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• Systematic mass testing in closed settings provides information on infection rates. • Test positivity was 11.6%, with only one-quarter reporting symptoms. • The prison wing handling new admissions reported the second-lowest positivity rate. • Testing uptake was higher in residents than in staff members. • Mass testing is a valuable tool to bring outbreaks under control quickly. The aim of this paper was to describe the results of mass asymptomatic testing for COVID-19 in a male prison in England following the declaration of an outbreak. It provides novel data on the implementation of a mass testing regime within a prison during the pandemic. The paper is an observational evaluation of the mass testing conducted for 6 months following the declaration of a COVID-19 outbreak within a prison. It investigated the incidence of positive cases in both staff and residents using polymerase chain reaction testing. Data from October 2020 until March 2021 was included. A total of 2170 tests were performed by 851 residents and 182 staff members; uptake was 48.3% for people living in prison and 30.4% for staff. Overall test positivity was 11.6% (14.3% for residents, 3.0% for staff), with around one-quarter of these reporting symptoms. The prison wing handling new admissions reported the second-lowest positivity rate (9.4%) of the eight wings. Mass testing for COVID-19 over a short space of time can lead to rapid identification of additional cases, particularly asymptomatic cases. Testing that relies on residents and staff reporting symptoms will underestimate the true extent of transmission and will likely lead to a prolonged outbreak. [ABSTRACT FROM AUTHOR]

This paper considers the nature of social surveillance through the physical activity tracking app MapMyRun and examines how this was experienced during the COVID-19 pandemic during the UK and USA summer 2020 lockdowns. In contributing to debates in digital geographies around the entanglements of the fleshy and digital body, the paper responds to calls for research to recognise the increasing sociality of self-tracking (Couture, 2021), specifically considering how, during the COVID-19 pandemic, these apps offered a form of connection during a time of isolation. Using data from email and video interviews, I argue that whilst a Foucauldian account of surveillance can be used as a point of departure, it is limited in accounting for the social aspects of self-tracking. I therefore propose that applying Robinson's (2000) concept of 'noisy surveillance' to self-tracking is useful for understanding the messiness of surveillance in terms of the complications and noisiness involved in interactions in digital spaces, as well as the opportunities for performance management online particularly during lockdown. [ABSTRACT FROM AUTHOR]

Rising demand for emergency food during the last couple of decades in the UK has led to a great deal of academic interest in food aid provision, and food banking in particular. Efforts have also been made to examine food poverty and responses to it in more critical terms, which has entailed moving beyond a focus on emergency food support to engage with 'more than food aid' approaches. In this paper, I discuss how these latter approaches are beginning to be mobilised by national organisations, local authorities and place-based food partnerships in the UK. An important catalyst for this shift was the Covid-19 pandemic, which provided the crisis conditions that encouraged public and third-sector actors to think about, and act upon, food poverty in different ways. Drawing on an analysis of submissions to a Covid-19 food inquiry, place-based food initiatives implemented during the pandemic period and more recent initiatives instigated by national food support and anti-hunger groups, the paper examines how a diverse range of organisations are becoming more critical of existing (food aid) responses to food poverty and are seeking to develop more supportive local foodscapes based on a 'more than food aid' approach. The paper concludes with a discussion of the implications of this shift for future research on food poverty. [ABSTRACT FROM AUTHOR]

In this paper, we study the impact of news and sentiments related to covid-19 on United Kingdom (UK)'s stock returns from February 4, 2020 to December 7, 2020. Our results show that covid-19 daily cases exert a significant negative effect on stock returns whereas covid-19 daily deaths have a significant positive impact. These findings hold when covid-related news and sentiments indices are controlled with the 2nd wave data, and when the US policies and equity market volatilities from infectious diseases are used as controls. The magnitude of the effect of covid cases and deaths indicates that the pandemic is not very harmful to the UK stock market. [ABSTRACT FROM AUTHOR]

