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.

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:

Data from the Opinions and Lifestyle Survey (OPN) on whether and where someone has had to prove they have received a coronavirus (COVID-19) vaccine or had a negative test result, on worries about children returning to school including the reasons why and peoples perceptions of their risk of catching COVID-19, covering the period 8 to 19 September 2021.

Does the performance of the EU compared to neighbouring countries affect popular support for the EU? Following the benchmarking approach, we argue that people compare the performance of their country inside the EU with that of a country outside the EU and, as a result of this comparison, form their attitudes towards the EU. The COVID-19 vaccine rollout in 2020 represents an ideal scenario to test this benchmarking expectation. While the pandemic challenged countries across the globe simultaneously, the speed at which governments launched their vaccination programs differed. The UK rolled out its vaccines weeks before EU countries, and we study whether this affected popular support for the EU. We conduct an Unexpected Event during Surveys Design (UESD) based on a Eurobarometer survey in the field when the first vaccine was administered in the UK. Our results show that the start of the COVID-19 vaccination in the UK led to a significant decrease in specific policy support for the EU, while there is no consistent evidence of change in diffuse support for the EU. Our article has important implications for understanding attitudes toward European integration and performance evaluations.

As of January 18, 2023, Portugal had the highest COVID-19 vaccination rate in Europe having administered 272.78 doses per 100 people in the country, while Malta had administered 258.49 doses per 100. The UK was the first country in Europe to approve the Pfizer/BioNTech vaccine for widespread use and began inoculations on December 8, 2020, and so far have administered 224.04 doses per 100. At the latest data, Belgium had carried out 253.89 doses of vaccines per 100 population. Russia became the first country in the world to authorize a vaccine - named Sputnik V - for use in the fight against COVID-19 in August 2020. As of August 4, 2022, Russia had administered 127.3 doses per 100 people in the country.

The seven-day rate of cases across Europe shows an ongoing perspective of which countries are worst affected by the virus relative to their population. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

Supplementary Information Files for Covid-19 vaccine hesitancy in the UK: The Oxford Coronavirus explanations, attitudes, and narratives survey (OCEANS) IIBackground: Our aim was to estimate provisional willingness to receive a COVID-19 vaccine, identify predictive socio-demographic factors, and, principally, determine potential causes in order to guide information provision. Methods: A non-probability online survey was conducted (24th September-17th October 2020) with 5,114 UK adults, quota sampled to match the population for age, gender, ethnicity, income, and region. The Oxford COVID-19 Vaccine Hesitancy Scale assessed intent to take an approved vaccine. Structural equation modelling estimated explanatory factor relationships. Results: 71.7% (n=3,667) were willing to be vaccinated, 16.6% (n=849) were very unsure, and 11.7% (n=598) were strongly hesitant. An excellent model fit (RMSEA=0.05/CFI=0.97/TLI=0.97), explaining 86% of variance in hesitancy, was provided by beliefs about the collective importance, efficacy, side effects, and speed of development of a COVID-19 vaccine. A second model, with reasonable fit (RMSEA=0.03/CFI=0.93/TLI=0.92), explaining 32% of variance, highlighted two higher-order explanatory factors: ‘excessive mistrust’ (r=0.51), including conspiracy beliefs, negative views of doctors, and need for chaos, and ‘positive healthcare experiences’ (r=-0.48), including supportive doctor interactions and good NHS care. Hesitancy was associated with younger age, female gender, lower income, and ethnicity, but socio-demographic information explained little variance (9.8%). Hesitancy was associated with lower adherence to social distancing guidelines. Conclusions: COVID-19 vaccine hesitancy is relatively evenly spread across the population. Willingness to take a vaccine is closely bound to recognition of the collective importance. Vaccine public information that highlights prosocial benefits may be especially effective. Factors such as conspiracy beliefs that foster mistrust and erode social cohesion will lower vaccine up-take.

