Technical and methodological data from the Coronavirus (COVID-19) Infection Survey, England, Wales, Northern Ireland and Scotland.

COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmathsThis work is described in more detail on the UK Data Service Impact and Innovation Lab blog: https://blog.ukdataservice.ac.uk/visualising-high-risk-areas-for-covid-19-mortality/Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Dataset description

Daily official UK Covid data. The data is available per country (England, Scotland, Wales and Northern Ireland) and for different regions in England. The different regions are split into two different files as part of the data is directly gathered by the NHS (National Health Service). The files that contain the word 'nhsregion' in their name, include data related to hospitals only, such as number of admissions or number of people in respirators. The files containing the word 'region' in their name, include the rest of the data, such as number of cases, number of vaccinated people or number of tests performed per day. The next paragraphs describe the columns for the different file types.

Region files

Files related to regions (word 'region' included in the file name) have the following columns: - "date": date in YYYY-MM-DD format - "area type": type of area covered in the file (region or nation) - "area name": name of area covered in the file (region or nation name) - "daily cases": new cases on a given date - "cum cases": cumulative cases - "new deaths 28days": new deaths within 28 days of a positive test - "cum deaths 28days": cumulative deaths within 28 days of a positive test - "new deaths_60days": new deaths within 60 days of a positive test - "cum deaths 60days": cumulative deaths within 60 days of a positive test - "new_first_episode": new first episodes by date - "cum_first_episode": cumulative first episodes by date - "new_reinfections": new reinfections by specimen data - "cum_reinfections": cumualtive reinfections by specimen data - "new_virus_test": new virus tests by date - "cum_virus_test": cumulative virus tests by date - "new_pcr_test": new PCR tests by date - "cum_pcr_test": cumulative PCR tests by date - "new_lfd_test": new LFD tests by date - "cum_lfd_test": cumulative LFD tests by date - "test_roll_pos_pct": percentage of unique case positivity by date rolling sum - "test_roll_people": unique people tested by date rolling sum - "new first dose": new people vaccinated with a first dose - "cum first dose": cumulative people vaccinated with a first dose - "new second dose": new people vaccinated with a first dose - "cum second dose": cumulative people vaccinated with a first dose - "new third dose": new people vaccinated with a booster or third dose - "cum third dose": cumulative people vaccinated with a booster or third dose

Country files

Files related to countries (England, Northern Ireland, Scotland and Wales) have the above columns and also: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

NHS Region files

Files related to nhsregion (word 'nhsregion' included in the file name) have the following columns: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

It's worth noting that the dataset hasn't been cleaned and it needs cleaning. Also, different files have different null columns. This isn't an error in the dataset but the way different countries and regions report the data.

The data source for this dataset is the NI Vaccine Management System (VMS). VMS holds vaccination reports for COVID-19 and influenza vaccines which were either administered in NI or to NI residents. This dataset is an aggregated summary of COVID-19 vaccinations recorded in VMS. It is effectively a day-by-day count of living people vaccinated by dose, age band (on the day that the dataset was extracted from VMS) and LGD of residence. Aggregated summary data from VMS is published daily to the NI COVID-19 Vaccinations Dashboard. This dataset is updated weekly and allows NI vaccination coverage to be included in the GOV.UK Coronavirus (COVID-19) in the UK dashboard.

Dataset

This dataset was created by noam kochavi

Released under CC0: Public Domain

Contents

• National Immunization Survey Adult COVID Module (NIS-ACM): CDC is providing information on the updated 2024-25 COVID-19 vaccine, the 2024-25 seasonal flu vaccine, and the RSV vaccine uptake and confidence. These data represent trends in vaccination status and intent, and other behavioral indicators, by demographics and other characteristics.

• The data start in September 2024.

• The archived data can be found here: - 2023-24 season: https://data.cdc.gov/Vaccinations/National-Immunization-Survey-Adult-COVID-Module-NI/uc4z-hbsd/about_data - Before October 2023:
https://data.cdc.gov/Vaccinations/National-Immunization-Survey-Adult-COVID-Module-NI/udsf-9v7b/about_data

Dataset of plainary Figure 1-8

National Immunization Survey Child COVID Module (NIS-CCM): CDC is providing information on COVID-19 vaccine confidence to supplement vaccine administration data. These data represent trends in vaccination status and intent, and other behavioral indicators, by demographics and other characteristics.

ABSTRACT Background: The inflammatory response plays a significant role in the outcome of coronavirus disease (COVID-19). Methods: We investigated plasma cytokine and chemokine concentrations in non-infected (NI), asymptomatic severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-infected blood donors (AS), and patients with severe COVID-19 (SC). Results: The SC group showed significantly higher levels of interleukin 6 (IL-6), IL-10, and CCL5 than the AS and NI groups. The SC and AS groups had considerably greater CXCL9 and CXCL10 concentrations than the NI group. Only NI and infected people showed separate clusters in the principal component analysis. Conclusions: SC, as well as AS was characterized by an inflammatory profile.

