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

Areas of enduring COVID-19 prevalence: drivers of prevalence and mitigating strategies

BACKGROUND

UK local authorities that experienced sustained high levels of COVID-19 between 1st March 2020 and 28th February 2021 were described by the UK Scientific Advisory Group for Emergencies as areas of enduring prevalence. This research was carried out in order to examine the views of local authority Directors of Public Health, who played a crucial role in the local response to COVID-19, on reasons for sustained high levels of prevalence in some areas, alongside an investigation of the mitigation strategies that they implemented during the course of the pandemic.

METHODS

Interviews were conducted with Directors of Public Health in 19 local authority areas across England, between July and November 2021. This included nine areas identified as areas of enduring prevalence and ten 'comparison' areas.

RESULTS

The outcomes of this study suggests that the geographical differences in prevalence rates are strongly influenced by health inequalities. Structural factors including deprivation, employment, and housing, due to their disproportionate impact on specific groups, converged with demographic factors, including ethnicity and age, and vaccination rates, and were identified as the main drivers of enduring prevalence. There are key differences in these drivers both within and, to a lesser extent, between local authorities. Other than these structural barriers, no major differences in facilitators or barriers to COVID-19 mitigation were identified between areas of varying prevalence. The main features of successful mitigation strategies were a locally tailored approach and partnership working involving local authority departments working with local health, community, voluntary and business organisations.

CONCLUSIONS

This study is the first to add the voices of Directors of Public Health, who played a crucial role in the local COVID-19 response. Areas of enduring prevalence existed during the pandemic which were caused by a complex mix of structural factors related to inequalities. Participants advised that more research is needed on the effectiveness of mitigation strategies and other measures to reduce the impact of structural inequalities, to better understand the factors that drive prevalence. This would include an assessment of how these factors combine to predict transmission and how this varies between different areas.

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

The reproduction numberR 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 ofR , 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, formulatingR 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.

Impact of the COVID-19 pandemic on midwifery-led service provision in the United Kingdom in 2020-21: findings of three national surveys

Background

The COVID-19 pandemic required all healthcare systems to adapt quickly. There is some evidence about the impact of the pandemic on United Kingdom maternity services overall, but little is known about the impact on midwifery-led services, including midwifery units and home birth services.

Objective

To describe changes to midwifery-led service provision in the United Kingdom and the Channel Islands during the COVID-19 pandemic.

Design

Three national surveys were circulated using the United Kingdom Midwifery Study System (UKMidSS) and the Royal College of Midwives (RCM) Heads and Directors of Midwifery Network. The UKMidSS surveys took place in wave 1 (April to June 2020) and in wave 2 (February to March 2021). The RCM survey was conducted in April 2020.

Findings

The response rate to the UKMidSS surveys was 84% in wave 1 and 70% in wave 2, while 48% of Heads and Directors of Midwifery responded to the RCM survey. Around 60% of midwifery units reported being open as usual in wave 1, with the remainder affected by closures. Fewer unit closures (15%) were reported in the wave 2 survey. Around 40% of services reported some reduction in home birth services in wave 1, compared with 15% in wave 2. The apparent impact of the pandemic varied widely across the four nations of the United Kingdom and within the English regions.

Conclusions

The pandemic led to increased centralisation of maternity care and the disruption of midwifery-led services, especially in the first wave. Further research should focus on the reasons behind closures, the regional variation and the impact on maternity care experience and outcomes.

Effect of Quarantine Strategies in a Compartmental Model with Asymptomatic Groups

We present an epidemiological model, which extend the classical SEIR model by accounting for the presence of asymptomatic individuals and the effect of isolation of infected individuals based on testing. Moreover, we introduce two types of home quarantine, namely gradual and abrupt one. We compute the equilibria of the new model and derive its reproduction number. Using numerical simulations we analyze the effect of quarantine and testing on the epidemic dynamic. Given a constraint that limits the maximal number of simultaneous active cases, we demonstrate that the isolation rate, which enforces this constraint, decreases with the increasing testing rate. Our simulations show that massive testing allows to control the infection spread using a much lower isolation rate than in the case of indiscriminate quarantining. Finally, based on the effective reproduction number we suggest a strategy to manage the epidemic. It consists in introducing abrupt quarantine as well as relaxing the quarantine in such a way that the epidemic remains under control and further waves do not occur. We analyze the sensitivity of the model dynamic to the quarantine size, timing and strength of the restrictions.

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.

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'.