The coronavirus (COVID-19) pandemic had a major impact on businesses within the accommodation and food service industry in the United Kingdom. Fewer businesses were trading at the start of the outbreak in March 2020, with the lowest value recorded during the first few weeks of lockdown from April 6-19 (18.2 percent). Since July most businesses have returned to trading, reaching close to 90 percent in the first week of August. However, the share of accommodation and food service businesses trading have fluctuated from September 21 onwards. By March 21, 2021, the number of businesses trading reached 37.3 percent.

As of May 21, 2020, about one third of respondents in the United Kingdom planned to spend their annual leave on holidays in the UK if travel abroad was still difficult due to lockdown restrictions. Over a quarter of respondents expected to spend more time at home.

As of August 2020, about 65 percent of businesses in the accommodation and food service sector in the United Kingdom had experienced a decrease in footfall in the last two weeks due to the ongoing coronavirus pandemic. For approximately 10.6 percent of businesses in the industry, footfall had increased.

Objectives: To assess the percentage of people in the UK with cough, fever or loss of taste or smell who have not had a positive COVID-19 test result who had been to work, to shops, socialised or provided care to a vulnerable person in the 10 days after developing symptoms. To investigate whether these rates differed according to the type of symptom, what the participant thought the cause of their symptoms was and whether they had taken a COVID-19 test.Design: Four online cross-sectional surveys using non-probability quota sampling method (n=8547).Setting: Data were collected across the UK from 20 September to 3 November 2021, via a market research company.Participants: Aged over 16 years living in the UK.Primary outcome measures: Out-of-home activity.Results: 498 participants reported one or more symptoms and had not had a positive COVID-19 test result. Within that group, about half of employed participants had attended work while symptomatic (51.2%-56.3% depending on the symptom, 95% CIs 42.2% to 65.6%). Rates of other contact behaviours ranged from 31.4% (caring for a vulnerable person after developing a cough: 95% CI 24.3% to 38.4%) to 61.5% (shopping for groceries or pharmacy after developing a cough: 95% CI 54.1% to 68.9%). There were no differences according to type of symptom experienced or what the participant felt might be the cause. People who had taken a COVID-19 test were less likely to go out shopping for non-essentials than people who had not taken a test.Conclusion: Many people in the UK with symptoms of an infectious disease were not following government advice to stay at home if they believed they had an infectious illness. Reducing these rates may require a shift in our national attitude to the acceptability of people attending work with infectious illnesses.

11th January 2020 Change to vaccination data made available by UK gov - now just cumulative number of vaccines delivered are available for both first and second doses. For the devolved nations the cumulative totals are available for the dates from when given, however for the UK as a whole the total doses given is just on the last date of the index, regardless of when those vaccines were given.

4th January 2020 VACCINATION DATA ADDED - New and Cumulative First Dose Vaccination Data added to UK_National_Total_COVID_Dataset.csv and UK_Devolved_Nations_COVID_Dataset.csv

2nd December 2020:

NEW population, land area and population density data added in file NEW_Official_Population_Data_ONS_mid-2019.csv. This data is scraped from the Office for National Statistics and covers the UK, devolved UK nations, regions and local authorities (boroughs).

20th November 2020:

With European governments struggling with a 'second-wave' of rising cases, hospitalisations and deaths resulting from the SARS-CoV-2 virus (COVID-19), I wanted to make a comparative analysis between the data coming out of major European nations since the start of the pandemic.

I started by creating a Sweden COVID-19 dataset and now I'm looking at my own country, the United Kingdom.

The data comes from https://coronavirus.data.gov.uk/ and I used the Developer's Guide to scrape the data, so it was a fairly simple process. The notebook that scapes the data is public and can be found here. Further information about data collection methodologies and definitions can be found here.

The data includes the overall numbers for the UK as a whole, the numbers for each of the devolved UK nations (Eng, Sco, Wal & NI), English Regions and Upper Tier Local Authorities (UTLA) for all of the UK (what we call Boroughs). I have also included a small table with the populations of the 4 devolved UK nations, used to calculate the death rates per 100,000 population.

