Impacts of vaccination and asymptomatic testing on SARS-CoV-2 transmission dynamics in a university setting (preprint)

We investigate the impact of vaccination and asymptomatic testing uptake on SARS-CoV-2 transmission in a university student population using a stochastic compartmental model. We find that the magnitude and timing of outbreaks is highly variable under different vaccine uptake levels. With low level interventions (no asymptomatic testing, 30% vaccinated), 53-71% of students become infected during the first term; with high interventions (90% using asymptomatic testing, 90% vaccinated) cumulative incidence is 7-9%, with around 80% of these cases estimated to be asymptomatic. Asymptomatic testing is most useful when vaccine uptake is low: when 30% of students are vaccinated, 90% uptake of asymptomatic testing leads to almost half the case numbers. Under high levels of vaccine uptake (70-90%), case numbers in the student population are largely driven by community importation. Our findings suggest that vaccination is critical for controlling SARS-CoV-2 transmission in university settings with asymptomatic testing being a useful supporting measure.

University students and staff able to maintain low daily contact numbers during various COVID-19 guideline periods

UK universities re-opened in September 2020, amidst the coronavirus epidemic. During the first term, various national social distancing measures were introduced, including banning groups of >6 people and the second lockdown in November;however, outbreaks among university students occurred. We aimed to measure the University of Bristol staff and student contact patterns via an online, longitudinal survey capturing self-reported contacts on the previous day. We investigated the change in contacts associated with COVID-19 guidance periods: post-first lockdown (23/06/2020–03/07/2020), relaxed guidance period (04/07/2020–13/09/2020), ‘rule-of-six’ period (14/09/2020–04/11/2020) and the second lockdown (05/11/2020–25/11/2020). In total, 722 staff (4199 responses) and 738 students (1906 responses) were included in the study. For staff, daily contacts were higher in the relaxed guidance and ‘rule-of-six’ periods than the post-first lockdown and second lockdown. Mean student contacts dropped between the ‘rule-of-six’ and second lockdown periods. For both staff and students, the proportion meeting with groups larger than six dropped between the ‘rule-of-six’ period and the second lockdown period, although was higher for students than for staff. Our results suggest university staff and students responded to national guidance by altering their social contacts. Most contacts during the second lockdown were household contacts. The response in staff and students was similar, suggesting that students can adhere to social distancing guidance while at university. The number of contacts recorded for both staff and students were much lower than those recorded by previous surveys in the UK conducted before the COVID-19 pandemic.

Limits of lockdown: characterising essential contacts during strict physical distancing (preprint)

COVID-19 has exposed health inequalities within countries and globally. The fundamental determining factor behind an individuals risk of infection is the number of social contacts they make. In many countries, physical distancing measures have been implemented to control transmission of SARS-CoV-2, reducing social contacts to a minimum. Characterising unavoidable social contacts is key for understanding the inequalities behind differential risks and planning vaccination programmes. We utilised an existing English longitudinal birth cohort, which is broadly representative of the wider population (n=6807), to explore social contact patterns and behaviours when strict physical distancing measures were in place during the UKs first lockdown in March-May 2020. Essential workers, specifically those in healthcare, had 4.5 times as many contacts as non-essential workers [incident rate ratio = 4.42 (CI95%: 3.88-5.04)], whilst essential workers in other sectors, mainly teaching and the police force had three times as many contacts [IRR = 2.84 (2.58-3.13)]. The number of individuals in a household, which is conflated by number of children, increases essential social contacts by 40%. Self-isolation effectively reduces numbers of contacts outside of the home, but not entirely. Together, these findings will aid the interpretation of epidemiological data and impact the design of effective SARS-CoV-2 control strategies, such as vaccination, testing and contact tracing.

