According to data from Pi Datametrics, Google search volumes from January to April 2020 increased across almost all categories compared to the same period in 2019. The coronavirus outbreak may have led to the rise in search volume for some of the categories, including "Things to do at home", "Food & drink", and "Fitness equipment & classes".

According to a recent analysis of UK consumers' e-commerce website visits, there was an considerable increase in the online order volume of household hardware during the Coronavirus crisis, compared to averages before the Coronavirus hit. Other product categories to experience increase in order volumes were cosmetics, furniture, and toys with **, ** and ** percent increases, respectively. The hardest hit product category was sports equipment with a decrease of ** percent. Online orders of sex toys also dropped by ** percent in volume compared to pre-Coronavirus period.
For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

Findings from the Coronavirus (COVID-19) Infection Survey for England.

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!

COVID-19 is a infectious Disease which has infected more than 500 people in UK and many more people world-wide.

Acknowledgements Sincere thanks to Public Health England and Local governments. Source of Data: UK Government and Public Health UK

****Notes on the methodology**** This service shows case numbers as reported to Public Health England (PHE), matched to Administrative Geography Codes from the Office of National Statistics. Cases include people who have recovered.

Events are time-stamped on the date that PHE was informed of the new case or death.

The map shows circles that grow or shrink in line with the number of cases in that geographic area.

Data from Scotland, Wales and Northern Ireland is represented on the charts, total indicators and on the country level map layer.

Contains Ordnance Survey data © Crown copyright and database right 2020. Contains National Statistics data © Crown copyright and database right 2020.

Terms of Use No special restrictions or limitations on using the item’s content have been provided.

This report is part of the GlobalData COVID-19 Impact on Consumer Behavior series, which tracks how the pandemic and many factors have impacted consumer behavior and purchase decisions. Read More

Context

Public Health England (PHE) coronavirus cases reported daily. This data shows case numbers as reported to PHE. Cases includes people who have recovered.

Content

DateVal : dd/mm/yyyy CMODateCount : The number of cases reported for the day CumCases: The cumulative number of cases IncreasePercent: The percentage increase in cases from the previous day DeathPercent: The percentage increase/decrease in daily deaths from the previous day CumCases7DayAvg: 7 day moving average of the cumulative number of cases CumDeaths7DayAvg: 7 day moving average of the cumulative number of deaths DailyDeath7DayAvg: 7 day moving average of the daily number of deaths

IncreasePercent and moving 7 day averages are calculated fields added to the original source.

Acknowledgements

https://www.gov.uk/government/publications/covid-19-track-coronavirus-cases https://coronavirus.data.gov.uk/

On January 12, 2021, over 4.5 thousand individuals in the UK were admitted to hospital with coronavirus (COVID-19), the highest single amount since the start of the pandemic. The daily hospital cases started to rise significantly at the end of 2020 and into January 2021, however since then the number of hospitalizations fell dramatically as the UK managed to vaccinate millions against COVID-19. Overall, since the pandemic started around 994 thousand people in the UK have been hospitalized with the virus.

The total number of cases in the UK can be found here. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Indicators from a range of data sources to assess the impact of the coronavirus (COVID-19) pandemic on domestic abuse in England and Wales.

Context

Within the current response of a pandemic caused by the SARS-CoV-2 coronavirus, which in turn causes the disease, called COVID-19. It is necessary to join forces to minimize the effects of this disease.

Therefore, the intention of this dataset is to save data scientists time:

• Gather the data at the country level, encoding the country with its ISO code to allow easy access to other data
• Perform pre-processing of data, calculations of increments and other indicators that can facilitate modeling.
• Add the response of the governments over time so that it can be taken into account in the modeling.
• Daily update.

This dataset is not intended to be static, so suggestions for expanding it are welcome. If someone considers it important to add information, please let me know.

Content

The data contained in this dataset comes mainly from the following sources:

Source: Center for Systems Science and Engineering (CSSE) at Johns Hopkins University https://github.com/CSSEGISandData/COVID-19 Provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE): https://systems.jhu.edu/

Source: OXFORD COVID-19 GOVERNMENT RESPONSE TRACKER https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker Hale, Thomas and Samuel Webster (2020). Oxford COVID-19 Government Response Tracker. Data use policy: Creative Commons Attribution CC BY standard.

The original data is updated daily.

