Controlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings.

Background: Re-opening universities while controlling COVID-19 transmission poses unique challenges. UK universities typically host 20,000 to 40,000 undergraduate students, with the majority moving away from home to attend. In the absence of realistic mixing patterns, previous models suggest that outbreaks associated with universities re-opening are an eventuality. Methods: We developed a stochastic transmission model based on realistic mixing patterns between students. We evaluated alternative mitigation interventions for a representative university. Results: Our model predicts, for a set of plausible parameter values, that if asymptomatic cases are half as infectious as symptomatic cases then 5,760 (3,940 - 7,430) out of 28,000 students, 20% (14% - 26%), could be infected during the first term, with 950 (656 - 1,209) cases infectious on the last day of term. If asymptomatic cases are as infectious as symptomatic cases then three times as many cases could occur, with 94% (93% - 94%) of the student population getting infected during the first term. We predict that one third of infected students are likely to be in their first year, and first year students are the main drivers of transmission due to high numbers of contacts in communal residences. We find that reducing face-to-face teaching is likely to be the single most effective intervention, and this conclusion is robust to varying assumptions about asymptomatic transmission. Supplementing reduced face-to-face testing with COVID-secure interactions and reduced living circles could reduce the percentage of infected students by 75%. Mass testing of students would need to occur at least fortnightly, is not the most effective option considered, and comes at a cost of high numbers of students requiring self-isolation. When transmission is controlled in the student population, limiting imported infection from the community is important. Conclusions: Priority should be given to understanding the role of asymptomatic transmission in the spread of COVID-19. Irrespective of assumptions about asymptomatic transmission, our findings suggest that additional outbreak control measures should be considered for the university setting. These might include reduced face-to-face teaching, management of student mixing and enhanced testing. Onward transmission to family members at the end of term is likely without interventions.

Managing COVID−19 within a university setting presents unique challenges. At the start of term, students arrive from geographically diverse locations and potentially have higher numbers of social contacts than the general population, particularly if living in university halls of residence accommodation. Mathematical models are useful tools for understanding the potential spread of infection and are being actively used to inform policy about the management of COVID−19. Our aim was to provide a rapid review and appraisal of the literature on mathematical models investigating COVID−19 infection in a university setting. We searched PubMed, Web of Science, bioRxiv/ medRxiv and sought expert input via social media to identify relevant papers. BioRxiv/ medRxiv and PubMed/Web of Science searches took place on 3 and 6 July 2020, respectively. Papers were restricted to English language. Screening of peer−reviewed and pre−print papers and contact with experts yielded five relevant papers − all of which were pre−prints. All models suggest a significant potential for transmission of COVID−19 in universities. Testing of symptomatic persons and screening of the university community regardless of symptoms, combined with isolation of infected individuals and effective contact tracing were critical for infection control in the absence of other mitigation interventions. When other mitigation interventions were considered (such as moving teaching online, social/physical distancing, and the use of face coverings) the additional value of screening for infection control was limited. Multiple interventions will be needed to control infection spread within the university setting and the interaction with the wider community is an important consideration. Isolation of identified cases and quarantine of contacts is likely to lead to large numbers of students requiring educational, psychological and behavioural support and will likely have a large impact on the attendance of students (and staff), necessitating online options for teaching, even where in−person classes are taking place. Models were highly sensitive to assumptions in the parameters, including the number and type of individuals contacts, number of contacts traced, frequency of screening and delays in testing. Future models could aid policy decisions by considering the incremental benefit of multiple interventions and using empirical data on mixing within the university community and with the wider community where available. Universities will need to be able to adapt quickly to the evolving situation locally to support the health and wellbeing of the university and wider communities.

University students have unique living, learning and social arrangements which may have implications for infectious disease transmission. To address this data gap, we created CONQUEST (COroNavirus QUESTionnaire), a longitudinal online survey of contacts, behaviour, and COVID-19 symptoms for University of Bristol (UoB) staff/students. Here, we analyse results from 740 students providing 1261 unique records from the start of the 2020/2021 academic year (14/09/2020–01/11/2020), where COVID-19 outbreaks led to the self-isolation of all students in some halls of residences. Although most students reported lower daily contacts than in pre-COVID-19 studies, there was heterogeneity, with some reporting many (median = 2, mean = 6.1, standard deviation = 15.0; 8% had ≥ 20 contacts). Around 40% of students’ contacts were with individuals external to the university, indicating potential for transmission to non-students/staff. Only 61% of those reporting cardinal symptoms in the past week self-isolated, although 99% with a positive COVID-19 test during the 2 weeks before survey completion had self-isolated within the last week. Some students who self-isolated had many contacts (mean = 4.3, standard deviation = 10.6). Our results provide context to the COVID-19 outbreaks seen in universities and are available for modelling future outbreaks and informing policy.

HighlightsIncreased sheep scab prevalence is often blamed on UK farmers not using prophylaxis.A Game Theory model is developed to assess whether prophylaxis is cost-effective.Prophylaxis is economical with high regional prevalence and low treatment costs.Currently, reactive treatment is the most cost-effective strategy for most farmers.The implications for future policy on scab control are discussed.