Abstract

Objective: Vaccination hesitancy and conspiracy beliefs are a threat to achieving population immunity in Covid-19. This study aimed to clarify the association between these and incentives to vaccination in the UK.

Design: In a longitudinal study, we collected UK public data at three time points: 1) before and 2) after the development of a vaccine, and 3) after the vaccination programme was underway.

Main Outcome Measures: Vaccination hesitancy; general and Covid-19 specific concerns about vaccination; belief in conspiracy theories.

Results: Vaccination hesitancy decreased between Times 1 (54%) and 3 (13%). Most concerns and reported incentives related to safety, though at Time 2, incentives included endorsement by trusted public figures. We found only small effects of conspiracy belief, and only at Time 1. A minority of participants remained anti-vaccination and stated nothing would change their minds.

Conclusion: Vaccination hesitancy seems to be falling the UK. However, anxiety about safety remains and could jeopardise the vaccination programme should any adverse effects be reported. Conspiracy beliefs seem to play only a minor role in hesitancy and may continue to decrease in importance with a successful vaccination programme. Understanding motivations behind vaccination hesitancy is vital if we are to achieve population immunity.

The UEFA European Championships 2020 were postponed in 2020 as a result of the coronavirus (COVID-19) pandemic and instead took place in June and July 2021. For the first time in the tournament's history, the games were hosted throughout several nations in Europe. Among these nations was the United Kingdom. A survey from April 2021 concluded that the majority of Britons believed that proof of COVID-19 vaccination should be required for anyone in order to attend any matches in the case of the entire tournament being held in the UK. Ultimately, London's Wembley Stadium hosted the semi-finals and finals of the tournament, which saw Italy crowned as champions for the second time in the nation's history.

Parameter estimates relate to the full model containing all predictors. The unstandardized regression coefficients represent the change in parental vaccination intention for a one-unit increase in the predictor variable (or, for dummy variables, a shift from the reference category to the category concerned). The model was based on 219 study participants with complete data.

The COVID-19 Health Inequalities Monitoring in England (CHIME) tool brings together data relating to the direct impacts of coronavirus (COVID-19) on factors such as mortality rates, hospital admissions, confirmed cases and vaccinations.

By presenting inequality breakdowns - including by age, sex, ethnic group, level of deprivation and region - the tool provides a single point of access to:

• show how inequalities have changed during the course of the pandemic and what the current cumulative picture is
• bring together data in one tool to enable users to access and use the intelligence more easily
• provide indicators with a consistent methodology across different data sets to facilitate understanding
• support users to identify and address inequalities within their areas, and identify priority areas for recovery

In the March 2023 update, data has been updated for deaths, hospital admissions and vaccinations. Data on inequalities in vaccination uptake within upper tier local authorities has been added to the tool for the first time. This replaces data for lower tier local authorities, published in December 2022, allowing the reporting of a wider range of inequality breakdowns within these areas.

Please send any questions or comments to PHA-OHID@dhsc.gov.uk

BackgroundCOVID-19 is characterized by strikingly large, mostly unexplained, interindividual variation in symptom severity: while some individuals remain nearly asymptomatic, others suffer from severe respiratory failure. Previous vaccinations for other pathogens, in particular tetanus, may partly explain this variation, possibly by readying the immune system.MethodsWe made use of data on COVID-19 testing from 103,049 participants of the UK Biobank (mean age 71.5 years, 54.2% female), coupled to immunization records of the last ten years. Using logistic regression, covarying for age, sex, respiratory disease diagnosis, and socioeconomic status, we tested whether individuals vaccinated for tetanus, diphtheria or pertussis, differed from individuals that had only received other vaccinations on 1) undergoing a COVID-19 test, 2) being diagnosed with COVID-19, and 3) whether they developed severe COVID-19 symptoms.ResultsWe found that individuals with registered diphtheria or tetanus vaccinations are less likely to develop severe COVID-19 than people who had only received other vaccinations (diphtheria odds ratio (OR)=0.47, p-value=5.3*10-5; tetanus OR=0.52, p-value=1.2*10-4).DiscussionThese results indicate that a history of diphtheria or tetanus vaccinations is associated with less severe manifestations of COVID-19. These vaccinations may protect against severe COVID-19 symptoms by stimulating the immune system. We note the correlational nature of these results, yet the possibility that these vaccinations may influence the severity of COVID-19 warrants follow-up investigations.