The features it includes are:

• Country Name

• Country Code ISO 3166 Alpha 3

• Date

• Incidence data:

  • confirmed
  • deaths
  • recoveries

• Daily increments:

  • confirmed_inc
  • deaths_inc
  • recoveries_inc

• Empirical Contagion Rate - ECR

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3508582%2F3e90ecbcdf76dfbbee54a21800f5e0d6%2FECR.jpg?generation=1586861653126435&alt=media" alt="">

• GOVERNMENT RESPONSE TRACKER - GRTStringencyIndex

  OXFORD COVID-19 GOVERNMENT RESPONSE TRACKER - Stringency Index

• Indices from Start Contagion

  • Days since the first case of contagion is overcome
  • Days since 100 cases are exceeded

• Percentages over the country's population:

  • confirmed_PopPct
  • deaths_PopPct
  • recoveries_PopPct

The method of obtaining the data and its transformations can be seen in the notebook:

Notebook COVID-19 Data by country with Government Response

Photo by Markus Spiske on Unsplash

Coronavirus affects some members of the population more than others. Emerging evidence suggests that older people, men, people with health conditions such as respiratory and pulmonary conditions, and people of a Black, Asian Minority Ethnic (BAME) background are at particular risk. There are also a number of other wider public health risk factors that have been found to increase the likelihood of an individual contracting coronavirus. This briefing presents descriptive evidence on a range of these factors, seeking to understand at a London-wide level the proportion of the population affected by each.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in the United Kingdom. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

TikTok saw a significant increase in popularity during the coronavirus (COVID-19) outbreak in the United Kingdom (UK), with a growth of ** percent among ***** year old users after the pandemic started.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

First reported in Wuhan, China, in December 2019, now more than 846,200 confirmed cases of COVID-19 are spread across 187 countries worldwide. The US and several countries in Europe such as Italy, Spain, and Belgium have continued to see a decrease in daily cases. Russia, Brazil, and Latin American countries are seeing increasing trends. India has also seen an increase in the number of new cases reported despite strict distancing measures taken early on.
Special populations analysis covered in the report include the following:
COVID-19 in children may result in systemic multisystem syndrome with severe outcomes.
Childhood routine vaccination rates drop during pandemic.
COVID-19’s impact in pregnant women unclear, though most cases are asymptomatic.
The COVID-19 pandemic could cause an increase in the prevalence of post-traumatic stress disorder (PTSD).
Complications of opioid addiction will be challenging for the management of disease during the COVID-19 pandemic. Read More

Vaccinations in London Between 8 December 2020 and 15 September 2021 5,838,305 1st doses and 5,232,885 2nd doses have been administered to London residents. Differences in vaccine roll out between London and the Rest of England London Rest of England Priority Group Vaccinations given Percentage vaccinated Vaccinations given Percentage vaccinated Group 1 Older Adult Care Home Residents 21,883 95% 275,964 96% Older Adult Care Home Staff 29,405 85% 381,637 88% Group 2 80+ years 251,021 83% 2,368,284 93% Health Care Worker 174,944 99% 1,139,243 100%* Group 3 75 - 79 years 177,665 90% 1,796,408 99% Group 4 70 - 74 years 252,609 90% 2,454,381 97% Clinically Extremely Vulnerable 278,967 88% 1,850,485 95% Group 5 65 - 69 years 285,768 90% 2,381,250 97% Group 6 At Risk or Carer (Under 65) 983,379 78% 6,093,082 88% Younger Adult Care Home Residents 3,822 92% 30,321 93% Group 7 60 - 64 years 373,327 92% 2,748,412 98% Group 8 55 - 59 years 465,276 91% 3,152,412 97% Group 9 50 - 54 years 510,132 90% 3,141,219 95% Data as at 15 September 2021 for age based groups and as at 12 September 2021 for non-age based groups * The number who have received their first dose exceeds the latest official estimate of the population for this group There is considerable uncertainty in the population denominators used to calculate the percentage vaccinated. Comparing implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following datasets can be used to estimate vaccine uptake by age group for London: ONS 2020 mid-year estimates (MYE). This is the population estimate used for age groups throughout the rest of the analysis. Number of people ages 18 and over on the National Immunisation Management Service (NIMS) ONS Public Health Data Asset (PHDA) dataset. This is a linked dataset combining the 2011 Census, the General Practice Extraction Service (GPES) data for pandemic planning and research and the Hospital Episode Statistics (HES). This data covers a subset of the population. Vaccine roll out in London by Ethnic Group Understanding how vaccine uptake varies across different ethnic groups in London is complicated by two issues: Ethnicity information for recipients is unavailable for a very large number of the vaccinations that have been delivered. As a result, estimates of vaccine uptake by ethnic group are highly sensitive to the assumptions about and treatment of the Unknown group in calculations of rates. For vaccinations given to people aged 50 and over in London nearly 10% do not have ethnicity information available, The accuracy of available population denominators by ethnic group is limited. Because ethnicity information is not captured in official estimates of births, deaths, and migration, the available population denominators typically rely on projecting forward patterns captured in the 2011 Census. Subsequent changes to these patterns, particularly with respect to international migration, leads to increasing uncertainty in the accuracy of denominators sources as we move further away from 2011. Comparing estimated population sizes and implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following population estimates are available by Ethnic group for London:

Cohort characteristics by COVID-19 test positive or not.