As I've said for before - I am not an Epidemiologist, Sociologist or even a Data Scientist. I am actually a Mechanical Engineer! The objective here is to improve my data science skills and maybe provide some useful data to the wider community.

Any questions, comments or suggestions are most welcome! I am open to requests and collaborations! Stay Safe!

In May 2020, 28 percent of UK residents said that due to the coronavirus (COVID-19) pandemic they were more likely to take a holiday elsewhere in the UK in 2020. However 43 percent felt that the pandemic had not affected their decision to vacation domestically.

This file contains daily attendance data for state-funded education settings for dates excluding half term and Easter dates. Data is in this file has been scaled to account for non-response so it is nationally representative.Further details on the column headings can be found in the “Table 1b variable additions and removal log” ancillary file.

Travel was brought to a standstill by COVID-19, threatening the UK economy. With restrictions now lifted, let's review some emerging trends in the UK tourism industry.

The Covid Winter Grant (CWG) was a fund provided by the Department for Work and Pensions and administered by local authorities in England.The purpose of the grant was to support those households which had been economically affected by the COVID pandemic and sought to provide immediate short term relief in the form of awards to these people.The criteria for allocation of funds was dictated by the DWP and awards could be given to support the provision of food, payments of energy and water bills and other essential items required during the winter period.The scheme ran from the 1st December 2020 through to 31st March 2021.The data provided in this set highlights how Leicester City Council distributed the grant received showing the number of awards made, number of rejections and spend under the DWP categories.This grant was also used to directly fund the provision of Free School Meals during the school holidays (Christmas break and February half term break) and this is highlighted in the dataset under "Direct Awards".The dataset also shows a map detailing how the fund was distributed across the wards of Leicester and there is also a supporting data set named "COVID Winter Grant Ward Data" which also provides this more granular information.

Holiday accommodation providers rely significantly on domestic tourists, with demand for holiday spending driven by consumer confidence and disposable income levels. Companies have faced significant volatility due to swings in tourism numbers, including the plunge caused by the COVID-19 pandemic followed by a strong recovery in domestic travel. Revenue is expected to soar at a compound annual rate of 11.1% over the five years through 2025-26 to £4.1 billion, despite a forecast dip of 0.1% in 2025-26. The growth rate has been skewed and inflated due to the significantly low value in the base year of 2020-21 amid the COVID-19-driven collapse in tourism. Revenue bounced back once COVID-19 restrictions were lifted, with revenue surging over the two years through 2022-23 amid a significant boost from the staycation trend. However, revenue growth had been held back since late 2022-23 by the cost-of-living crisis tightening consumers' purse strings. Significant consumer demand for holidays and soaring inflation encouraged holiday accommodation providers to hike prices, boosting revenue from bookings but also putting some price-sensitive consumers off from staying at industry accommodation. A slowdown in staycations and prolonged financial challenges have weakened revenue over the three years through 2025-26.The emergence of online travel agents has made it easier for independent accommodation providers to compete with larger companies, enticing newcomers into the industry. However, the enduring popularity of online private short-term rentals like Airbnb steals away guests. Intensifying competition has placed pressure on prices, which, alongside severe inflationary pressures, has weighed on the average profit margin, which is estimated to be 14.7% in 2025-26. Revenue is forecast to mount at a compound annual rate of 3.1% over the five years through 2030-31 to £4.7 billion. Climbing domestic and international visitor numbers will support growth. Given the anticipated expansion in inbound visits to the UK, companies must find ways to attract foreign travellers, who typically stay at hotels or use home-sharing platforms. Growing disposable incomes will spur consumer spending on holiday trips, though this may also lead to some travelling abroad or staying at more upscale hotels. That being said, home-sharing platforms like Airbnb and competitively-priced hotels investing in enhancing facilities and offerings will continue to lure consumers away from holiday accommodation providers. Industry companies will have to bump up investment in technology and sustainability to remain competitive and attract guests. Intense price competition and elevated staff costs will continue to weigh on revenue and profit.

