Data acquired with SWATH MS then underwent protein identification using the twin plasma library and the new z-scores merged library. Here is the intensity data for these library searches.

This statistic presents the distribution of estimated income losses of Manchester City Football Club as a result of the ongoing COVID-19 containment measures. Although these figures are illustrative, not definitive, and an array of other outcomes is possible, these data suggests that Manchester City will experience a total loss of income of over *** million British pounds, of which **** million British pounds will be lost from Premier League TV revenue.

This dataset comprises of 16 anonymised interview transcripts with older adults aged 65 years and over living in areas of high socioeconomic deprivation in Manchester. These transcripts provide detail about participants' experiences of and attitudes towards engaging in physical activity in the aftermath of the COVID-19 pandemic. They were analysed using reflexive thematic analysis.

This review summarizes the economic impacts of the pandemic on ethnic minorities, focusing on the city of Manchester. It utilizes multiple reporting sources to explore various dimensions of the economic shock in the UK, linking this to studies of pre-COVID-19 economic and ethnic composition in Manchester and in the combined authority area of Greater Manchester. We then make inferences about the pandemic's short-term impact specific to the city region. Greater Manchester has seen some of the highest rates of COVID-19 and as a result faced particularly stringent “lockdown” regulations. Manchester is the sixth most deprived Local Authority in England, according to 2019 English Indices of Multiple Deprivation. As a consequence, many neighborhoods in the city were always going to be less resilient to the economic shock caused by the pandemic compared with other, less-deprived, areas. Particular challenges for Manchester include the high rates of poor health, low-paid work, low qualifications, poor housing conditions and overcrowding. Ethnic minority groups also faced disparities long before the onset of the pandemic. Within the UK, ethnic minorities were found to be most disadvantaged in terms of employment and housing–particularly in large urban areas containing traditional settlement areas for ethnic minorities. Further, all Black, Asian, and Minority ethnic (BAME) groups in Greater Manchester were less likely to be employed pre-pandemic compared with White people. For example, people of Pakistani and Bangladeshi ethnic backgrounds, especially women, have the lowest levels of employment in Greater Manchester. Finally, unprecedented cuts to public spending as a result of austerity have also disproportionately affected women of an ethnic minority background alongside disabled people, the young and those with no or low-level qualifications. This environment has created and sustained a multiplicative disadvantage for Manchester's ethnic minority residents through the course of the COVID-19 pandemic.

Anxiety and depression are the most prevalent classes of mental illnesses; rates of anxiety and depression have been exacerbated due to the COVID-19 pandemic. Vulnerability to anxiety and depression are affected by risk and resilience factors, such as personality constructs. Recent research (e.g., Lyon et al, 2020; 2021) suggests that, out of all 30 NEO-PI-R personality constructs, variance in anxiety and depression are explained by a small number of personality constructs. However it is unclear which mechanisms mediate the relationship between these personality constructs and anxiety and depression. The purpose of this study was to investigate the mediating effect of emotion regulation strategies on the relationship between personality constructs and COVID-related anxiety and depression. Data were collected from a sample of 210 students at the University of Manchester. Measures included a select number of narrow Big Five personality facets which explain variance in anxiety and depression (facets depression, assertiveness, gregariousness, positive emotion and competence), select COPE Inventory strategies associated with coping with pandemics, and COVID-related anxiety and depression. Measures of COPE strategies and mental health were adapted to refer to coping and mental health in response to COVID pandemic.

High accuracy classification of COVID-19 coughs using Mel-frequency cepstral coefficients and a Convolutional Neural Network with a use case for smart home devices.

Diagnosing COVID-19 early in domestic settings is possible through smart home devices that can classify audio input of coughs, and determine whether they are COVID-19. Research is currently sparse in this area and data is difficult to obtain. How- ever, a few small data collection projects have en- abled audio classification research into the application of different machine learning classification algorithms, including Logistic Regression (LR), Support Vector Machines (SVM), and Convolution Neural Networks (CNN). We show here that a CNN using audio converted to Mel-frequency cepstral coefficient spectrogram images as input can achieve high accuracy results; with classification of validation data scoring an accuracy of 97.5% cor- rect classification of covid and not covid labelled audio. The work here provides a proof of concept that high accuracy can be achieved with a small dataset, which can have a significant impact in this area. The results are highly encouraging and provide further opportunities for research by the academic community on this important topic.

Dunne, Robert (2020), “COVID-19 CNN MFCC classifier”, The University of Manchester, V1, doi: 10.17632/ww5dfy53cw.1

Analysis of populations in the UK by likelihood of having received a third vaccination against COVID-19 using the Coronavirus (COVID-19) Infection Survey. This survey is being delivered in partnership with University of Oxford, University of Manchester, UK Health Security Agency and Wellcome Trust.

Demographics table.

This statistic presents the estimated income losses of each of the ** football clubs within the English Premier League. Although these figures are illustrative, not definitive, and an array of other outcomes is possible depending on individual clubs' situations, this data suggests that Manchester United will experience the greatest loss of income of over *** million British pounds, the largest share of which will be lost from commercial/retail revenue.

The Greater London Authority (GLA) commissioned the University of Manchester to conduct a rapid evidence review to document and understand the impact of COVID-19 (in terms of both health and the broader impacts on existing social and economic inequalities) on those with protected characteristics, as well as those living in poorer, or more precarious, socioeconomic circumstances, paying particular attention to its effect in London. The report provides the outcomes of the review, as well as a series of recommendations, which are focused on identifying tractable policy solutions in order to prevent, or mitigate, the inequalities in relation to protected characteristics and socioeconomic position that result from the COVID-19 pandemic and policy responses to it. Also available to download below is a spreadsheet documenting the formalised literature review searches.

