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

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 discovered 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.

Estimating hepatitis B virus prevalence among key population groups for European Union and European Economic Area countries and the United Kingdom: a modelling study.

BACKGROUND: Hepatitis B virus (HBV) epidemiology in Europe differs by region and population risk group, and data are often incomplete. We estimated chronic HBV prevalence as measured by surface antigen (HBsAg) among general and key population groups for each country in the European Union, European Economic Area and the United Kingdom (EU/EEA/UK), including where data are currently unavailable. METHODS: We combined data from a 2018 systematic review (updated in 2021), data gathered directly by the European Centre for Disease Control (ECDC) from EU/EEA countries and the UK and further country-level data. We included data on adults from the general population, pregnant women, first time blood donors (FTBD), men who have sex with men (MSM), prisoners, people who inject drugs (PWID), and migrants from 2001 to 2021, with three exceptions made for pre-2001 estimates. Finite Mixture Models (FMM) and Beta regression were used to predict country and population group HBsAg prevalence. A separate multiplier method was used to estimate HBsAg prevalence among the migrant populations within each country, due to biases in the data available. RESULTS: There were 595 included studies from 31 countries (N = 41,955,969 people): 66 were among the general population (mean prevalence ([Formula: see text]) 1.3% [range: 0.0-7.6%]), 52 among pregnant women ([Formula: see text]1.1% [0.1-5.3%]), 315 among FTBD ([Formula: see text]0.3% [0.0-6.2%]), 20 among MSM ([Formula: see text]1.7% [0.0-11.2%]), 34 among PWID ([Formula: see text]3.9% [0.0-16.9%]), 24 among prisoners ([Formula: see text]2.9% [0.0-10.7%]), and 84 among migrants ([Formula: see text]7.0% [0.2-37.3%]). The FMM grouped countries into 3 classes. We estimated HBsAg prevalence among the general population to be < 1% in 24/31 countries, although it was higher in 7 Eastern/Southern European countries. HBsAg prevalence among each population group was higher in most Eastern/Southern European than Western/Northern European countries, whilst prevalence among PWID and prisoners was estimated at > 1% for most countries. Portugal had the highest estimated prevalence of HBsAg among migrants (5.0%), with the other highest prevalences mostly seen in Southern Europe. CONCLUSIONS: We estimated HBV prevalence for each population group within each EU/EAA country and the UK, with general population HBV prevalence to be < 1% in most countries. Further evidence is required on the HBsAg prevalence of high-risk populations for future evidence synthesis.

Estimates of hepatitis B virus prevalence among general population and key risk groups in EU/EEA/UK countries: a systematic review.

BackgroundThe burden of chronic hepatitis B virus (HBV) varies across the European Union (EU) and European Economic Area (EEA).AimWe aimed to update the 2017 HBV prevalence estimates in EU/EEA countries and the United Kingdom for 2018 to 2021.MethodsWe undertook a systematic review, adding to HBV prevalence estimates from an existing (2005-2017) database. Databases were searched for original English-language research articles including HBV surface antigen prevalence estimates among the general population, pregnant women, first-time blood donors (FTB), men who have sex with men (MSM), migrants and people in prison. Country experts contributed grey literature data. Risk of bias was assessed using a quality assessment framework.FindingsThe update provided 147 new prevalence estimates across the region (updated total n = 579). Median HBV prevalence in the general population was 0.5% and the highest was 3.8% (Greece). Among FTB, the highest prevalence was 0.8% (Lithuania). Estimates among pregnant women were highest in Romania and Italy (5.1%). Among migrants, the highest estimate was 31.7% (Spain). Relative to 2017 estimates, median prevalence among pregnant women decreased by 0.5% (to 0.3%) and increased by 0.9% (to 5.8%) among migrants. Among MSM, the highest estimate was 3.4% (Croatia). Prevalence among people in prison was highest in Greece (8.3%) and the median prevalence increased by 0.6% (to 2.1%).ConclusionsThe HBV prevalence is low in the general population and confined to risk populations in most European countries with some exceptions. Screening and treatment should be targeted to people in prison and migrants.

Evaluation and deployment of isotype-specific salivary antibody assays for detecting previous SARS-CoV-2 infection in children and adults.