• This paper provides an overview of the key trends and themes of discussion concerning attitudes to and the adoption of two health measures to control COVID-19 in the UK and US. • Using social media data collected via Twitter, we explore how different public groups react when confronted with mandatory policies. • We find two constituencies have been brought together: pre-existing vaccine critical 'anti-vaccine' groups and newly formed oppositional COVID-19 groups. • The oppositional COVID-19 constituencies vary in composition and nature, but the central focus is a disagreement about the seriousness and threat of COVID-19 and public health measures to control COVID-19. What unites many disparate interests is an aversion to mandates. Protests starting in the summer of 2020, notedly in the US and UK, have brought together two constituencies: pre-existing anti-vaccine groups and newly formed oppositional COVID-19 groups. The oppositional COVID-19 groups vary in composition and nature, but the central focus is a disagreement about the seriousness and threat of COVID-19 and with the public health measures to control COVID-19. What unites many disparate interests is an aversion to mandates. The compulsion to undertake particular public health activities such as mask-wearing and vaccination is a complex topic of public attitudes and beliefs alongside public health goals and messaging. We aim to analyse social media discussions about facemask wearing and the adoption of potential vaccines for COVID-19. Using media monitoring software MeltwaterTM, we analyse English-language tweets for one year from 1st June 2020 until 1st June 2021. We pay particular attention to connections in conversations between key topics of concern regarding masks and vaccines across social media networks. We track where ideas and activist behaviours towards both health interventions have originated, have similarities, and how they have changed over time. Our aim is to provide an overview of the key trends and themes of discussion concerning attitudes to and adoption of health measures in the control of COVID-19 and how publics react when confronted with mandatory policies. We draw on an already extensive literature about mandatory vaccination policies to inform our assessment, from psychology and behavioural science to ethics, political theory, sociology, and public policy. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused huge and disruptive technological changes in the healthcare sector, transforming the way businesses and societies function. To respond to the global health crisis, there have been numerous innovation projects in the healthcare sector, including the fast design and manufacturing of personal protective equipment (PPE) and medical devices, and testing, treatment, and vaccine technologies. Many of these innovative activities happen beyond organizational boundaries with collaboration and open innovation. In this paper, we review the current literature on open innovation strategy during the pandemic and adopt the co-evolution view of business ecosystems to address the context of change. Based on a detailed exploration of the COVID-19-related technologies in the UK and global healthcare sectors, we identify the key emerging themes of open innovation in crisis. Further discussions are conducted in relation to each theme. Our results and analysis can help provide policy recommendations for the healthcare sector, businesses, and society to recover from the crisis. [ABSTRACT FROM AUTHOR]

The paper estimates the causal effect of a health treatment on patients' beliefs, preferences and memories about the treatment. It exploits a natural experiment which occurred in the United Kingdom during the COVID-19 vaccination campaign. UK residents could choose to opt into the vaccination program, but not which vaccine they received. The assignment to a vaccine offered little objective information for learning about its qualities, but triggered strong psychological demand for reassuring beliefs. We surveyed a sample of UK residents about their beliefs on the different COVID-19 vaccines before and after receiving their jab. Before vaccination, individuals exhibit similar prior beliefs and stated preferences about the different vaccines. After vaccination, however, they update their beliefs overly optimistically about the safety and effectiveness of the vaccine they received, state that they would have chosen it if they could, and have distorted memories about their past beliefs. These results cannot be explained by conventional experience effects. At the aggregated level, they show that random assignment to a health treatment predicts a polarization of opinions about its quality. At the individual level, these findings provide evidence in line with the predictions of motivated beliefs and over-inference from weak signals in a real-world health setting. • We interviewed UK residents before and after they received a COVID-19 vaccine. • Individuals could not choose the type of vaccine (Pfizer or Moderna) that they received. • Right after the vaccination, they view their own vaccine as better than they used to think. • They wrongly remember that they thought well of it all along, and would have chosen it if they could. • These results cannot be explained by conventional experience effects but are consistent with motivated reasoning. [ABSTRACT FROM AUTHOR]

