Safe management of full-capacity live/mass events in COVID-19 will require mathematical, epidemiological and economic modelling

Effectiveness of testing, contact tracing and isolation interventions among the general population on reducing transmission of SARS-CoV-2: a systematic review

We conducted a systematic literature review of general population testing, contact tracing, case isolation and contact quarantine interventions to assess their effectiveness in reducing SARS-CoV-2 transmission, as implemented in real-world settings. We designed a broad search strategy and aimed to identify peer-reviewed studies of any design provided there was a quantitative measure of effectiveness on a transmission outcome. Studies that assessed the effect of testing or diagnosis on disease outcomes via treatment, but did not assess a transmission outcome, were not included. We focused on interventions implemented among the general population rather than in specific settings; these were from anywhere in the world and published any time after 1 January 2020 until the end of 2022. From 26 720 titles and abstracts, 1181 were reviewed as full text, and 25 met our inclusion criteria. These 25 studies included one randomized control trial (RCT) and the remaining 24 analysed empirical data and made some attempt to control for confounding. Studies included were categorized by the type of intervention: contact tracing (seven studies); specific testing strategies (12 studies); strategies for isolating cases/contacts (four studies); and 'test, trace, isolate' (TTI) as a part of a package of interventions (two studies). None of the 25 studies were rated at low risk of bias and many were rated as serious risk of bias, particularly due to the likely presence of uncontrolled confounding factors, which was a major challenge in assessing the independent effects of TTI in observational studies. These confounding factors are to be expected from observational studies during an on-going pandemic, when the emphasis was on reducing the epidemic burden rather than trial design. Findings from these 25 studies suggested an important public health role for testing followed by isolation, especially where mass and serial testing was used to reduce transmission. Some of the most compelling analyses came from examining fine-grained within-country data on contact tracing; while broader studies which compared behaviour between countries also often found TTI led to reduced transmission and mortality, this was not universal. There was limited evidence for the benefit of isolation of cases/contacts away from the home environment. One study, an RCT, showed that daily testing of contacts could be a viable strategy to replace lengthy quarantine of contacts. Based on the scarcity of robust empirical evidence, we were not able to draw any firm quantitative conclusions about the quantitative impact of TTI interventions in different epidemic contexts. While the majority of studies found that testing, tracing and isolation reduced transmission, evidence for the scale of this impact is only available for specific scenarios and hence is not necessarily generalizable. Our review therefore emphasizes the need to conduct robust experimental studies that help inform the likely quantitative impact of different TTI interventions on transmission and their optimal design. Work is needed to support such studies in the context of future emerging epidemics, along with assessments of the cost-effectiveness of TTI interventions, which was beyond the scope of this review but will be critical to decision-making. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

Evaluating the feasibility and acceptability of a safety protocol to mitigate SARS-CoV-2 transmission risks when participating in full-capacity live mass events: a cross-sectional survey and interview-based study

OBJECTIVE

Investigate the feasibility and acceptability of a novel COVID-19 safety protocol combining professionally witnessed home-based videoed pre-event testing and a data-driven risk assessment model that was piloted at the Standon Calling Festival in July 2021.

DESIGN

Observational study using a sequential explanatory mixed-methods design involving a survey, personal interviews and group discussions with a cross section of participants.

SETTING

Standon Calling Festival, Hertfordshire, England.

PARTICIPANTS

4726 adults who attended Standon Calling and consented to participate in the study.

RESULTS

Nearly a quarter (23.1%; 1093) attendees (women 65%, men 35%) responded to the postevent survey. Eleven participants were interviewed before thematic saturation was reached. The majority (81.0%) of respondents found the at-home testing protocol convenient and of reasonable cost (73.6%). Confidence in the test result was enhanced due to professional-supported videoing (76.2%), whereas 72.6% had confidence in the security of the data. Videoed self-testing helped 45.0% of respondents to feel more confident in their lateral flow testing technique. The majority (85.5%) felt safer at the event and 93.7% agreed that the protocol did not interfere with their enjoyment of the event. Themes generated from interviews showed that the protocol could be applied to other disease areas and events, but there were concerns that over-reliance on test results alone could lead some people to have a false sense of security around the safety of the live event.

CONCLUSIONS

Our study showed that a protocol that combines professionally witnessed home-based videoed pre-event testing is highly acceptable and feasible, and it can inform decision making and support the safe reopening of live mass events at full capacity. Although COVID-19 is now considered endemic in the UK, this protocol can be of value for other countries where the live events industry remains heavily impacted. Risk modelling should be tested and evaluated at future events to further increase the robustness of this protocol.

Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach

BACKGROUND

Modelling COVID-19 transmission at live events and public gatherings is essential to controlling the probability of subsequent outbreaks and communicating to participants their personalized risk. Yet, despite the fast-growing body of literature on COVID-19 transmission dynamics, current risk models either neglect contextual information including vaccination rates or disease prevalence or do not attempt to quantitatively model transmission.

OBJECTIVE

This paper attempted to bridge this gap by providing informative risk metrics for live public events, along with a measure of their uncertainty.

METHODS

Building upon existing models, our approach ties together 3 main components: (1) reliable modelling of the number of infectious cases at the time of the event, (2) evaluation of the efficiency of pre-event screening, and (3) modelling of the event's transmission dynamics and their uncertainty using Monte Carlo simulations.

RESULTS

We illustrated the application of our pipeline for a concert at the Royal Albert Hall and highlighted the risk's dependency on factors such as prevalence, mask wearing, and event duration. We demonstrate how this event held on 3 different dates (August 20, 2020; January 20, 2021; and March 20, 2021) would likely lead to transmission events that are similar to community transmission rates (0.06 vs 0.07, 2.38 vs 2.39, and 0.67 vs 0.60, respectively). However, differences between event and background transmissions substantially widened in the upper tails of the distribution of the number of infections (as denoted by their respective 99th quantiles: 1 vs 1, 19 vs 8, and 6 vs 3, respectively, for our 3 dates), further demonstrating that sole reliance on vaccination and antigen testing to gain entry would likely significantly underestimate the tail risk of the event.

CONCLUSIONS

Despite the unknowns surrounding COVID-19 transmission, our estimation pipeline opens the discussion on contextualized risk assessment by combining the best tools at hand to assess the order of magnitude of the risk. Our model can be applied to any future event and is presented in a user-friendly RShiny interface. Finally, we discussed our model's limitations as well as avenues for model evaluation and improvement.