BACKGROUND: Saliva is easily obtainable non-invasively and potentially suitable for detecting both current and previous SARS-CoV-2 infection, but there is limited evidence on the utility of salivary antibody testing for community surveillance. METHODS: We established 6 ELISAs detecting IgA and IgG antibodies to whole SARS-CoV-2 spike protein, to its receptor binding domain region and to nucleocapsid protein in saliva. We evaluated diagnostic performance, and using paired saliva and serum samples, correlated mucosal and systemic antibody responses. The best-performing assays were field-tested in 20 household outbreaks. RESULTS: We demonstrate in test accuracy (N = 320), spike IgG (ROC AUC: 95.0%, 92.8-97.3%) and spike IgA (ROC AUC: 89.9%, 86.5-93.2%) assays to discriminate best between pre-pandemic and post COVID-19 saliva samples. Specificity was 100% in younger age groups (0-19 years) for spike IgA and IgG. However, sensitivity was low for the best-performing assay (spike IgG: 50.6%, 39.8-61.4%). 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 household outbreaks, antibody responses were heterogeneous but remained a reliable indicator of recent infection. Intriguingly, unvaccinated children without confirmed infection showed evidence of exposure almost exclusively through specific IgA responses. CONCLUSIONS: Through robust standardisation, evaluation and field-testing, this work provides a platform for further studies investigating SARS-CoV-2 transmission and mucosal immunity with the potential for expanding salivo-surveillance to other respiratory infections in hard-to-reach settings.

If a person has been previously infected with SARS-CoV-2 they will produce specific proteins, called antibodies. These are present in the saliva and blood. Saliva is easier to obtain than blood, so we developed and evaluated six tests that detect SARS-CoV-2 antibodies in saliva in children and adults. Some tests detected antibodies to a particular protein made by SARS-CoV-2 called the spike protein, and these tests worked best. The most accurate results were obtained by using a combination of tests. Similar tests could also be developed to detect other respiratory infections which will enable easier identification of infected individuals.

Voluntary risk mitigation behaviour can reduce impact of SARS-CoV-2: a real-time modelling study of the January 2022 Omicron wave in England.

BACKGROUND: Predicting the likely size of future SARS-CoV-2 waves is necessary for public health planning. In England, voluntary "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 ahead of 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 predicted 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 (NALSPAC = 2686 and NTwins = 6155) reported some risk mitigation behaviours, with vaccination and using home testing kits reported most frequently. 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 12,000 and 46,000 cumulative deaths, depending on assumptions about severity and vaccine effectiveness. The actual number of deaths was 15,208 (26 November 2021-1 March 2022). 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: Predicting future infection burden is affected by uncertainty in disease severity and vaccine effectiveness estimates. In addition to biological uncertainty, we show 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.

A mixed methods analysis of participation in a social contact survey.

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 never (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.

Assessing the effectiveness of prophylactic treatment strategies for sheep scab.

Ovine psoroptic mange (sheep scab) is a condition caused by a hypersensitivity response to the ectoparasitic mite, Psoroptes ovis. It is an animal welfare concern and causes extensive economic losses to the sheep industry worldwide. More effective scab management is required to limit increases in infection prevalence, particularly given growing concerns over acaricide resistance. Here, a stochastic metapopulation model is used to explore the effectiveness of a range of prophylactic acaricide treatment strategies in comparison to no intervention. Over a simulated one-year period, movement control, based on the prophylactic treatment of animals being moved in sales, followed by farm biosecurity of bought in animals, was shown to be the most effective at reducing scab risk and more cost-effective than no intervention. Localised targeting of prophylaxis in areas of high scab prevalence was more effective than using prophylaxis at random, however, this localised effect declined post-treatment because of the import of infected animals. The analysis highlights the role of the movement of infected animals in maintaining high levels of scab infection and the importance of reducing this route of transmission to allow localised management to be effective.

Reassessing the evidence for universal school-age BCG vaccination in England and Wales: re-evaluating and updating a modelling study.

