Mathematical models of COVID-19 vaccination in high-income countries: A systematic review (preprint)

Objectives Since COVID-19 first emerged in 2019, mathematical models have been developed to predict transmission and provide insight into disease control strategies. A key research need now is for models to inform long-term vaccination policy. We aimed to review the variety of existing modelling methods, in order to identify gaps in the literature and highlight areas for future model development. Study design This study was a systematic review. Methods We searched PubMed, Embase and Scopus from 1 January 2019 to 6 February 2023 for peer-reviewed, English-language articles describing age-structured, dynamic, mathematical models of COVID-19 transmission and vaccination in high-income countries that include waning immunity or reinfection. We extracted details of the structure, features and approach of each model and combined them in a narrative synthesis. Results Of the 1109 articles screened, 47 were included. Most studies used deterministic, compartmental models set in Europe or North America that simulated a time horizon of 3.5 years or less. Common outcomes included cases, hospital utilisation and deaths. Only nine models included long COVID, costs, life-years or quality of life-related measures. Two studies explored the potential impact of new variants beyond Omicron. Conclusions This review demonstrates a need for long-term models that focus on outcome measures such as quality-adjusted life years, the population-level effects of long COVID and the cost-effectiveness of future policies -- all of which are essential considerations in the planning of long-term vaccination strategies.

Mechanistic models of humoral kinetics following COVID-19 vaccination (preprint)

Introduction: Future COVID-19 vaccine programmes need to take into account the variable responses elicited by different vaccines and their waning protection over time. Existing descriptions of antibody response to COVID-19 vaccination convey limited information about the mechanisms of antibody production and maintenance. Methods: We describe the antibody dynamics elicited by COVID-19 vaccination with two biologically-motivated mathematical models of antibody production by plasma cells and subsequent decay. We fit the models using Markov Chain Monte Carlo to seroprevalence data from 14,602 uninfected individuals collected via the primary care network in England between May 2020 and September 2022. We ensure our models are structurally and practically identifiable when using anti- body data alone. We analyse the effect of age, vaccine type, number of doses, and the interval between doses on antibody production and longevity of response. Results: We find evidence that individuals over 35 years of age who received a second dose of ChAdOx1-S generate a persistent antibody response suggestive of long-lived plasma cell induction, while individuals that receive two doses of BNT162b2, or one dose of either vaccine do not. We also find that plasamblast productive capacity, the likely driver of short-term antibody responses, is greater in younger people than older people (≤ 4.5 fold change in point estimates), people vaccinated with two doses than people vaccinated with one dose (≤ 12 fold change), and people vaccinated with BNT162b2 than people vaccinated with ChAdOx1-S (≤ 440 fold change). The effect of age on antibody dynamics is more pronounced in people vaccinated with BNT162b2 than people vaccinated with ChAdOx1-S. We find the half-life of an antibody to be between 23 - 106 days. Conclusion: Routinely-collected seroprevalence data are a valuable source of information for characterising within-host mechanisms of antibody production and persistence. Extended sampling and linking seroprevalence data to outcomes would allow for powerful conclusions about how humoral kinetics protect against disease.

Evaluation of isotype specific salivary antibody assays for detecting previous SARS-CoV-2 infection in children and adults (preprint)

Saliva is easily obtainable non-invasively and potentially suitable for detecting both current and previous SARS-CoV-2 infection. We established 6 standardised enzyme linked immunosorbent assays (ELISA) capable of detecting IgA and IgG antibodies to whole SARS-CoV-2 spike protein, to its receptor binding domain region and to nucleocapsid protein in saliva. In test accuracy (n=320), we found that spike IgG performed best (ROC AUC: 95.0%, 92.8-97.3%), followed by spike IgA (ROC AUC: 89.9%, 86.5-93.2%) for discriminating between pre-pandemic and post COVID-19 saliva samples. Using machine learning, diagnostic performance was improved when a combination of tests was used. As expected, salivary IgA was poorly correlated with serum, indicating an oral mucosal response whereas salivary IgG responses were predictive of those in serum. When deployed to 20 household outbreaks undergoing Delta and Omicron infection, antibody responses were heterogeneous but remained a reliable indicator of recent infection. Intriguingly, unvaccinated children showed evidence of exposure almost exclusively through specific IgA responses in the absence of evidence of viral infection. We have provided robust standardisation, evaluation, and field-testing of salivary antibody assays as tools for monitoring SARS-CoV-2 immune responses. Future work should focus on investigating salivary antibody responses following infection and vaccination to understand patterns of SARS-CoV-2 transmission and inform ongoing vaccination strategies.