• Critical events such as the Covi-19 pandemic are best understood as endogenous to processes of societal transitions. • Qualitative system dynamics modelling provides the basis for rigorous comparative analysis of transition case studies. • The Covid-19 pandemic has had a differential effect on ongoing transitions in UK food and (auto)mobility provision, including the establishment of kernels of new transitions. The 2020 Covid-19 pandemic provides an empirical testing ground for assessing the impact of critical events on societal transitions. Such events are typically seen as exogenous to the transition process, an assumption which is investigated in this paper. Using a qualitative system dynamics modelling approach we conceptualize transition pathways as sets of interacting sequences of events. This enables the analysis of event sequences that constitute the evolving pandemic as impacting on those pathways. We apply this approach to the provision of (auto)mobility and food in the UK. This shows the way in which the pandemic has had a differential effect on ongoing transitions in both systems, sometimes slowing them down, and sometimes accelerating them. In addition, it reveals how it has established new transition pathways. The empirical work further shows how qualitative modelling with system dynamics facilitates an explicit and systematic comparative analysis of transition case studies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has resulted in cities implementing lockdown measures, causing unprecedented disruption (e.g. school/shop/office closures) to urban life often extending over months. With the spread of COVID-19 now being relatively contained, many cities have started to ease their lockdown restrictions by phases. Following the phased recovery strategy proposed by the UK government following the first national lockdown, this paper utilises Greater London as its case study, selecting three main reopening measures (i.e., schools, shops and hospitality reopening). This paper applies sentiment analysis and topic modelling to explore public opinions expressed via Twitter. Our findings reveal that public attention towards the reopening measures reached a peak before the date of policy implementation. The attitudes expressed in discussing reopening measures changed from negative to positive. Regarding the discussed topics related to reopening measures, we find that citizens are more sensitive to early-stage reopening than later ones. This study provides a time-sensitive approach for local authorities and city managers to rapidly sense public opinion using real-time social media data. Governments and policymakers can make use of the framework of sensing public opinion presented herein and utilise it in leading their post-lockdown cities into an adaptive, inclusive and smart recovery. • Twitter data timely capture the public opinion regarding reopening policies. • Sentiment analysis reveals public attitudes to reopening measures. • Topic modelling was utilised for exploring topics about reopening. • Urban citizens are more sensitive to the earlier lockdown lifting measures • Public attention to announcements provides early feedback to implementation. [ABSTRACT FROM AUTHOR]

Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long-short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease. [ABSTRACT FROM AUTHOR]

Modelling complex sociotechnical systems to try to understand how observed behaviours emerge from a network of interacting, interdependent and interrelated functions is a major challenge. Woods et al (Branlat, 2010), have pointed out that it is difficult to find a satisfactory current methodology. They suggested that perhaps the Functional Resonance Analysis Methodology developed by Hollnagel (Hollnagel, 2012) could be an appropriate approach to try. In Part 1 this approach was employed to build an overall model of the UK's COVID 19 response management system, which was constructed as a framework against which, a series of more detailed analyses of specific health care responses could be examined. This meant being conscious of the bigger picture of simultaneous activities and the dynamic emergence of unexpected developments. At that stage, it was of necessity a model of "work as imagined" from careful official and speculative media sources. Although, a full rigorous application will require a more authenticated, official (peer reviewed?), set of data, reports and evidence statements, which doubtless will be available eventually from the inevitable Public Inquiry, it seems a pity to delay gaining and applying any insights and adaptations from "work as (actually) done", that have been observed to date. The recent select committee evidence from people at the heart of the system, (Committee, 2021), which may, or may not be corroborated later, nevertheless now provides a provisional database, which can be utilised to test whether the FRAM model can produce such insights from the actual performance of this highly complex system. This paper has thus attempted a trial run and has found that it can produce a plausible set of insights, which can explain how the system behaved in practice. With such a serious challenge to Government systems worldwide, in all their advisory, operation and decision-making functions, such insights although provisional, could usefully be incorporated and formalised in the current systems rather than waiting for inquiry endorsed recommendations to be formally considered perhaps some years into the future. The paper thus sets out a set of conclusions and recommendations, caveated by emphasising the lack of fully authenticated public domain data on which it is based. The main conclusion however is that the current system appears not to have included any of the functions which could have provided the adaptability and resilience required by fast moving emergencies, such as pandemics. The exception noted was the establishment of a parallel, independent vaccine development and delivery function and it is hoped that at least this lesson from the work as done could be recognised as providing the type of adaptability required and incorporated without delay. [ABSTRACT FROM AUTHOR]

