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

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.


Recommendation?
Major revision is needed (please make suggestions in comments)

Comments to the Author(s) Paper Summary & Contribution
The focus of this paper is on leveraging modeling to inform policy decisions surrounding the pandemic-response and management of higher education in the UK. Specifically, the authors examine COVID-19 case data from the Fall 2020 semester to evaluate the effectiveness of various features of outbreak mitigation used by universities. The authors then use simulation modeling to determine the efficacy and value of return-to-campus interventions such as temporally staggered student arrivals. The main three findings highlighted by the authors include the following: 1.
evidence of spillover transmission between university populations and the wider community in some, but not all, settings; 2.
reductions in adherence to non-pharmaceutical interventions (NPIs) likely have a bigger impact than any marginal benefits from a staggered return to campus; and 3.
the emergence of more transmissible variants reduces the effectiveness of mass asymptomatic testing.

Methodology -
The analysis shown in Figure 3 is interesting. I wonder if the authors could speak to what is driving some of the differences seen in these different regions? Could this be driven by differences across university testing / contact tracing / quarantine efforts? What about the concentration of restaurants / pubs / shopping versus universities that are near largely residential neighborhoods? -For the staggered return to campus analysis, would it be possible to include an analysis of the resources required to accommodate certain return strategies? Since I would imagine this would be one of the key reasons universities decide to stagger student return. Testing and staffing requirements can be costly, and this could be an important factor that administrators consider when making these decisions.

Relevant Context -
It would be really helpful if the authors could add some related literature to place their own analysis and recommendations in the context of other work. There has been a flurry of research activity on COVID-19 policy over the past year and adding some context on whether this analysis is in agreement or disagreement with other work would be incredibly helpful. If the questions and recommendations here are completely novel, that would also be good to know.

Paper Flow -
The flow of the paper currently reads like a lab notebook with various sections for each type of model the authors built. I'm not sure if this is something that can be corrected due to the nature of the work, but perhaps presenting a roadmap in the introduction section that describes the logic behind why the following models will be presented would help the readers orient to what they are about to read. Otherwise, the lack of flow makes it difficult to get through.

Policy Recommendation -
Together, this body of work represents valuable thoughts on return-to-campus planning for university administrators. Despite the limitations of each of the models, this paper could be helpful in guiding administrators on what factors to consider when making these planning decisions.
-Something that could be improved is a more robust discussion of the costs associated with some of these interventions. Though it will undeniably be heterogeneous across universities, currently there is no discussion on the costs associated with some of these interventions, and that can often be a major deciding factor.

Minor Comments -
Please define epidemiological terms such as the secondary attack rate. This would make the paper much more accessible to a non-specialist audience.
-Consider presenting R-squared values in your regression tables.

Recommendation -
The paper addresses a timely question at the intersection of science, society, and policy. Though the policy recommendations made by the authors are not individually rigorous enough to be directly implemented, the models put together offer valuable insights for administrators who find themselves with this planning problem. I have included several recommendations on areas where the paper could be improved, and recommend a major revision so the authors might have a chance to make these changes. I wish the authors well and look forward to seeing an improved version of this paper in the future.

Decision letter (RSOS-210310.R0)
We hope you are keeping well at this difficult and unusual time. We continue to value your support of the journal in these challenging circumstances. If Royal Society Open Science can assist you at all, please don't hesitate to let us know at the email address below.

Dear Dr Tildesley
On behalf of the Editors, we are pleased to inform you that your Manuscript RSOS-210310 "SARS-COV-2 INFECTION IN UK UNIVERSITY STUDENTS: LESSONS FROM SEPTEMBER-DECEMBER 2020 AND MODELLING INSIGHTS FOR FUTURE STUDENT RETURN" has been accepted for publication in Royal Society Open Science subject to minor revision in accordance with the referees' reports. Please find the referees' comments along with any feedback from the Editors below my signature.
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Methodology -The analysis shown in Figure 3 is interesting. I wonder if the authors could speak to what is driving some of the differences seen in these different regions? Could this be driven by differences across university testing / contact tracing / quarantine efforts? What about the concentration of restaurants / pubs / shopping versus universities that are near largely residential neighborhoods? -For the staggered return to campus analysis, would it be possible to include an analysis of the resources required to accommodate certain return strategies? Since I would imagine this would be one of the key reasons universities decide to stagger student return. Testing and staffing requirements can be costly, and this could be an important factor that administrators consider when making these decisions.

