As of August 11, 2022, over 23.4 million people in the United Kingdom had tested positive for COVID-19 with 3,948 cases reported on that day. During the large wave of cases in the winter 2020/21, the number of daily hospitalizations also peaked with both graphs taking similar shapes. Although hospitalizations did increase, rising case numbers at the end of 2021 did not fully corresponded into a similarly large surge as the previous winter, as experts pointed to the effectiveness of being vaccinated against COVID-19.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

Estimates of the risk of hospital admission for coronavirus (COVID-19) and death involving COVID-19 by vaccination status, overall and by age group, using anonymised linked data from Census 2021. Experimental Statistics.

Outcome definitions

For this analysis, we define a death as involving COVID-19 if either of the ICD-10 codes U07.1 (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified) is mentioned on the death certificate. Information on cause of death coding is available in the User Guide to Mortality Statistics. We use date of occurrance rather than date of registration to give the date of the death.

We define COVID-109 hospitalisation as an inpatient episode in Hospital Episode Statistics where the primary diagnosis was COVID-19, identified by the ICD-19 codes (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified). Where an individual had experienced more than one COVID-19 hospitalisation, the earliest that occurred within the study period was used. We define the date of COVID-19 hospitalisation as the start of the hospital episode.

ICD-10 code

U07.1 :

COVID-19, virus identified

U07.2:

COVID-19, virus not identified

Vaccination status is defined by the dose and the time since the last dose received

Unvaccinated:

no vaccination to less than 21 days post first dose

First dose 21 days to 3 months:

more than or equal to 21 days post second dose to earliest of less than 91 days post first dose or less than 21 days post second dose

First dose 3+ months:

more than or equal to 91 days post first dose to less than 21 days post second dose

Second dose 21 days to 3 months:

more than or equal to 21 days post second dose to earliest of less than 91 days post second dose or less than 21 days post third dose

Second dose 3-6 months:

more than or equal to 91 days post second dose to earliest of less than 182 days post second dose or less than 21 days post third dose

Second dose 6+ months:

more than or equal to 182 days post second dose to less than 21 days post third dose

Third dose 21 days to 3 months:

more than or equal to 21 days post third dose to less than 91 days post third dose

Third dose 3+ months:

more than or equal to 91 days post third dose

Model adjustments

Three sets of model adjustments were used

Age adjusted:

age (as a natural spline)

Age, socio-demographics adjusted:

age (as a natural spline), plus socio-demographic characteristics (sex, region, ethnicity, religion, IMD decile, NSSEC category, highest qualification, English language proficiency, key worker status)

Fully adjusted:

age (as a natural spline), plus socio-demographic characteristics (sex, region, ethnicity, religion, IMD decile, NSSEC category, highest qualification, English language proficiency, key worker status), plus health-related characteristics (disability, self-reported health, care home residency, number of QCovid comorbidities (grouped), BMI category, frailty flag and hospitalisation within the last 21 days.

Age

Age in years is defined on the Census day 2021 (21 March 2021). Age is included in the model as a natural spline with boundary knots at the 10th and 90th centiles and internal knots at the 25th, 50th and 75th centiles. The positions of the knots are calculated separately for the overall model and for each age group for the stratified model.

Context

COVID-19 is a Pandemic which was spread worldwide in the early months of 2020, Which has had a major impact on the United Kingdom. As the UK has recently carried out wide spread vaccination and ended Lockdown I am providing the recent COVID-19 figures.

Content

Several Datasets are provided, focusing on Deaths, Cases, Hospitalisation and Vaccination. Files often protray the same information but from a different reference point. For example for Deaths there is one displaying figures from people who died using there positive date as a reference point, whereas the other is using the date of death.

Acknowledgements

These datasets was scrapped off the UK Gov website in regards to COVID-19. For those looking to build a more complex project using a constant data flow, they do provide an API which may assist.

Inspiration

Possible area to explore are: What was the Impact of Vaccines on the COVID-19 Pandemic? What was the Impact of a Lockdown on the COVID-19 Pandemic? Which Nation managed the spread of COVID-19 the best?

Licence

Open Government Licence V3.0

All data relating to “Coronavirus (COVID-19) hospital admissions by vaccination and pregnancy status, England”.

Context

Complete COVID-19 dataset is a collection of the COVID-19 data maintained by Our World in Data. It is updated daily and includes data on confirmed cases, deaths, hospitalizations, testing, and vaccinations as well as other variables of potential interest.

Content

The variables represent all data related to confirmed cases, deaths, hospitalizations, and testing, as well as other variables of potential interest.
the columns are: iso_code, continent, location, date, total_cases, new_cases, new_cases_smoothed, total_deaths, new_deaths, new_deaths_smoothed, total_cases_per_million, new_cases_per_million, new_cases_smoothed_per_million, total_deaths_per_million, new_deaths_per_million, new_deaths_smoothed_per_million, reproduction_rate, icu_patients, icu_patients_per_million, hosp_patients, hosp_patients_per_million, weekly_icu_admissions, weekly_icu_admissions_per_million, weekly_hosp_admissions, weekly_hosp_admissions_per_million, total_tests, new_tests, total_tests_per_thousand, new_tests_per_thousand, new_tests_smoothed, new_tests_smoothed_per_thousand, positive_rate, tests_per_case, tests_units, total_vaccinations, people_vaccinated, people_fully_vaccinated, new_vaccinations, new_vaccinations_smoothed, total_vaccinations_per_hundred, people_vaccinated_per_hundred, people_fully_vaccinated_per_hundred, new_vaccinations_smoothed_per_million, stringency_index, population, population_density, median_age, aged_65_older, aged_70_older, gdp_per_capita, extreme_poverty, cardiovasc_death_rate, diabetes_prevalence, female_smokers, male_smokers, handwashing_facilities, hospital_beds_per_thousand, life_expectancy, human_development_index