A mixed methods analysis of participation in social contact surveys (preprint)

Background Social contact survey data forms a core component of modern epidemic models: however, there has been little assessment of the potential biases in such data. Methods We conducted focus groups with university students who had (n=13) and had not (n=14) completed a social contact survey during the COVID-19 pandemic. Qualitative findings were explored quantitatively by analysing participation data. Results The opportunity to contribute to COVID-19 research, to be heard and feel useful were frequently reported motivators for participating in the contact survey. Reductions in survey engagement following lifting of COVID-19 restrictions may have occurred because the research was perceived to be less critical and/ or because the participants were busier and had more contacts. Having a high number of contacts to report, uncertainty around how to report each contact, and concerns around confidentiality were identified as factors leading to inaccurate reporting. Focus groups participants thought that financial incentives or provision of study results would encourage participation. Conclusions Incentives could improve engagement with social contact surveys. Qualitative research can inform the format, timing, and wording of surveys to optimise completion and accuracy. Graphical abstract

Impact of voluntary risk-mitigation behaviour on transmission of the Omicron SARS-CoV-2 variant in England (preprint)

Abstract Background The Omicron variant of SARS-CoV-2 infection poses substantial challenges to public health. In England, "plan B" mitigation measures were introduced in December 2021 including increased home working and face coverings in shops, but stopped short of restrictions on social contacts. The impact of voluntary risk mitigation behaviours on future SARS-CoV-2 burden is unknown. Methods We developed a rapid online survey of risk mitigation behaviours during the winter 2021 festive period and deployed in two longitudinal cohort studies in the UK (Avon Longitudinal Study of Parents and Children (ALSPAC) and TwinsUK/Covid Symptom Study (CSS) Biobank) in December 2021. Using an individual-based, probabilistic model of COVID-19 transmission between social contacts with SARS-CoV-2 Omicron variant parameters and realistic vaccine coverage in England, we describe the potential impact of the SARS-CoV-2 Omicron wave in England in terms of the effective reproduction number and cumulative infections, hospital admissions and deaths. Using survey results, we estimated in real-time the impact of voluntary risk mitigation behaviours on the Omicron wave in England, if implemented for the entire epidemic wave. Results Over 95% of survey respondents (N_ALSPAC=2,686 and N_Twins=6,155) reported some risk mitigation behaviours, with being fully vaccinated and using home testing kits the most frequently reported behaviours. Less than half of those respondents reported that their behaviour was due to "plan B". We estimate that without risk mitigation behaviours, the Omicron variant is consistent with an effective reproduction number between 2.5 and 3.5. Due to the reduced vaccine effectiveness against infection with the Omicron variant, our modelled estimates suggest that between 55% and 60% of the English population could be infected during the current wave, translating into between 15,000 and 46,000 cumulative deaths, depending on assumptions about vaccine effectiveness. We estimate that voluntary risk reduction measures could reduce the effective reproduction number to between 1.8 and 2.2 and reduce the cumulative number of deaths by up to 24%. Conclusions We conclude that voluntary measures substantially reduce the projected impact of the SARS-CoV-2 Omicron variant, but that voluntary measures alone would be unlikely to completely control transmission.

Short-term Projections based on Early Omicron Variant Dynamics in England (preprint)

Throughout the ongoing COVID-19 pandemic, the worldwide transmission and replication of SARS-COV-2, the causative agent of COVID-19 disease, has resulted in the opportunity for multiple mutations to occur that may alter the virus transmission characteristics, the effectiveness of vaccines and the severity of disease upon infection. The Omicron variant (B.1.1.529) was first reported to the WHO by South Africa on 24 November 2021 and was declared a variant of concern by the WHO on 26 November 2021. The variant was first detected in the UK on 27 November 2021 and has since been reported in a number of countries globally where it is frequently associated with rapid increase in cases. Here we present analyses of UK data showing the earliest signatures of the Omicron variant and mathematical modelling that uses the UK data to simulate the potential impact of this variant in the UK. In order to account for the uncertainty in transmission advantage, vaccine escape and severity at the time of writing, we carry out a sensitivity analysis to assess the impact of these variant characteristics on future risk.

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.

Limited impact of contact tracing in a University setting for COVID-19 due to asymptomatic transmission and social distancing (preprint)

Contact tracing is an important tool for controlling the spread of infectious diseases, including COVID-19. Here, we investigate the spread of COVID-19 and the effectiveness of contact tracing in a university population, using a data-driven ego-centric network model constructed with social contact data collected during 2020 and similar data collected in 2010. We find that during 2020, university staff and students consistently reported fewer social contacts than in 2010, however those contacts occurred more frequently and were of longer duration. We find that contact tracing in the presence of social distancing is less impactful than without social distancing. By combining multiple data sources, we show that University-aged populations are likely to develop asymptomatic COVID-19 infections. We find that asymptomatic index cases cannot be reliably back-traced through contact tracing and consequently transmission in their social network is not significantly reduced through contact tracing. In summary, social distancing restrictions had a large impact on limiting COVID-19 outbreaks in universities; to reduce transmission further contact tracing should be used in conjunction with alternative interventions.

