According to survey carried out in Great Britain in March 2020, 17 percent of respondents strongly approve of the government's coronavirus (COVID-19) response, while a further 39 percent somewhat approve of the way the government is responding. On the other hand, 21 percent of respondents overall disapprove of the government's response to coronavirus pandemic. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Release model requires permission from Fiona Stevenson for data protection purposes. For access to this dataset please contact f.stevenson@ucl.ac.uk

Please find further information regarding this dataset in the attached file. Design Cross-sectional single-arm service evaluation of real-time user data. Setting 31 Post-COVID clinics in the UK. Participants 3,754 adults diagnosed with PCS in primary or secondary care, deemed suitable for rehabilitation. Intervention Patients using the Living With Covid Recovery (LWCR) Digital Health Intervention (DHI) registered between 30/11/20 and 23/03/22. Primary and secondary outcome measures The primary outcome was the baseline Work and Social Adjustment Scale (WSAS). WSAS measures the functional limitations of the patient; scores ≥20 indicate moderately severe limitations. Other symptom data collected included fatigue (FACIT-F), depression (PHQ-8), anxiety (GAD-7), breathlessness (MRC Dyspnoea Scale and Dyspnoea-12), cognitive impairment (PDQ-5) and health-related quality of life (EQ-5D).

Data collection period 30/11/20 to 17/7/22 (inclusive)

Datasets and interview transcripts from a Q-methodology study with 54 individuals with a range of different experiences of, and expertise in relation to, the COVID-19 pandemic. Participants included, for example, seldom-heard and low-income individuals, health practitioners, health and social policy academics and relevant policy makers, key workers, furloughed staff, and individuals directed to shield by the NHS. Participants from England and Scotland rank ordered 60 statements onto a quasi-normal shaped grid according to their point of view in 2021. The dataset includes data from the Q sorts (n=54), socio-demographic survey (n=54) and post-sort qualitative interviews (n=53).

Governments are taking a wide range of measures in response to the COVID-19 outbreak. The Oxford COVID-19 Government Response Tracker (OxCGRT) aims track and compare government responses to the coronavirus outbreak worldwide rigorously and consistently.

The OxCGRT systematically collects information on several different common policy responses governments have taken, scores the stringency of such measures, and aggregates these scores into a common Stringency Index. For more, please visit > https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker

This is the README file for the scripts of the preprint "Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication Study" by Carollo et al. (2022)

Access the pre-print here: https://ucl.scienceopen.com/document/read?vid=0769d88b-e572-48eb-9a71-23ea1d32cecf

Abstract: Background: The global COVID-19 pandemic has forced countries to impose strict lockdown restrictions and mandatory stay-at-home orders with varying impacts on individual’s health. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. Methods: We tested a) the impact of the chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Two new machine learning models - namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable in the UK dataset from wave 1 (n = 435). In the second part of the study, we tested b) whether the pattern of self-perceived loneliness found in the first UK national lockdown was generalizable to the second wave of UK lockdown (17 October 2020 to 31 January 2021). To do so, data from wave 2 of the UK lockdown (n = 263) was used to conduct a graphical and statistical inspection of the week-by-week distribution of self-perceived loneliness scores. Results: In both SVR and MLR models, depressive symptoms resulted to be the most time-sensitive variable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shaped pattern between week 3 to 7 of wave 1 of the UK national lockdown. Furthermore, despite the sample size by week in wave 2 was too small for having a meaningful statistical insight, a qualitative and descriptive approach was adopted and a graphical U-shaped distribution between week 3 and 9 of lockdown was observed. Conclusions: Consistent with past studies, study findings suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions.

In particular, the folder includes the scripts for the pre-processing, training, and post-processing phases of the research.

==== PRE-PROCESSING WAVE 1 DATASET ==== - "01_preprocessingWave1.py": this file include the pre-processing of the variables of interest for wave 1 data; - "02_participantsexcludedWave1.py": this file include the script adopted to implement the exclusion criteria of the study for wave 1 data; - "03_countryselectionWave1.py": this file include the script to select the UK dataset for wave 1.

==== PRE-PROCESSING WAVE 2 DATASET ==== - "04_preprocessingWave1.py": this file include the pre-processing of the variables of interest for wave 2 data; - "05_participantsexcludedWave1.py": this file include the script adopted to implement the exclusion criteria of the study for wave 2 data; - "06_countryselectionWave1.py": this file include the script to select the UK dataset for wave 2.

==== TRAINING ==== - "07_MLR.py": this file includes the script to run the multiple regression model; - "08_SVM.py": this file includes the script to run the support vector regression model.

==== POST-PROCESSING: STATISTICAL ANALYSIS ==== - "09_KruskalWallisTests.py": this file includes the script to run the multipair and the pairwise Kruskal-Wallis tests.

As of January 12, 2023, COVID-19 has been responsible for 202,157 deaths in the UK overall. The North West of England has been the most affected area in terms of deaths at 28,116, followed by the South East of England with 26,221 coronavirus deaths. Furthermore, there have been 22,264 mortalities in London as a result of COVID-19.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

The Coronavirus outbreak had an impact on the exports and imports of businesses in the United Kingdom (UK). According to the results of a recent survey, the Coronavirus' impact on the imports of businesses was more highly rated than its impact on exports. Almost one-quarter of all UK businesses surveyed stated that their imports sector was affected.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

Descriptive statistic of the explanatory variables.