In this paper, we trace how mathematical models are made 'evidence enough' and 'useful for policy'. Working with the interview accounts of mathematical modellers and other scientists engaged in the UK Covid-19 response, we focus on two weeks in March 2020 prior to the announcement of an unprecedented national lockdown. A key thread in our analysis is how pandemics are made 'big'. We follow the work of one particular device, that of modelled 'doubling-time'. By following how modelled doubling-time entangles in its assemblage of evidence-making, we draw attention to multiple actors, including beyond models and metrics, which affect how evidence is performed in relation to the scale of epidemic and its policy response. We draw attention to: policy; Government scientific advice infrastructure; time; uncertainty; and leaps of faith. The 'bigness' of the pandemic, and its evidencing, is situated in social and affective practices, in which uncertainty and dis-ease are inseparable from calculus. This materialises modelling in policy as an 'uncomfortable science'. We argue that situational fit in-the-moment is at least as important as empirical fit when attending to what models perform in policy. • Pandemics are performed as crises of uncertain yet catastrophic potential. • Mathematical models are forms of scalar narrative affording epidemics their scale. • Models and projections are made useful as evidence in their situated policy events. • Calculations are given agency as affects in the evidencing of pandemics. • Modelling pandemics is a site of 'uncomfortable science' and 'dis-ease'. [ABSTRACT FROM AUTHOR]

The most common reaction to suggesting that we could learn valuable lessons from the way the current pandemic has been/ is being handled, is to discourage the attempt; as it is suggested that it can all be done more accurately and authoritatively after the inevitable Public Inquiry (Slater, 2019). On the other hand, a more constructive approach, is to capture and understand the work that was actually done.This would include normal activities, as well as positive adaptations to challenges and failures that may have occurred. Such an approach aimed at improving what worked, rather than blaming people for what went wrong, has the potential to contribute more successfully to controlling the consequences of the current crisis. Such an approach should thus be aimed at detecting and feeding back lessons from emerging and probably unexpected behaviours and helping to design the system to adapt better to counter the effects. The science and discipline of Human Factors (HF) promotes system resilience. This can be defined as an organisation's ability to adjust its functioning before, during or after significant disturbances (such as a pandemic), enabling adaptation and operation under both anticipated and unanticipated circumstances. A "functional" approach methodology enables the identification of where the system and its various interdependent functions (an activity or set of activities that are required to give a certain output), could be improved and strengthened; if not immediately, at least for the future. Along these lines, suggestions for adding key resilience functions are additionally identified and outlined. The application and insights gained from this functional approach to the 2015 MERS-Cov pandemic in South Korea has been seen as contributing substantially to the effective response to the current crisis in that country (Min, submitted for publication). In this paper, we present an overarching framework for a series of projects that are planned to carry out focussed systems-based analysis to generate learning from key aspects of the COVID-19 pandemic response in the United Kingdom. [ABSTRACT FROM AUTHOR]