A global survey on attitudes towards coronavirus COVID-19 vaccinations found out that people from Hong Kong tended to think more positively about a COVID-19 vaccine if it was developed in Singapore, Germany, or the United Kingdom. As per the survey results generated in *************, about ** percent of Hong Kong respondents had a positive impression of the coronavirus vaccines from Singapore, compared to only ***** percent when it comes to Indian vaccines.

Estimates of the risk of all-cause and cardiac death in the 12 weeks after vaccination or positive SARS-CoV-2 test compared with subsequent weeks for people aged 12 to 29 years in England using two sources of mortality data: ONS death registrations and deaths recorded in Hospital Episode Statistics. 8 December 2020 to 25 May 2022. Experimental Statistics.

This dataset reports the uptake of at least one dose of the COVID-19 vaccine among patients registered with GP practices in England. It provides a measure of immunisation coverage and supports monitoring of public health efforts to reduce the spread and severity of COVID-19. The data is sourced from Immform, EMIS Health, and TPP systems.

Rationale

Vaccination is a critical tool in controlling the COVID-19 pandemic. Monitoring vaccine uptake helps identify gaps in coverage, inform targeted outreach, and evaluate the effectiveness of immunisation campaigns. This indicator supports efforts to increase vaccine uptake and protect vulnerable populations.

Numerator

The numerator is the number of patients who have received at least one dose of a COVID-19 vaccine.

Denominator

The denominator is the total number of patients registered with GP practices, as recorded in the Immform-EMIS Health and TPP systems.

Caveats

Automated data collection is only possible from GP practices whose IT suppliers support automatic extraction. Some organisations may not have responded or submitted data, which could affect completeness and accuracy.

External References

More information is available from the following source:

Immform COVID-19 Collections Portal

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

This statistical report, co-authored with the UK Health Security Agency (UKSHA), reports childhood vaccination coverage statistics for England in 2022-23. Data relates to the routine vaccinations offered to all children up to the age of 5 years, derived from the Cover of Vaccination Evaluated Rapidly (COVER). Additional information on children aged 2 and 3 vaccinated against seasonal flu are collected from GPs through UKHSA's ImmForm system.

Content

This dataset is a result of survey data generated from respondents to an Ipsos survey asking the question:"If a vaccine for COVID-19 were available, I would get it," on its Global Advisor online platform between 2020-10-08 to 2020-10-13 compared to data gathered between 2020-12-17 to 2020-12-20. October 2020 data is gathered from approximately 16,500 respondents and the December 2020 data is gathered from 13,542 respondents, both from adults aged 16-74 from 15 countries.

"The data is weighted so that each country’s sample composition best reflects the demographic profile of the adult population according to the most recent census data."

"Where results do not sum to 100 or the ‘difference’ appears to be +/-1 more/less than the actual, this may be due to rounding, multiple responses, or the exclusion of “don't know” or not stated responses."

"The precision of Ipsos online polls is calculated using a credibility interval with a poll of N=1,000 accurate to +/-3.5 percentage points and of N=500 accurate to +/- 5.0 percentage points. For more information on Ipsos’ use of credibility intervals, please visit the Ipsos website."

"The publication of these findings abides by local rules and regulations."

Methodology GLOBAL ATTITUDES ON A COVID-19 VACCINE

Article U.S. and U.K. are optimistic indicators for COVID-19 vaccination uptake

CVD-COVID-UK, co-ordinated by the British Heart Foundation (BHF) Data Science Centre (https://bhfdatasciencecentre.org/), is one of the NIHR-BHF Cardiovascular Partnership’s National Flagship Projects.