Acknowledgements/ Data Source

https://systems.jhu.edu/research/public-health/ncov/ https://www.ecdc.europa.eu/en/publications-data/download-data-hospital-and-icu-admission-rates-and-current-occupancy-covid-19 https://coronavirus.data.gov.uk/details/healthcare https://covid19tracker.ca/ https://healthdata.gov/dataset/covid-19-reported-patient-impact-and-hospital-capacity-state-timeseries https://ourworldindata.org/coronavirus-testing#our-checklist-for-covid-19-testing-data

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.

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.

BackgroundVaccination, one of the most important and effective ways of preventing infectious diseases, has recently been used to control the COVID-19 pandemic. The present meta-analysis study aimed to evaluate the effectiveness of COVID-19 vaccines in reducing the incidence, hospitalization, and mortality from COVID-19.MethodsA systematic search was performed independently in Scopus, PubMed via Medline, ProQuest, and Google Scholar electronic databases as well as preprint servers using the keywords under study. We used random-effect models and the heterogeneity of the studies was assessed using I2 and χ2 statistics. In addition, the Pooled Vaccine Effectiveness (PVE) obtained from the studies was calculated by converting based on the type of outcome.ResultsA total of 54 studies were included in this meta-analysis. The PVE against SARS-COV 2 infection were 71% [odds ratio (OR) = 0.29, 95% confidence intervals (CI): 0.23–0.36] in the first dose and 87% (OR = 0.13, 95% CI: 0.08–0.21) in the second dose. The PVE for preventing hospitalization due to COVID-19 infection was 73% (OR = 0.27, 95% CI: 0.18–0.41) in the first dose and 89% (OR = 0.11, 95% CI: 0.07–0.17) in the second dose. With regard to the type of vaccine, mRNA-1273 and combined studies in the first dose and ChAdOx1 and mRNA-1273 in the second dose had the highest effectiveness in preventing infection. Regarding the COVID-19-related mortality, PVE was 68% (HR = 0.32, 95% CI: 0.23–0.45) in the first dose and 92% (HR = 0.08, 95% CI: 0.02–0.29) in the second dose.ConclusionThe results of this meta-analysis indicated that vaccination against COVID-19 with BNT162b2 mRNA, mRNA-1273, and ChAdOx1, and also their combination, was associated with a favorable effectiveness against SARS-CoV2 incidence rate, hospitalization, and mortality rate in the first and second doses in different populations. We suggest that to prevent the severe form of the disease in the future, and, in particular, in the coming epidemic picks, vaccination could be the best strategy to prevent the severe form of the disease.Systematic review registrationPROSPERO International Prospective Register of Systematic Reviews: http://www.crd.york.ac.uk/PROSPERO/, identifier [CRD42021289937].

Performance of the QCOVID3 algorithm to predict risk of COVID-19 related death and hospitalisation for the total cohort and by age, sex, and vaccination dose (95% CI).

BackgroundPredicting the future UK COVID-19 epidemic provides a baseline of a vaccine-only mitigation policy from which to judge the effects of additional public health interventions. A previous 12-month prediction of the size of the epidemic to October 2022 underestimated its sequelae by a fifth. This analysis seeks to explain the reasons for the underestimation before offering new long-term predictions.MethodsA Dynamic Causal Model was used to identify changes in COVID-19 transmissibility and the public's behavioral response in the 12-months to October 2022. The model was then used to predict the future trends in infections, long-COVID, hospital admissions and deaths over 12-months to October 2023.FindingsThe model estimated that the secondary attack rate increased from 0.4 to 0.5, the latent period shortened from 2.7 to 2.6 and the incubation period shortened from 2.0 to 1.95 days between October 2021 and October 2022. During this time the model also estimated that antibody immunity waned from 177 to 160 days and T-cell immunity from 205 to 180 days. This increase in transmissibility was associated with a reduction in pathogenicity with the proportion of infections developing acute respiratory distress syndrome falling for 6–2% in the same twelve-month period. Despite the wave of infections, the public response was to increase the tendency to expose themselves to a high-risk environment (e.g., leaving home) each day from 33–58% in the same period.The predictions for October 2023 indicate a wave of infections three times larger this coming year than last year with significant health and economic consequences such as 120,000 additional COVID-19 related deaths, 800,000 additional hospital admissions and 3.5 million people suffering acute-post-COVID-19 syndrome lasting more than 12 weeks.InterpretationThe increase in transmissibility together with the public's response provide plausible explanations for why the model underestimated the 12-month predictions to October 2022. The 2023 projection could well-underestimate the predicted substantial next wave of COVID-19 infection. Vaccination alone will not control the epidemic. The UK COVID-19 epidemic is not over. The results call for investment in precautionary public health interventions.