OBJECTIVES: In 2005, England and Wales switched from universal BCG vaccination against tuberculosis (TB) disease for school-age children to targeted vaccination of neonates. We aimed to recreate and re-evaluate a previously published model, the results of which informed this policy change. DESIGN: We recreated an approach for estimating the impact of ending the BCG schools scheme, correcting a methodological flaw in the model, updating the model with parameter uncertainty and improving parameter estimates where possible. We investigated scenarios for the assumed annual decrease in TB incidence rates considered by the UK's Joint Committee on Vaccination and Immunisation and explored alternative scenarios using notification data. SETTING: England and Wales. OUTCOME MEASURES: The number of vaccines needed to prevent a single notification and the average annual additional notifications caused by ending the policy change. RESULTS: The previously published model was found to contain a methodological flaw and to be spuriously precise. It greatly underestimated the impact of ending school-age vaccination compared with our updated, corrected model. The updated model produced predictions with wide CIs when parameter uncertainty was included. Model estimates based on an assumption of an annual decrease in TB incidence rates of 1.9% were closest to those estimated using notification data. Using this assumption, we estimate that 1600 (2.5; 97.5% quantiles: 1300, 2000) vaccines would have been required to prevent a single notification in 2004. CONCLUSIONS: The impact of ending the BCG schools scheme was found to be greater than previously thought when notification data were used. Our results highlight the importance of independent evaluations of modelling evidence, including uncertainty, and evaluating multiple scenarios when forecasting the impact of changes in vaccination policy.

Estimation of reproduction numbers in real time: Conceptual and statistical challenges.

The reproduction number R has been a central metric of the COVID-19 pandemic response, published weekly by the UK government and regularly reported in the media. Here, we provide a formal definition and discuss the advantages and most common misconceptions around this quantity. We consider the intuition behind different formulations of R , the complexities in its estimation (including the unavoidable lags involved), and its value compared to other indicators (e.g. the growth rate) that can be directly observed from aggregate surveillance data and react more promptly to changes in epidemic trend. As models become more sophisticated, with age and/or spatial structure, formulating R becomes increasingly complicated and inevitably model-dependent. We present some models currently used in the UK pandemic response as examples. Ultimately, limitations in the available data streams, data quality and time constraints force pragmatic choices to be made on a quantity that is an average across time, space, social structure and settings. Effectively communicating these challenges is important but often difficult in an emergency.

Commentary on the use of the reproduction number R during the COVID-19 pandemic.

Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.

Meta-analysis of the severe acute respiratory syndrome coronavirus 2 serial intervals and the impact of parameter uncertainty on the coronavirus disease 2019 reproduction number.

The serial interval of an infectious disease, commonly interpreted as the time between the 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 coronavirus disease 2019 data. In this paper, we estimate these key quantities in the context of coronavirus disease 2019 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean of 5.9 (95% CI 5.2; 6.7) and SD 4.1 (95% CI 3.8; 4.7) days (empirical distribution), the generation interval with a mean of 4.9 (95% CI 4.2; 5.5) and SD 2.0 (95% CI 0.5; 3.2) days (fitted gamma distribution), and the incubation period with a mean 5.2 (95% CI 4.9; 5.5) and SD 5.5 (95% CI 5.1; 5.9) 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 modeling coronavirus disease 2019 transmission.

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.

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

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.

Contacts and behaviours of university students during the COVID-19 pandemic at the start of the 2020/2021 academic year.

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.

Sheep scab spatial distribution: the roles of transmission pathways.

BACKGROUND: Ovine psoroptic mange (sheep scab) is a highly pathogenic contagious infection caused by the mite Psoroptes ovis. Following 21 years in which scab was eradicated in the UK, it was inadvertently reintroduced in 1972 and, despite the implementation of a range of control methods, its prevalence increased steadily thereafter. Recent reports of resistance to macrocyclic lactone treatments may further exacerbate control problems. A better understanding of the factors that facilitate its transmission are required to allow improved management of this disease. Transmission of infection occurs within and between contiguous sheep farms via infected sheep-to-sheep or sheep-environment contact and through long-distance movements of infected sheep, such as through markets. METHODS: A stochastic metapopulation model was used to investigate the impact of different transmission routes on the spatial pattern of outbreaks. A range of model scenarios were considered following the initial infection of a cluster of highly connected contiguous farms. RESULTS: Scab spreads between clusters of neighbouring contiguous farms after introduction but when long-distance movements are excluded, infection then self-limits spatially at boundaries where farm connectivity is low. Inclusion of long-distance movements is required to generate the national patterns of disease spread observed. CONCLUSIONS: Preventing the movement of scab infested sheep through sales and markets is essential for any national management programme. If effective movement control can be implemented, regional control in geographic areas where farm densities are high would allow more focussed cost-effective scab management.

