SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return

Enright, J. et al. (2021) SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return. Royal Society Open Science, 8(8), 210310. (doi: 10.1098/rsos.210310) (PMID:34386249) (PMCID:PMC8334840)

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In this paper we present work on SARS-CoV-2 transmission in UK higher education settings using multiple approaches to assess the extent of university outbreaks, how much those outbreaks may have led to spillover in the community, and the expected effects of control measures. Firstly, we found that the distribution of outbreaks in universities in late 2020 were consistent with the expected importation of infection from arriving students. Considering outbreaks at one university, larger halls of residence posed higher risks for transmission. The dynamics of transmission from university outbreaks to wider communities is complex, and while sometimes spillover does occur, occasionally even large outbreaks do not give any detectable signal of spillover to the local population. Secondly, we explored proposed control measures for reopening and keeping open universities. We found the proposal of staggering the return of students to university residence is of limited value in terms of reducing transmission. We show that student adherence to testing and self-isolation are likely to be much more important for reducing transmission during term time. Finally we explored strategies for testing students in the context of a more transmissible variant and found that frequent testing would be necessary to prevent a major outbreak.

Item Type:Articles
Additional Information:The authors would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support during the programme Infectious Dynamics of Pandemics where work on this paper was undertaken. This work was supported by EPSRC grant no EP/R014604/1. KJB acknowledges support from a University of Nottingham Anne McLaren Fellowship. ELF acknowledges support via KJB’s fellowship and the Nottingham BBSRC Doctoral Training Partnership. MLT was supported by the UK Engineering and Physical Sciences Research Council [grant number EP/N509620/1]. EBP, EJN, LD, JRG and MJT were supported by UKRI through the JUNIPER modelling consortium [grant number MR/V038613/1]. EMH, LD and MJT were supported by the Medical Research Council through the COVID-19 Rapid Response Rolling Call [grant number MR/V009761/1]. HBS is funded by the Wellcome Trust and the Royal Society [grant number 202562/Z/16/Z]. JE is partially funded by the UK Engineering and Physical Sciences Research Council [grant number EP/T004878/1].
Keywords:Epidemic modelling, pandemic modelling, COVID-19, SARS-CoV-2, higher education.
Glasgow Author(s) Enlighten ID:Enright, Dr Jessica
Creator Roles:
Enright, J.Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Writing – review and editing, Visualization
Authors: Enright, J., Hill, E. M., Stage, H. B., Bolton, K. J., Nixon, E. J., Fairbanks, E. L., Tang, M. L., Brooks-Pollock, E., Dyson, L., Budd, C. J., Hoyle, R. B., Schewe, L., Gog, J. R., and Tildesley, M. J.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Royal Society Open Science
Publisher:The Royal Society
ISSN (Online):2054-5703
Published Online:04 August 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Royal Society Open Science 2021 8(8):210310
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
305944Multilayer Algorithmics to Leverage Graph StructureKitty MeeksEngineering and Physical Sciences Research Council (EPSRC)EP/T004878/1M&S - Statistics