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.

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Hotels (establishments that provide paid lodging and full guest services, typically with a continuous staff presence) market have seen moderate change in the recent years and is also expected to evolve in similar fashion in the near future. The report United Kingdom Hotels Market Analytics to 2024: Rooms and Revenue Analytics provides deep dive data analytics on wide ranging Hotels business aspects including overall revenue by customer type – Business and Leisure, by type of hotel – Budget, Midscale, Upscale & Luxury, Room & Non-Room Revenues, Number of Establishments & Rooms and Guest In-Flow’s for the period 2015 to 2019 and forecast to 2024. Read More

This file contains daily attendance data for state-funded education settings for dates affected by October half term and Easter break only.Settings on half term or Easter break are excluded from these figures. For this reason, data within this file is not comparable to data within table 1b. Data is in this file has been scaled to account for non-response so it is nationally representative of settings that were not on half term or Easter break.

Variables included in the dataset.GenderStay-at-home factor scoreAgeEmployment statusCurrent living arrangementsCovid-19-like symptomsDistancing - “self-isolating” itemDistancing - “social-distancing” itemDistancing - “reduced contact” itemExtraversionAgreeablenessConscientiousnessEmotional stabilityOpenness to experienceSocial connectedness

This is an indicator designed to accompany the Summary Hospital-level Mortality Indicator (SHMI). 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. This indicator shows the number of provider spells which are coded as COVID-19, and therefore excluded from the SHMI, as a percentage of all provider spells in the SHMI (prior to the exclusion). This indicator is being published as an experimental statistic. 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. Please note that there has been a fall in the number of spells for most trusts between this publication and the previous SHMI publication, ranging from 0 per cent to 5 per cent. This is due to COVID-19 impacting on activity from March 2020 onwards and appears to be an accurate reflection of hospital activity rather than a case of missing data. 2. The data for St Helens and Knowsley Teaching Hospitals NHS Trust (trust code RBN) has incomplete information on secondary conditions that the patients suffers from, and this will have affected the calculation of this indicator. Values for this trust should therefore be interpreted with caution. Please note, this issue was not identified until after this publication was initially released on 13th May 2021. Data quality notices were later added to this publication in July 2021. 3. Day cases and regular day attenders are excluded from the SHMI. However, some day cases for University College London Hospitals NHS Foundation Trust (trust code RRV) have been incorrectly classified as ordinary admissions meaning that they have been included in the SHMI. Maidstone and Tunbridge Wells NHS Trust (trust code RWF) has submitted a number of records with a patient classification of ‘day case’ or ‘regular day attender’ and an intended management value of ‘patient to stay in hospital for at least one night’. This mismatch has resulted in the patient classification being updated to ‘ordinary admission’ by the HES data cleaning rules. This may have resulted in the number of ordinary admissions being overstated. The trust has been contacted to clarify what the correct patient classification is for these records. Values for these trusts should therefore be interpreted with caution. 4. There is a shortfall in the number of records for Mid Cheshire Hospitals NHS Foundation Trust (trust code RBT), meaning that values for this trust are based on incomplete data and should therefore be interpreted with caution. 5. We recommend that values for Guy’s and St Thomas’ NHS Foundation Trust (trust code RJ1) are interpreted with caution as there is a possible shortfall in the number of records which is currently under investigation. 6. On 1 April 2021 Western Sussex Hospitals NHS Foundation Trust (trust code RYR) merged with Brighton and Sussex University Hospitals NHS Trust (trust code RXH). The new trust is called University Hospitals Sussex NHS Foundation Trust (trust code RYR). However, as we received notification of this change after data processing for this publication began, separate indicator values have been produced for this publication. The next publication in this series will reflect the updated organisation structure. 7. 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.