Estimates of regional infectivity of COVID-19 in the United Kingdom following imposition of social distancing measures.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reproduction number has become an essential parameter for monitoring disease transmission across settings and guiding interventions. The UK published weekly estimates of the reproduction number in the UK starting in May 2020 which are formed from multiple independent estimates. In this paper, we describe methods used to estimate the time-varying SARS-CoV-2 reproduction number for the UK. We used multiple data sources and estimated a serial interval distribution from published studies. We describe regional variability and how estimates evolved during the early phases of the outbreak, until the relaxing of social distancing measures began to be introduced in early July. Our analysis is able to guide localized control and provides a longitudinal example of applying these methods over long timescales. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Modelling that shaped the early COVID-19 pandemic response in the UK.

Infectious disease modelling has played an integral part of the scientific evidence used to guide the response to the COVID-19 pandemic. In the UK, modelling evidence used for policy is reported to the Scientific Advisory Group for Emergencies (SAGE) modelling subgroup, SPI-M-O (Scientific Pandemic Influenza Group on Modelling-Operational). This Special Issue contains 20 articles detailing evidence that underpinned advice to the UK government during the SARS-CoV-2 pandemic in the UK between January 2020 and July 2020. Here, we introduce the UK scientific advisory system and how it operates in practice, and discuss how infectious disease modelling can be useful in policy making. We examine the drawbacks of current publishing practices and academic credit and highlight the importance of transparency and reproducibility during an epidemic emergency. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

A spatial model of COVID-19 transmission in England and Wales: early spread, peak timing and the impact of seasonality.

An outbreak of a novel coronavirus was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable human-to-human transmission in England. We adapted an existing national-scale metapopulation model to capture the spread of COVID-19 in England and Wales. We used 2011 census data to inform population sizes and movements, together with parameter estimates from the outbreak in China. We predict that the epidemic will peak 126 to 147 days (approx. 4 months) after the start of person-to-person transmission in the absence of controls. Assuming biological parameters remain unchanged and transmission persists from February, we expect the peak to occur in June. Starting location and model stochasticity have a minimal impact on peak timing. However, realistic parameter uncertainty leads to peak time estimates ranging from 78 to 241 days following sustained transmission. Seasonal changes in transmission rate can substantially impact the timing and size of the epidemic. We provide initial estimates of the epidemic potential of COVID-19. These results can be refined with more precise parameters. Seasonal changes in transmission could shift the timing of the peak into winter, with important implications for healthcare capacity planning. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK.

Household bubbles and COVID-19 transmission: insights from percolation theory.

In 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. We 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. We find that bubbling scenarios in which single-person households join with another household have a minimal impact on network connectivity and transmission potential. Ubiquitous scenarios where all households form a bubble are likely to lead to an 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. Bubbling 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. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

The population attributable fraction of cases due to gatherings and groups with relevance to COVID-19 mitigation strategies.

Many countries have banned groups and gatherings as part of their response to the pandemic caused by the coronavirus, SARS-CoV-2. Although there are outbreak reports involving mass gatherings, the contribution to overall transmission is unknown. We used data from a survey of social contact behaviour that specifically asked about contact with groups to estimate the population attributable fraction (PAF) due to groups as the relative change in the basic reproduction number when groups are prevented. Groups of 50+ individuals accounted for 0.5% of reported contact events, and we estimate that the PAF due to groups of 50+ people is 5.4% (95% confidence interval 1.4%, 11.5%). The PAF due to groups of 20+ people is 18.9% (12.7%, 25.7%) and the PAF due to groups of 10+ is 25.2% (19.4%, 31.4%). Under normal circumstances with pre-COVID-19 contact patterns, large groups of individuals have a relatively small epidemiological impact; small- and medium-sized groups between 10 and 50 people have a larger impact on an epidemic. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.