CVD-COVID-UK aims to understand the relationship between COVID-19 and cardiovascular diseases through analyses of de-identified, pseudonymised, linked, nationally collated health datasets across the four nations of the UK. The consortium has over 400 members across more than 50 institutions including data custodians, data scientists and clinicians, all of whom have signed up to an agreed set of principles with an inclusive, open and transparent ethos.

Approved researchers access data within secure trusted/secure research environments (TREs/SDEs) provided by NHS England (England), the National Safe Haven (Scotland), the Secure Anonymised Information Linkage (SAIL) Databank (Wales) and the Honest Broker Service (Northern Ireland). A dashboard of datasets available in each nation’s TRE can be found here: https://bhfdatasciencecentre.org/areas/cvd-covid-uk-covid-impact/

This dataset represents the linked datasets in SAIL Databank’s TRE for Wales and contains the following datasets: • Welsh Longitudinal GP Dataset - Welsh Primary Care (Daily COVID codes only) (GPCD) • Welsh Longitudinal General Practice Dataset (WLGP) - Welsh Primary Care • Critical Care Dataset (CCDS) • Emergency Department Dataset Daily (EDDD) • Emergency Department Dataset (EDDS) • Outpatient Database for Wales (OPDW) • Outpatient Referral (OPRD) • Patient Episode Dataset for Wales (PEDW) • COVID-19 Test Results (PATD) • COVID-19 Test Trace and Protect (CTTP) - Legacy • COVID-19 Shielded People List (CVSP) • SARS-CoV-2 viral sequencing data (COG-UK data)-Lineage/Variant Data-Wales (CVSD) • Covid Vaccination Dataset (CVVD) • Annual District Death Daily (ADDD) • Annual District Death Extract (ADDE) • COVID-19 Consolidated Deaths (CDDS) • Intensive Care National Audit and Research Centre (ICCD) - Legacy - COVID only • Intensive Care National Audit and Research Centre (ICNC) • Welsh Dispensing Dataset (WDDS) - Legacy • Annual District Birth Extract (ADBE) • Maternity Indicators Dataset (MIDS) • National Community Child Health Database (NCCHD) • Care Home Dataset (CARE) • Congenital Anomaly Register and Information Service (CARS) • Referral to Treatment Times (RTTD) • SAIL Dementia e-Cohort (SDEC) • Welsh Ambulance Services NHS Trust (WASD) • Welsh Demographic Service Dataset (WDSD) • Welsh Results Reports Service (WRRS) • ONS 2011 Census Wales (CENW)

