contacts and adoption of non-pharmaceutical intervention measures (NPIs). Due to the generalised academic freedom in Europe, many universities are asked to autonomously decide on and implement intervention measures and regulate on-campus operations. In the context of rapidly changing vaccination coverage and parameters of the virus, universities often lack sufficient scientific insight to base these decisions on.
Methods: To address this problem, we analyse a calibrated, data-driven agent-based simulation of transmission dynamics of 13,284 students and 1,482 faculty members in a medium-sized European university. We use a co-location network reconstructed from student enrollment data and calibrate transmission risk based on outbreak size distributions in education institutions. We focus on actionable interventions that are part of the already existing decision-making process of universities to provide guidance for concrete policy decisions.
Results: Here we show that, with the Omicron variant of the SARS-CoV-2 virus, even a reduction to 25% occupancy and universal mask mandates are not enough to prevent large outbreaks given the vaccination coverage of about 85% recently reported for students in Austria.
Conclusions: Our results show that controlling the spread of the virus with available vaccines in combination with NPIs is not feasible in the university setting if presence of students and faculty on campus is required.
J. Lasser, T. Hell, D. Garcia, Assessment of the Effectiveness of Omicron Transmission Mitigation Strategies for European Universities Using an Agent-Based Network Model, Clinical Infectious Diseases (2022) ciac340
This publication was supported by the following project(s):
- WWTF, Project No. WWTF VRG16-005