Request I believe the above scheme needs to be put in place urgently. Can you please answer the following questions: 1. How many people have applied to you for Ill Health Retirement with Long Covid? 2. How many people have been rejected for Tier One and/or Tier Two levels of IHR when applying with Long Covid? 3. What evidence (listing guidance and research evidence) are being used to reject or confirm applications for IHR with Long Covid? Response Question 1 & 2 A copy of the information is attached. Question 3 Each Scheme Medical Adviser (SMA) is expected to adopt evidence-based practice in arriving at a decision. They do this by combining the following: Medical evidence provided in the Scheme member’s application, Further medical evidence that the SMA may have requested from the Scheme member’s treating healthcare professionals, Information that the employer may have provided in Part A of Form AW33E (e.g. demands of the work duties, any workplace adjustments tried, and the effectiveness of such adjustments), Information that the Scheme member may have provided in Part B of Form AW33E (for example, how long COVID affects them), Current medical literature on long COVID, And the SMA’s occupational health expertise. When assessing ill-health retirement applications from scheme members who have long COVID, the SMA might consult the following guidance and research evidence: • The Society of Occupational Medicine (SOM): ‘Long COVID and Return to Work – What Works?’ (https://www.som.org.uk/sites/som.org.uk/files/Long_COVID_and_Return_to_Work_What_Works_0.pdf) • The Faculty of Occupational Medicine (FOM): ‘Guidance for healthcare professionals on return to work for patients with post-COVID syndrome’ (https://www.fom.ac.uk/wp-content/uploads/FOM-Guidance-post-COVID_healthcare-professionals.pdf) • Occupational and Environmental Medicine (academic journal of the FOM: https://oem.bmj.com) • Occupational Medicine (academic journal of the SOM: https://academic.oup.com/occmed?login=false) • Industrial Injuries Advisory Council publication: ‘COVID-19 and Occupational Impacts’ (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1119955/covid-19-and-occupational-impacts.pdf) • NICE: https://cks.nice.org.uk/topics/long-term-effects-of-coronavirus-long-covid • Nature. An example of a recent publication in this journal is Davis, H., McCorkell, L., Vogel, J. M., & Topol, E. J. (2023). Long covid: major findings, mechanisms and recommendations. Nature Reviews Microbiology, 21(3), 133-146. Full text available at https://www.nature.com/articles/s41579-022-00846-2 • British Medical Journal (BMJ) • Journal of the American Medical Association (JAMA) • The Lancet • New England Journal of Medicine In summary, the SMA is expected to adopt an individual approach to each case and use careful clinical judgement when applying the medical research literature and guidance to the specific medical circumstances of a Scheme member with long COVID.

This statistic presents ratings of the level of external economic and financial uncertainty influencing the largest companies in the United Kingdom, according to their chief financial officers. Data is presented quarterly from surveys conducted in the first quarter of 2016 to the second quarter of 2021. Owing to the global Coronavirus (COVID-19) pandemic, just over 70 percent of respondents rated the level of uncertainty as high or very high in the fourth quarter of 2020. By the second quarter of 2021, this had halved to 35 percent.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

As of November 24, 2024 there were over 274 million confirmed cases of coronavirus (COVID-19) across the whole of Europe since the first confirmed cases in France in January 2020. France has been the worst affected country in Europe with 39,028,437 confirmed cases, followed by Germany with 38,437,756 cases. Italy and the UK have approximately 26.8 million and 25 million cases respectively. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

Estimated effects on effective reproduction number.

Notes:

Trends in Covid total deaths per million. The latest data for over 100 countries around the world.

Objectives: This study investigated perceived loneliness, anxiety, and depression among young adults in the UK across five timepoints: pre-pandemic (December 2019), two coronavirus disease (COVID-19) lockdowns (March–June 2020, January–April 2021), and two post-lockdown phases (November–December 2021, May 2022). It aimed to assess mental health resilience, defined as a return to baseline levels post-lockdown, and identify critical timepoints where loneliness predicted mental health outcomes.Methods: A total of 158 participants (aged 18–82, predominantly under 25) completed online questionnaires measuring mental health (Patient Health Questionnaire-8 (PHQ-8); General Anxiety Disorder-7 (GAD-7)) and loneliness (DeJong Gierveld Loneliness Scale) at two data collection points, under a cross-sectional design. Retrospective data were collected for pre-pandemic and lockdown periods, while prospective data were gathered post-lockdown. Linear mixed models and regression analyses were used to examine changes in mental health and loneliness over time and to identify predictive relationships.Results: Loneliness and mental health significantly deteriorated during lockdowns, with depression and anxiety scores worsening from pre-pandemic levels. Partial recovery was observed post-lockdown, but scores remained above baseline. Loneliness emerged as a key predictor of mental health outcomes, particularly during post-lockdown phases. The immediate post-lockdown period was identified as a critical window for interventions.Conclusions: COVID-19 lockdowns were associated with heightened loneliness and mental health challenges, with sustained effects post-lockdown. Timely interventions targeting loneliness, especially after periods of social restriction, are essential to mitigate long-term mental health impacts and inform future responses to global crises.