Low uptake of COVID-19 lateral flow testing among university students: a mixed methods evaluation (preprint)

BackgroundUniversity populations offer a unique opportunity to quantify COVID-19 lateral flow testing (LFT) uptake. MethodsMixed methods evaluation of LFT among University of Bristol students comprising an analysis of testing uptake using logistic regression analyses; a survey; and qualitative interviews to explore experiences of testing and subsequent behaviour. Results12,391 LFTs were conducted on 8025/36,054 (22.3%) students. Only one in 10 students had the recommended two tests. There were striking demographic disparities in uptake with those from ethnic minority groups having lower uptake (e.g. 3% of Chinese students were tested vs. 30.7% of White students), and variations by level and year of study (ranging from 5.3% to 33.7%), place of residence (29.0% to 35.6%) and faculty (15.2% to 32.8%). Barriers to engagement with testing included a lack of awareness, knowledge and understanding, and concerns about the accuracy and safety. Students understood limitations of LFTs but requested further information about test accuracy. Tests were used to inform behavioural decisions, often in combination with other information, such as the potential for exposure to the virus and perceptions of vulnerability. ConclusionsThe low uptake of testing brings into question the role of mass LFT in university settings.

Early epidemiological signatures of novel SARS-CoV-2 variants: establishment of B.1.617.2 in England (preprint)

The rapid emergence of SARS-CoV-2 mutants with new phenotypic properties is a critical challenge to the control of the ongoing pandemic. B.1.1.7 was monitored in the UK through routine testing and S-gene target failures (SGTF), comprising over 90% of cases by March 2021. Now, the reverse is occurring: SGTF cases are being replaced by an S-gene positive variant, which we associate with B.1.617.2. Evidence from the characteristics of S-gene positive cases demonstrates that, following importation, B.1.617.2 is transmitted locally, growing at a rate higher than B.1.1.7 and a doubling time between 5-14 days. S-gene positive cases should be prioritised for sequencing and aggressive control in any countries in which this variant is newly detected.

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.

Increased hazard of mortality in cases compatible with SARS-CoV-2 variant of concern 202012/1 - a matched cohort study (preprint)

ObjectivesTo establish whether there is any change in mortality associated with infection of a new variant of SARS-CoV-2 (VOC-202012/1), first detected in UK in December 2020, compared to that associated with infection with circulating SARS-CoV-2 variants. DesignMatched cohort study. Cases are matched by age, gender, ethnicity, index of multiple deprivation, lower tier local authority region, and sample date of positive specimen, and differing only by detectability of the spike protein gene using the TaqPath assay - a proxy measure of VOC-202012/1 infection. SettingUnited Kingdom, Pillar 2 COVID-19 testing centres using the taqPath assay. Participants54,773 pairs of participants testing positive for SARS-CoV-2 in Pillar 2 between 1st October 2020 and 29th January 2021. Main outcome measures - Death within 28 days of first positive SARS-CoV-2 test. ResultsThere is a high probability that the risk of mortality is increased by infection with VOC-202012/01 (p <0.001). The mortality hazard ratio associated with infection with VOC-202012/1 compared to infection with previously circulating variants is 1.7 (95% CI 1.3 - 2.2) in patients who have tested positive for COVID-19 in the community. In this comparatively low risk group, this represents an increase of deaths from 1.8 in 1000 to 3.1 in 1000 detected cases. ConclusionsIf this finding is generalisable to other populations, VOC-202012/1 infections have the potential to cause substantial additional mortality over and previously circulating variants. Healthcare capacity planning, national and international control policies are all impacted by this finding, with increased mortality lending weight to the argument that further coordinated and stringent measures are justified to reduce deaths from SARS-CoV-2.

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.

Household bubbles and COVID-19 transmission: insights from percolation theory (preprint)

BackgroundIn the era of social distancing to curb the spread of COVID-19, bubbling is the combining of two or more households to create an exclusive larger group. The impact of bubbling on COVID-19 transmission is challenging to quantify because of the complex social structures involved. MethodsWe developed a network description of households in the UK, using the configuration model to link households. We explored the impact of bubbling scenarios by joining together households of various sizes. For each bubbling scenario, we calculated the percolation threshold, that is, the number of connections per individual required for a giant component to form, numerically and theoretically. We related the percolation threshold to the household reproduction number. ResultsWe find that bubbling scenarios in which single-person households join with another household has a minimal impact on network connectivity and transmission potential. Ubiquitous scenarios where all households form a bubble are likely to lead to extensive transmission that is hard to control. The impact of plausible scenarios, with variable uptake and heterogeneous bubble sizes, can be mitigated with reduced numbers of contacts outside the household. ConclusionsBubbling of households comes at an increased risk of transmission, however under certain circumstances risks can be modest and could be balanced by other changes in behaviours.

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.

Meta-analysis of the SARS-CoV-2 serial interval and the impact of parameter uncertainty on the COVID-19 reproduction number (preprint)

The serial interval of an infectious disease, commonly interpreted as the time between onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections) and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early COVID-19 data. In this paper we estimate these key quantities in the context of COVID-19 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean 5.6 (95% CrI 5.1-6.2) and SD 4.2 (95% CrI 3.9-4.6) days (empirical distribution), the generation interval with a mean 4.8 (95% CrI 4.3-5.41) and SD 1.7 (95% CrI 1.0-2.6) days (fitted gamma distribution), and the incubation period with a mean 5.5 (95% CrI 5.1-5.8) and SD 4.9 (95% CrI 4.5-5.3) days (fitted log normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modelling COVID-19 transmission.

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.