According to a survey completed in Great Britain in March 2020, ** percent of respondents rated their government as very good at communicating information about the coronavirus (COVID-19) outbreak, while a further ** percent rated the communication as fairly good. On the other hand, ** percent overall gave a poor rating to the government for the information it was providing about the coronavirus situation. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

In 2025, 100 percent of diagnostic imaging services in the NHS were considered 'good'. Outpatient service was another service that held good ratings, with half of available services rated 'outstanding', and the other half rated as 'good'. Services like urgent and emergency services, or maternity, on the other hand, had more than half rated as either 'inadequate' or with 'require improvement' ratings.

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.

Motivation

Why did I create this dataset? This is my first time creating a notebook in Kaggle and I am interested in learning more about COVID-19 and how different countries are affected by it and why. It might be useful to compare different metrics between different countries. And I also wanted to participate in a challenge, and I've decided to join the COVID-19 datasets challenge. While looking through the projects, I noticed https://www.kaggle.com/koryto/countryinfo and it inspired me to start this project.

Method

My approach is to scour the Internet and Kaggle looking for country data that can potentially have an impact on how the COVID-19 pandemic spreads. In the end, I ended up with the following for each country:

• Monthly temperature and precipitation from Worldbank
• Latitude and longitude
• Population, density, gender and age
• Airport traffic from Worldbank
• COVID-19 date of first case and number of cases and deaths as of March 26, 2020
• 2009 H1N1 flu pandemic cases and deaths obtained from Wikipedia
• Property affordability index and Health care index from Numbeo
• Number of hospital beds and ICU beds from Wikipedia
• Flu and pneumonia death rate from Worldlifeexpectancy.com (Age Adjusted Death Rate Estimates: 2017)
• School closures due to COVID-19
• Number of COVID-19 tests done
• Number of COVID-19 genetic strains
• US Social Distancing Policies from COVID19StatePolicy’s SocialDistancing repository on GitHub
• DHL Global Connectedness Index 2018 (People Breadth scores)
• Datasets have been merged by country name whenever possible. I needed to rename some countries by hand, e.g. US to United Sates, etc. but it's possible that I might have missed some. See the output file covid19_merged.csv for the merged result.

See covid19_data - data_sources.csv for data source details.

Notebook: https://www.kaggle.com/bitsnpieces/covid19-data

Caveats

Since I did not personally collect each datapoint, and because each datasource is different with different objectives, collected at different times, measured in different ways, any inferences from this dataset will need further investigation.

Other interesting sources of information

• IMF Policy Tracker
• nCov strain analysis across the globe!
• Google Mobility Report
• Oxford government response tracker
• Impact on aviation
• Impact on restaurants
• Harvard
• Tableau datahub
• Unemployment outlook from ILO

Acknowledgements

I want to acknowledge the authors of the datasets that made their data publicly available which has made this project possible. Banner image is by Brian.

I hope that the community finds this dataset useful. Feel free to recommend other datasets that you think will be useful / relevant! Thanks for looking.

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.

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.

Estimated effects on effective reproduction number.

Descriptive statistic of the explanatory variables.

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)

Context

COVID-19 The Government Response Stringency Index

The Government Response Stringency Index is a composite measure based on nine response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest response).

Content

OxCGRT collects publicly available information on indicators of government response. These indicators take policies such as school closures, travel bans, etc. and record them on an ordinal scale; the remainder are financial indicators such as fiscal or monetary measures.

OxCGRT measures the variation in governments’ responses using its 'COVID-19 Government Response Stringency Index (Stringency Index)'. This composite measure is a simple additive score of nine indicators measured on an ordinal scale, rescaled to vary from 0 to 100. Please note that this measure is for comparative purposes only, and should not necessarily be interpreted as a rating of the appropriateness or effectiveness of a country's response.

Acknowledgements

Data published by Thomas Hale, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik School of Government. https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker

Photo by Étienne Godiard on Unsplash

Inspiration

BCG - COVID-19 AI Challenge Improve BCG Data and Provide Insights to "BCG - COVID-19" Clinical Trials

This is a large (1318 participants) survey of UK General Practitioners during the Covid-19 pandemic via online survey, April/May 2021. This measured Maslach Burnout Inventory Scores and FACIT-Sp-NI scores (a burnout score, and a spiritual health score) as well as gathering data on gender as GMC, ethnicity, religion, number of sessions per week worked, length of service, area of service, and area of training.

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.

Frailty is a syndrome of increased vulnerability to incomplete resolution of homeostasis (healing or return to baseline function) following a stressor event (such as an infection or fall) and it is associated with poor outcomes including increased mortality and reduced quality of life. The pathophysiology of frailty is poorly understood. Age and frailty have been proven to be independently predictive of outcomes in patients admitted with an acute illness. In COVID-19, routine frailty identification informed decision making about treatment.

This dataset from 01-03-2020 to 01-04-2022 of 327,346 patients admitted during all waves of the COVID pandemic both with and without COVID-19. The dataset includes granular demographics, frailty scores, physiology and vital signs, all care contacts and investigations (including imaging), all medications including dose and routes, care outcomes, length of stay and outcomes including readmission and mortality.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. 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 & > 120 ITU bed capacity. 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”. 

Data set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. 

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. 

Available supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

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.

Difference of mobility changes in the countries with high or low stringency index.

This dataset contains all of the extracted data and recorded quality assessment scores for the studies included in the systematic review: Burch, E et al. “Early mathematical models of COVID-19 vaccination in high-income countries: a systematic review.” Public health vol. 236 (2024): 207-215. Doi:10.1016/j.puhe.2024.07.029.

Key demographics, ventilation parameters, treatment and disease severity scores, by UK pandemic wave.

File contains basic public metadata, including sequence_name, location, date, pangolin lineage assignment, version and associated scores, scorpio VOC/VUI constellation call and associated scores, key spike protein mutations calls and a list of all nucleotide mutations found.