Relevant Context
-It would be really helpful if the authors could add some related literature to place their own analysis and recommendations in the context of other work. There has been a flurry of research activity on COVID-19 policy over the past year and adding some context on whether this analysis is in agreement or disagreement with other work would be incredibly helpful. If the questions and recommendations here are completely novel, that would also be good to know.
Paper Flow -The flow of the paper currently reads like a lab notebook with various sections for each type of model the authors built. I'm not sure if this is something that can be corrected due to the nature of the work, but perhaps presenting a roadmap in the introduction section that describes the logic behind why the following models will be presented would help the readers orient to what they are about to read. Otherwise, the lack of flow makes it difficult to get through.
Policy Recommendation -Together, this body of work represents valuable thoughts on return-to-campus planning for university administrators. Despite the limitations of each of the models, this paper could be helpful in guiding administrators on what factors to consider when making these planning decisions.
-Something that could be improved is a more robust discussion of the costs associated with some of these interventions. Though it will undeniably be heterogeneous across universities, currently there is no discussion on the costs associated with some of these interventions, and that can often be a major deciding factor.
Minor Comments -Please define epidemiological terms such as the secondary attack rate. This would make the paper much more accessible to a non-specialist audience.
-Consider presenting R-squared values in your regression tables.

Recommendation
-The paper addresses a timely question at the intersection of science, society, and policy. Though the policy recommendations made by the authors are not individually rigorous enough to be directly implemented, the models put together offer valuable insights for administrators who find themselves with this planning problem. I have included several recommendations on areas where the paper could be improved, and recommend a major revision so the authors might have a chance to make these changes. I wish the authors well and look forward to seeing an improved version of this paper in the future.

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We would like to thank the editor and the reviewer for the comments on our manuscript. Below we provide a response to the reviewer comments and indicate the changes that we have made to the manuscript.
Reviewer 1 comments and response (in bold): Figure 3 is interesting. I wonder if the authors could speak to what is driving some of the differences seen in these different regions? Could this be driven by differences across university testing / contact tracing / quarantine efforts? What about the concentration of restaurants / pubs / shopping versus universities that are near largely residential neighborhoods? Table 4 and Figure 2 Figure 3 is beyond the scope of this paper, as these were chosen as general illustrations of the types of spillover effects that might be observed.

Additional text in the manuscript [in results part 2.4.2 after "interaction in different local authorities"]: "Considerations such as the severity of imposed NPIs, magnitude of student body, and uptake and efficacy of testing, tracing, and quarantining measures likely all influence the overall results, but their individual contributions are not identifiable in this analysis."
Appendix A 2 -For the staggered return to campus analysis, would it be possible to include an analysis of the resources required to accommodate certain return strategies? Since I would imagine this would be one of the key reasons universities decide to stagger student return. Testing and staffing requirements can be costly, and this could be an important factor that administrators consider when making these decisions.