Importance: COVID-19 vaccine development has progressed at unprecedented speed. Widespread public uptake of the vaccine is crucial to stem the pandemic. Objective: To examine the factors associated with survey participants’ self-reported likelihood of selecting and receiving a hypothetical COVID-19 vaccine. Design, Setting and Participants: A survey of a nonprobability convenience sample of 2000 recruited participants including a choice-based conjoint analysis was conducted to estimate respondents’ probability of choosing a vaccine and willingness to receive vaccination . Participants were then asked to evaluate their willingness to receive each vaccine individually. The survey presented respondents with 5 choice tasks. In each, participants evaluated 2 hypothetical COVID-19 vaccines and were asked whether they would choose vaccine A, vaccine B, or neither vaccine . Vaccine attributes included efficacy, protection duration, major side effects, minor side effects, US Food and Drug Administration (FDA) approval process, national origin of vaccine, and endorsement. Levels of each attribute for each vaccine were randomly assigned and attribute order was randomized across participants. Survey data wereas collected on July 9, 2020. Main Outcomes and Measures: Average marginal component effect sizes and marginal means were calculated to estimate the relationship between each vaccine attribute-level and the probability of the respondent choosing a vaccine and self-reported willingness to receive vaccination . Results: A total of 1,971 US adults responded to the survey (median age 43; IQR: 30 to 58); 999 (51%) were women, 1,432 (73%) White, 277 (14%) Black, and 190 (10%) Latinx. An increase in efficacy from 50% to 70% was associated with a higher n increased the estimated probability of choosing a vaccine ofby .07 [95% CI: .06 to .09]; and an increase from 50% to 90% was associated with a higher probability of choosing a vaccine of .16 [95% CI: .15 to .18]. An increase in protection duration from 1 to 5 years was associated with a higher probability of choosing a vaccine of .05 [95% CI: .04 to .07]. A decrease in the incidence of major side effects from 1 in 10,000 to 1 in 1,000,000 was associated with a higher probability of choosing a vaccine of .07 [95% CI: .05 to .08]. An FDA emergency use authorization was associated with a lower probability of choosing a vaccine of -.03 [95% CI: -.01 to -.04] compared with full FDA approval. A vaccine that originated from a non-US country was associated with a lower probability of choosing a vaccine [China: -.13 (95% CI: -.11 to -.15 UK: -.04 (95% CI: -.02 to -.06)]. Endorsements from the US Centers for Disease Control and Prevention [.09 (95% CI: .07 to .11)] and World Health Organization [.06 (95% CI: .04 to .08)], compared with an endorsement from President Trump, were associated with higher probabilities of choosing a vaccine. Analyses of participants’ willingness to receive each vaccine when assessed individually yield similar results. Efficacy was the most important factor. An increase in efficacy from 50% to 90% was associated with a 10% higher marginal mean willingness to receive a vaccine [.51 to .61]. A reduction in the incidence of major side effects was associated with a 4% higher marginal mean willingness to receive a vaccine [.54 to .58]. A vaccine originating in China was associated with a 10% lower willingness to receive a vaccine versus one developed in the US [.60 to .50] Endorsements from the CDC and WHO were associated with substantial increases in willingness to receive a vaccine, 7% and 6%, respectively , from a baseline endorsement by President Trump [.52 to .59; .52 to .58]. Conclusions and Relevance: In this survey study of US adults, vaccine-related attributes and political characteristics were associated with self-reported preferences for choosing a hypothetical COVID-19 vaccine and self-reported willingness to receive vaccination. These results may help inform public health campaigns to address vaccine hesitancy when a COVID-19 vaccine becomes available.

BackgroundCOVID-19 is characterized by strikingly large, mostly unexplained, interindividual variation in symptom severity: while some individuals remain nearly asymptomatic, others suffer from severe respiratory failure. Previous vaccinations for other pathogens, in particular tetanus, may partly explain this variation, possibly by readying the immune system.MethodsWe made use of data on COVID-19 testing from 103,049 participants of the UK Biobank (mean age 71.5 years, 54.2% female), coupled to immunization records of the last ten years. Using logistic regression, covarying for age, sex, respiratory disease diagnosis, and socioeconomic status, we tested whether individuals vaccinated for tetanus, diphtheria or pertussis, differed from individuals that had only received other vaccinations on 1) undergoing a COVID-19 test, 2) being diagnosed with COVID-19, and 3) whether they developed severe COVID-19 symptoms.ResultsWe found that individuals with registered diphtheria or tetanus vaccinations are less likely to develop severe COVID-19 than people who had only received other vaccinations (diphtheria odds ratio (OR)=0.47, p-value=5.3*10-5; tetanus OR=0.52, p-value=1.2*10-4).DiscussionThese results indicate that a history of diphtheria or tetanus vaccinations is associated with less severe manifestations of COVID-19. These vaccinations may protect against severe COVID-19 symptoms by stimulating the immune system. We note the correlational nature of these results, yet the possibility that these vaccinations may influence the severity of COVID-19 warrants follow-up investigations.

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.