Background: The Coronavirus Disease 2019 (COVID-19) epidemic broke out in Wuhan, China, and it spread rapidly. Since January 23, 2020, China has launched a series of unusual and strict measures, including the lockdown of Wuhan city to contain this highly contagious disease. We collected the epidemiological data to analyze the trend of this epidemic in China.Methods: We closely tracked the Chinese and global official websites to collect the epidemiological information about COVID-19. The number of total and daily new confirmed cases of COVID-19 in China was presented to illustrate the trend of this epidemic.Results: On January 23, 2020, 835 confirmed COVID-19 cases were reported in China. On February 6, 2020, there were 31,211 cases. By February 20, 2020, the number reached as high as 75,993. Most cases were distributed in and around Wuhan, Hubei province. Since January 23, 2020, the number of daily new cases in China except Hubei province reached a peak of 890 on the eleventh day and then it declined to a low level of 34 within two full-length incubation periods (28 days), and the number of daily new cases in Hubei also started to decrease on the twelfth day, from 3,156 on February 4, 2020 to 955 on February 15, 2020.Conclusion: The COVID-19 epidemic has been primarily contained in China. The battle against this epidemic in China has provided valuable experiences for the rest of the world. Strict measures need to be taken as earlier as possible to prevent its spread.

This dataset tracks the updates made on the dataset "National Immunization Survey Adult COVID Module (NIS-ACM): Trends in Behavioral Indicators Among Unvaccinated People" as a repository for previous versions of the data and metadata.

In order to access primary care services in Northern Ireland, patients need to register with a GP practice. Registrations can be divided into different types: first registrations, transfers from other parts of the UK, migrant registrations and service related registrations. Individual registrations will be deducted from the index of registered patients for a number of reasons including notification of death, emigration, returning to their home country, moving to Great Britain etc. There may be a lag between a patient presenting themselves at a GP Practice and completion of registration. This lag may be greater for patients who have to provide additional documentation as proof of entitlement to services. Similarly for deductions, there may be a lag in removing individuals from the index of registered patients.

Given the sensitive nature of the data, this dataset is primarily used to identify patient populations and facilitate linkage to other datasets. Some variables may be provided in aggregated format, for example age may be replaced with age band and postcode replaced with higher level geographical classifications.

GP Cypher codes and Practice numbers will not be provided.

This dataset tracks the updates made on the dataset "National Immunization Survey Child COVID Module (NIS-CCM): Vaccination Status and Intent by Demographics | Data | Centers for Disease Control and Prevention (cdc.gov)" as a repository for previous versions of the data and metadata.

From 18 August 2020 to 21 June 2021 a survey was issued to educational settings in Northern Ireland. The management information, relating to staff and pupil attendance during this time, presented in the following link is derived from this temporary data collection from grant-aided schools and educational settings. Figures reflect the responses made by settings to the survey.

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

The COVID-19 pandemic caused far-reaching societal changes, including significant educational impacts affecting over 1.6 billion pupils and 100 million education practitioners globally. Senior school leaders were at the forefront and were exposed to particularly high demands during a period of “crisis leadership”. This occupation were already reporting high work-related stress and large numbers leaving the profession preceding COVID-19. This cross-sectional descriptive study through the international COVID-Health Literacy network aimed to examine the well-being and work-related stress of senior school leaders (n = 323) in Wales (n = 172) and Northern Ireland (n = 151) during COVID-19 (2021–2022). Findings suggest that senior school leaders reported high workloads (54.22±11.30 hours/week), low well-being (65.2% n = 202, mean WHO-5 40.85±21.57), depressive symptoms (WHO-5 34.8% n = 108) and high work-related stress (PSS-10: 29.91±4.92). High exhaustion (BAT: high/very high 89.0% n = 285) and specific psychosomatic complaints (experiencing muscle pain 48.2% n = 151) were also reported, and females had statistically higher outcomes in these areas. School leaders were engaging in self-endangering working behaviours; 74.7% (n = 239) gave up leisure activities in favour of work and 63.4% (n = 202) sacrificed sufficient sleep, which was statistically higher for females. These findings are concerning given that the UK is currently experiencing a “crisis” in educational leadership against a backdrop of pandemic-related pressures. Senior leaders’ high attrition rates further exacerbate this, proving costly to educational systems and placing additional financial and other pressures on educational settings and policy response. This has implications for senior leaders and pupil-level outcomes including health, well-being and educational attainment, requiring urgent tailored and targeted support from the education and health sectors. This is particularly pertinent for Wales and Northern Ireland as devolved nations in the UK, who are both implementing or contemplating major education system level reforms, including new statutory national curricula, requiring significant leadership, engagement and ownership from the education profession.

The high infection rate and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) make it a world-wide pandemic. Individuals infected by the virus exhibited different degrees of symptoms, and most convalescent individuals have been shown to develop both cellular and humoral immune responses. However, virus-specific adaptive immune responses in severe patients during acute phase have not been thoroughly studied. Here, we found that in a group of COVID-19 patients with acute respiratory distress syndrome (ARDS) during hospitalization, most of them mounted SARS-CoV-2-specific antibody responses, including neutralizing antibodies. However, compared to healthy controls, the percentages and absolute numbers of both NK cells and CD8+ T cells were significantly reduced, with decreased IFNγ expression in CD4+ T cells in peripheral blood from severe patients. Most notably, their peripheral blood lymphocytes failed in producing IFNγ against viral proteins. Thus, severe COVID-19 patients at acute infection stage developed SARS-CoV-2-specific antibody responses but were impaired in cellular immunity, which emphasizes on the role of cellular immunity in COVID-19.