Contact patterns before and during the UK's Autumn 2020 COVID-19 lockdown among university students and staff (preprint)

Introduction Contact patterns are an important determinant of infection transmission. CONQUEST (COroNavirus QUESTionnaire) is an online survey of contacts for University of Bristol (UoB) staff and students. Results from 23/06/2020-24/11/2020 are used to investigate contact patterns among staff/students throughout various UK COVID-19 guidance periods. Methods Responses captured self-reported contacts on the previous day, with COVID-19 guidance periods split: post-first lockdown (23/06/2020-03/07/2020), relaxed guidance period (04/07/2020-13/09/2020), "rule-of-six" period (14/09/2020-04/11/2020), and the second lockdown (05/11/2020-25/11/2020). Results 722 staff (4199 responses) (mean household size: 2.6) and 738 students (1906 responses) (mean household size: 4.5) were included in the study. For staff, median contacts on the previous day were higher in the relaxed guidance and "rule-of-six" periods (3) than the post-lockdown and second lockdown periods (2). Mean contacts were higher during these two middle periods (4-6 per week) than the post-lockdown and second lockdown periods (~3). Mean contacts dropped between the last two periods (5.4 to 3.3), driven by a mean reduction in contacts in non-home/university locations (2.9 to 1.2). Few students responded until October. After 05/10/2020, the median was 2 and the mean was around 6-7 each week, until the second lockdown when it dropped to ~4. Mean number of contacts dropped between the last two periods (6.3 to 4.0) driven by a reduction in all contact types, including UoB contacts (3.5 to 2.5). Discussion The mean and median number of contacts for UoB staff and students were low compared to pre-COVID-19 studies throughout the survey period and lowest during the second lockdown.

Modelling pooling strategies for SARS-CoV-2 testing in a university setting (preprint)

Pre-symptomatic and asymptomatic transmission of SARS-CoV-2 are important elements in the Covid-19 pandemic, and until vaccines are made widely available there remains a reliance on testing to manage the spread of the disease, alongside non-pharmaceutical interventions such as measures to reduce close social interactions. In the UK, many universities opened for blended learning for the 2020-2021 academic year, with a mixture of face to face and online teaching. In this study we present a simulation framework to evaluate the effectiveness of different asymptomatic testing strategies within a university setting, across a range of transmission scenarios. We show that when positive cases are clustered by known social structures, such as student households, the pooling of samples by these social structures can substantially reduce the total cost of conducting RT-qPCR tests. We also note that routine recording of quantitative RT-qPCR results would facilitate future modelling studies.

Contacts and behaviours of university students during the COVID-19 pandemic at the start of the 2020/21 academic year (preprint)

CONQUEST (COroNavirus QUESTionnaire) is an online survey of contacts, behaviour, and COVID-19 symptoms for University of Bristol (UoB) staff/students. We analysed survey results from the start of the 2020/2021 academic year, prior to the second national lockdown (14/09/2020-01/11/2020), where COVID-19 outbreaks led to lockdown of some student halls of residence. The aim of these analyses was to enhance knowledge of student contact patterns to inform infection disease mathematical modelling approaches. Responses captured information on demographics, contacts on the previous day, symptoms and self-isolation during the prior week, and COVID-19 status. 740 students provided 1261 unique records. Of 42 (3%) students testing positive in the prior fortnight, 99% had been self-isolating. The median number of contacts on the previous day was 2 (interquartile range: 1-5), mode: 1, mean: 6.1; 8% had [≥]20 contacts. 57% of student contacts were other UoB students/staff. Most students reported few daily contacts but there was heterogeneity, and some reported many. Around 40% of student contacts were with individuals not affiliated with UoB, indicating potential for transmission to non-students/staff.

High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions (preprint)

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.

COVID-19 transmission in a university setting: a rapid review of modelling studies (preprint)

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.

Collider bias undermines our understanding of COVID-19 disease risk and severity (preprint)

Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly, often from non-random sampling such as hospital admissions, targeted testing or voluntary participation. Here, we highlight the challenge of interpreting observational evidence from such samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.