Relevant Context
3 -It would be really helpful if the authors could add some related literature to place their own analysis and recommendations in the context of other work. There has been a flurry of research activity on COVID-19 policy over the past year and adding some context on whether this analysis is in agreement or disagreement with other work would be incredibly helpful. If the questions and recommendations here are completely novel, that would also be good to know.
We agree that this is important and have added in additional paragraphs in the conclusion which compare our results to other literature: "The mass migration of students at the beginning and end of academic terms, their unique living arrangements during term time and unique patterns of social mixing, make them an important population for the spread of infectious respiratory illnesses. Despite this, prior to the COVID-19 pandemic, there was little data collected on outbreaks of infectious disease at universities (although one such dataset collected between October 2007 and mid-February 2008 has now been published in 2021 \cite{Eames2021}) and university students were an understudied population. Therefore, at the start of the pandemic there was a limited evidence base to support policy decisions around universities. Our study brings together expertise from multiple research groups and presents results from multiple statistical and modelling analyses and provides new understanding on infectious disease outbreaks at universities and how these could be mitigated.
An important finding of our study is that adherence to NPIs is likely to have more impact than staggering the return of students to university. Survey data suggest that in the autumn term of 2020, students generally did have high adherence to NPIs; an Office for National Statistics (ONS) survey found high adherence (90\%) to social distancing across multiple universities \cite{ONSsurvey}. In addition, a survey of University of Bristol students found that 99\% of students self-isolated after testing positive for COVID-19 and the majority of survey participants reported low contact numbers \cite{Nixon2021}. However, there was heterogeneity in adherence, with some students reporting many contacts and with only 61\% of students with cardinal COVID-19 symptoms self-isolating \cite{Nixon2021}. In future, it will be important that students maintain their high levels of adherence and to ensure they have sufficient resources to allow them to do so.
Several of the scenarios presented here have considered the frequency of asymptomatic screening at universities. This has been explored in other modelling studies, for example \cite{Lopman2021} found that monthly screening can reduce cumulative incidence by 59\% and weekly screening by 87\%. We found that increasing the frequency of asymptomatic screening is likely to be important in the presence of a more transmissible SARS CoV-2 variant, with cases only being able to be maintained below 1200 (mean cumulative over 100 days) when testing occurs every 3 days (in a population of 25,000). This finding corroborates with a study that used an agent based model to simulate COVID-19 transmission at the University of California San Diego, where larger outbreaks resulted in a maximum outbreak size of 158 when asymptomatic screening occurred monthly and 7 when it occurred twice weekly \cite{Goyal2021}, but with a much lower impact seen on the average outbreak size when increasing from monthly to twice weekly testing, ranging from 1.9 to 1.1 respectively. Brooks Pollock et al. \cite{Brooks-Pollock2020} also found that mass testing was more effective for higher values of the reproduction number. This highlights the importance of reassessing control measures under different variants.
We have focused here on COVID-19 risks and mitigation strategies for when students return to university and during the university term itself, however, we have covered little on the risk of transmission from infected students to private homes at the end of term. Previous modelling work suggests that in an unvaccinated population, an infectious student would on average generate just less than one secondary within-household infection, but this is dependent on the prevalence in the student population at the time of departure \cite{Harper2021}. Although it is expected that vaccination will reduce the impact of students returning to private homes at the end of term, the UK vaccination program is ongoing and there are particular spatial areas and demographic groups where low uptake is expected \cite{deFigueiredo2020.12.17.20248382}, suggesting that this still may be an important question to consider in future." We have also added in section 3.5.3 "In addition, the model did not include a reduction in the risk of transmission occurring over contacts due to face covering use or social distancing, however other work ~\cite{Hambridge2021} suggests that if such measures are in place in a university setting and/or if there are moderate levels of immunity, the impact of testing is less prominent, highlighting the importance of considering testing in the context of other measures." In the conclusion we also have added in "For example, to sustain a regular testing regime at universities under financial, logistical, or structural constraints, mathematical modelling suggests that pooling RT-qPCR testing may be a cost-effective method, although this may come with additional caveats resulting from the associated reduction in sensitivity (when cases are not detected) and sensitivity (when students self-isolate but are not infected) \cite{Hemani2021}."

Paper Flow
4 -The flow of the paper currently reads like a lab notebook with various sections for each type of model the authors built. I'm not sure if this is something that can be corrected due to the nature of the work, but perhaps presenting a roadmap in the introduction section that describes the logic behind why the following models will be presented would help the readers orient to what they are about to read. Otherwise, the lack of flow makes it difficult to get through.
We thank the reviewer for this comment. We have gone through the paper and reorganised the introduction and conclusion sections to include the core results and a roadmap for the paper (with a figure) has been included in the introduction. We believe that this significantly helps to improve the flow of the paper.
5 -Together, this body of work represents valuable thoughts on return-to-campus planning for university administrators. Despite the limitations of each of the models, this paper could be helpful in guiding administrators on what factors to consider when making these planning decisions.
We would like to thank the reviewer for this comment. We agree that this work can help to inform administrators regarding future planning decisions and this group are currently working towards an approach to disseminate these findings to the wider higher education sector.