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

Jessica Enright*, Edward Hill, Helena Stage, Kirsty Bolton, Emily J Nixon, Emma Fairbanks, Maria Tang, Ellen Brooks Pollock, Louise Dyson, Chris Budd, Rebecca Hoyle, Lars Schewe, Julia Gog, Michael Tildesley

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

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 was 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 is 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.
Original languageEnglish
Article number210310
Number of pages37
JournalRoyal Society Open Science
Volume8
Issue number8
DOIs
Publication statusPublished - 4 Aug 2021

Bibliographical note

Funding Information:
acknowledges support from a University of Nottingham Anne McLaren Fellowship. E.L.F. acknowledges support via K.J.B.’s fellowship and the Nottingham BBSRC Doctoral Training Partnership. M.L.T. was supported by the UK Engineering and Physical Sciences Research Council (grant no. EP/N509620/1). E.B.-P., E.J.N., L.D., J.R.G. and M.J.T. were supported by UKRI through the JUNIPER modelling consortium (grant no. MR/V038613/1). E.M.H., L.D. and M.J.T. were supported by the Medical Research Council through the COVID-19 Rapid Response Rolling Call (grant no. MR/V009761/1). H.B.S. is funded by the Wellcome Trust and the Royal Society (grant no. 202562/ Z/16/Z). J.E. is partially funded by the UK Engineering and Physical Sciences Research Council (grant no. EP/ T004878/1). Acknowledgements. The authors 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.

Funding Information:
Data accessibility. This article has no additional data. Authors’ contributions. J.E. conceptualization, methodology, software, formal analysis, investigation, writing—original draft, review and editing, visualization. E.M.H. conceptualization, methodology, software, formal analysis, investigation, writing—original draft, review and editing, visualization. H.B.S. conceptualization, methodology, software, formal analysis, investigation, writing—original draft, review and editing, visualization. K.J.B. conceptualization, methodology, investigation, data curation, writing—original draft, review and editing, visualization, supervision. E.J.N. conceptualization, methodology, software, formal analysis, investigation, writing—original draft, review and editing, visualization. E.L.F. conceptualization, methodology, software, formal analysis, investigation, data curation, writing—original draft, review and editing, visualization. M.L.T. conceptualization, investigation, writing—original draft, review and editing. E.B.-P. conceptualization, software, review and editing, supervision. L.D. conceptualization, review and editing, supervision. C.J.B. conceptualization, methodology, software, formal analysis, investigation, writing—original draft, review and editing, visualization. R.B.H. conceptualization, methodology, software, formal analysis, investigation, writing—original draft, review and editing, visualization. L.S. conceptualization, methodology, software, formal analysis, investigation, writing—original draft, review and editing, visualization. J.R.G. conceptualization, writing—original draft, review and editing, supervision, project management. M.J.T. conceptualization, methodology, formal analysis, investigation, writing—original draft, review and editing, supervision, project management. Competing interests. We declare we have no competing interests. Funding. This work was supported by EPSRC grant no EP/R014604/1. The authors would also like to thank the Virtual Forum for Knowledge Exchange in Mathematical Sciences (V-KEMS) for the support during the workshop Unlocking higher education Spaces – What Might Mathematics Tell Us? where work on this paper was undertaken. K.J.B.

Publisher Copyright:
© 2021 The Authors.

Keywords

  • epidemic modelling
  • pandemic modelling
  • COVID-19
  • SARS-CoV-2
  • higher education

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