These documents were produced through a collaboration between GLA, PHE London and Association of Directors of Public Health London. The wider impacts slide set pulls together a series of rapid evidence reviews and consultation conversations with key London stakeholders. The evidence reviews and stakeholder consultations were undertaken to explore the wider impacts of the pandemic on Londoners and the considerations for recovery within the context of improving population health outcomes. The information presented in the wider impact slides represents the emerging evidence available at the time of conducting the work (May-August 2020). The resource is not routinely updated and therefore further evidence reviews to identify more recent research and evidence should be considered alongside this resource. It is useful to look at this in conjunction with the ‘People and places in London most vulnerable to COVID-19 and its social and economic consequences’ report commissioned as part of this work programme and produced by the New Policy Institute. Additional work was also undertaken on the housing issues and priorities during COVID. A short report and examples of good practice are provided here. These reports are intended as a resource to support stakeholders in planning during the transition and recovery phase. However, they are also relevant to policy and decision-making as part of the ongoing response. The GLA have also commissioned the University of Manchester to undertake a rapid evidence review on inequalities in relation to COVID-19 and their effects on London.

Categorical variables presented as n (%) and continuous as median (IQR).

Columns indicate whether proteins were identified in all datasets, or only within acute (A1), 3-months post-hospital discharge (R1) or healthy controls (H1). If proteins were absent within a disease group, this is indicated by ‘missing’.

Multivariable models adjusted by demographics and variables showing significance at univariable analysis. Multivariable model 1: vital signs included as EWS, and not individually. Multivariable Model 2: vital signs included as separate variables, and not including EWS.

These indicators are designed to accompany the SHMI publication. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. There has been a fall in the number of spells for some trusts due to COVID-19 impacting on activity from March 2020 onwards and this appears to be an accurate reflection of hospital activity rather than a case of missing data. Contextual indicators on the number of provider spells which are excluded from the SHMI due to them being related to COVID-19 and on the number of provider spells as a percentage of pre-pandemic activity (January 2019 – December 2019) are produced to support the interpretation of the SHMI. These indicators are being published as experimental statistics. Experimental statistics are official statistics which are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. Notes: 1. There is a shortfall in the number of records for Frimley Health NHS Foundation Trust (trust code RDU), Manchester University NHS Foundation Trust (trust code R0A), Royal Surrey County Hospital NHS Foundation Trust (trust code RA2), and Wrightington, Wigan and Leigh NHS Foundation Trust (trust code RRF). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 2. There is a high percentage of invalid diagnosis codes for Hampshire Hospitals NHS Foundation Trust (trust code RN5). Values for this trust should therefore be interpreted with caution. 3. A number of trusts are currently engaging in a pilot to submit Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS), rather than the Admitted Patient Care (APC) dataset. As the SHMI is calculated using APC data, this does have the potential to impact on the SHMI value for these trusts. Trusts with SDEC activity removed from the APC data have generally seen an increase in the SHMI value. This is because the observed number of deaths remains approximately the same as the mortality rate for this cohort is very low; secondly, the expected number of deaths decreases because a large number of spells are removed, all of which would have had a small, non-zero risk of mortality contributing to the expected number of deaths. We are working to better understand the planned changes to the recording of SDEC activity and the potential impact on the SHMI. The trusts affected in this publication are: Barts Health NHS Trust (trust code R1H), Cambridge University Hospitals NHS Foundation Trust (trust code RGT), Croydon Health Services NHS Trust (trust code RJ6), Epsom and St Helier University Hospitals NHS Trust (trust code RVR), Frimley Health NHS Foundation Trust (trust code RDU), Imperial College Healthcare NHS Trust (trust code RYJ), Manchester University NHS Foundation Trust (trust code R0A), Norfolk and Norwich University Hospitals NHS Foundation Trust (trust code RM1), and University Hospitals of Derby and Burton NHS Foundation Trust (trust code RTG). 4. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

Food and drink venues, such as bars and restaurants, were allowed to resume trading in the United Kingdom (UK) in July 2020, following a nationwide lockdown due to the outbreak of coronavirus (COVID-19). As of August, the majority of establishments across Great Britain had resumed trading. In London, 71.2 percent of sites that were open pre-lockdown had re-opened to customers. This lagged behind other major cities, including Liverpool and Manchester.

The Evidence for Equality National Survey (EVENS) is a national survey that documents the experiences and attitudes of ethnic and religious minorities in Britain. EVENS was developed by the Centre on the Dynamics of Ethnicity (CoDE) in response to the disproportionate impacts of COVID-19 and is the largest and most comprehensive survey of the lives of ethnic and religious minorities in Britain for more than 25 years. EVENS used pioneering, robust survey methods to collect data in 2021 from 14,200 participants of whom 9,700 identify as from an ethnic or religious minority. The EVENS main dataset, which is available from the UK Data Service under SN 9116, covers a large number of topics including racism and discrimination, education, employment, housing and community, health, ethnic and religious identity, and social and political participation.

The EVENS Teaching Dataset provides a selection of variables in an accessible form to support the use of EVENS in teaching across a range of subjects and levels of study. The dataset includes demographic data and variables to support the analysis of:

• racism and belonging
• health and well-being during COVID-19
• political attitudes and trust.

The same data as stored in the "raw data" dataset is reformatted in "pickle" format for use in python plotting. README.txt contains descriptions of the data in each column of the table.

Roadside advertising impacts have been affected by the coronavirus outbreak in the United Kingdom (UK). However, in Edinburgh, Greater London, and Greater Manchester on the Thursday and Saturday before Easter, impacts increased compared to the week before. There was a decrease in roadside ad impacts on Good Friday and Easter Sunday. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.