The economic impact of COVID-19 has exacerbated inequalities in society, but disability has been neglected. This paper contributes to this knowledge gap by providing a comprehensive analysis of the differential labour market impact of COVID-19 by disability in the UK. Using data from the Labour Force Survey before and during the pandemic it estimates disability gaps in pre-pandemic risk factors, as well as changes in labour market inequality nearly one year on. Disabled workers are found to face higher COVID-19-related economic and health risks, including being more likely to work in 'shutdown' industries, and in occupations with greater proximity to others and exposure to disease. However, established measures of inequality, including the disability employment and pay gap suggest limited impact of COVID-19 in 2020. Nevertheless, the increase in the probability of being temporarily away from work, even among otherwise comparable workers, is 40% higher for disabled workers and consistent with disproportionate use of the government's job retention scheme. While the reasons for this are likely to be complex, there is a risk that it will contribute to future disability-related labour market inequality. • We explore disability-related labour market inequality in the UK during COVID-19. • We estimate COVID-19 work-related risk factors and changes in outcomes. • Disabled workers face greater COVID-19 economic and health risks pre-pandemic. • Disability gaps in employment and pay exhibit minimal change one year post-pandemic. • Disabled workers have a markedly larger rise in being temporarily away from work. [ABSTRACT FROM AUTHOR]

Due to lockdown measures taken by the UK government during the Coronavirus disease 2019 pandemic, the national electricity demand profile presented a notably different performance. The Coronavirus disease 2019 crisis has provided a unique opportunity to investigate how such a landscape-scale lockdown can influence the national electricity system. However, the impacts of social and economic restrictions on daily electricity demands are still poorly understood. This paper investigated how the UK-wide electricity demand was influenced during the Coronavirus disease 2019 crisis based on multivariate time series forecasting with Bidirectional Long Short Term Memory, to comprehend its correlations with containment measures, weather conditions, and renewable energy supplies. A deep-learning-based predictive model was established for daily electricity demand time series forecasting, which was trained by multiple features, including the number of coronavirus tests (smoothed), wind speed, ambient temperature, biomass, solar & wind power supplies, and historical electricity demand. Besides, the effects of Coronavirus disease 2019 pandemic on the Net-Zero target of 2050 were also studied through an interlinked approach. • Impact of COVID-19 measures on UK electricity demands are quantitatively identified. • Nationwide lockdown restrictions accelerated the UK energy demand reduction. • Renewable supplies will keep growing across the UK in the post-epidemic era. [ABSTRACT FROM AUTHOR]

• The study surveyed participants during and after the first COVID-19 peak in the UK. • Perceived access to public and private green space are linked to better health and wellbeing. • Private gardens can compensate for a lack of perceived access to public green spaces. • Public green spaces are more protective for those without a private garden. • Public and private green space are an essential health resource in times of crisis. Research has consistently shown that access to parks and gardens is beneficial to people's health and wellbeing. In this paper, we explore the role of both public and private green space in subjective health and wellbeing during and after the first peak of the COVID-19 outbreak that took place in the UK in the first half of 2020. It makes use of the longitudinal COVID-19 Public Experiences (COPE) study, with baseline data collected in March/April 2020 (during the first peak) and follow-up data collected in June/July 2020 (after the first peak) which included an optional module that asked respondents about their home and neighbourhood (n = 5,566). Regression analyses revealed that both perceived access to public green space (e.g. a park or woodland) and reported access to a private green space (a private garden) were associated with better subjective wellbeing and self-rated health. In line with the health compensation hypothesis for green space, private gardens had a greater protective effect where the nearest green space was perceived to be more than a 10-minute walk away. This interaction was however only present during the first COVID-19 peak when severe lockdown restrictions came into place, but not in the post-peak period when restrictions were being eased. The study found few differences across demographic groups. A private garden was relatively more beneficial for men than for women during but not after the first peak. The results suggest that both public and private green space are an important resource for health and wellbeing in times of crisis. [ABSTRACT FROM AUTHOR]