In late-November 2021, the Omicron variant of SARS-CoV-2 (the virus which causes COVID-19) was designated as a variant of concern by the World Health Organization due to fears about a higher transmissibility from the variant and a possible decrease in the effectiveness of vaccines against it. The Omicron variant has been detected in multiple countries since the discovery, and as of April 1, 2022, almost 965 thousand cases have been sequenced in the United Kingdom.

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.

As of January 13, 2023, there have been 270,744,353 confirmed cases of coronavirus (COVID-19) across the whole of Europe since the first confirmed cases in January 2020. There were approximately 12.1 million new cases reported in the week beginning January 24, 2022, the highest number of daily cases in a single week. There was a significant increase in the number of new cases in Europe in winter 2021/22 as the Omicron variant emerged. France has had the highest amount of confirmed cases in Europe with 38,337,350, followed by Germany with 37,594,526 cases. A full breakdown of the confirmed cases in Europe can be found here.

For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

Between July and November 2021, the Delta variant has accounted for for at least 99 percent of the COVID-19 variants analyzed in the United Kingdom. However, in the two-week period up to January 5, 2022, the Omicron variant accounted for around 96 percent of SARS-COV-2 variants detected in the UK. The Omicron variant had been designated by the World Health Organization as a variant of concern in November 2021 and is regarded as more infectious than previous variants.

The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. Reliable estimates of the infection fatality ratio (IFR) and infection hospitalisation ratio (IHR) along with the time-delay between infection and hospitalisation/death can inform forecasts of the numbers/timing of severe outcomes and allow healthcare services to better prepare for periods of increased demand. The REal-time Assessment of Community Transmission-1 (REACT-1) study estimated swab positivity for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in England approximately monthly from May 2020 to March 2022. Here, we analyse the changing relationship between prevalence of swab positivity and the IFR and IHR over this period in England, using publicly available data for the daily number of deaths and hospitalisations, REACT-1 swab positivity data, time-delay models, and Bayesian P-spline models. We analyse data for all age groups together, as well as in 2 subgroups: those aged 65 and over and those aged 64 and under. Additionally, we analysed the relationship between swab positivity and daily case numbers to estimate the case ascertainment rate of England’s mass testing programme. During 2020, we estimated the IFR to be 0.67% and the IHR to be 2.6%. By late 2021/early 2022, the IFR and IHR had both decreased to 0.097% and 0.76%, respectively. The average case ascertainment rate over the entire duration of the study was estimated to be 36.1%, but there was some significant variation in continuous estimates of the case ascertainment rate. Continuous estimates of the IFR and IHR of the virus were observed to increase during the periods of Alpha and Delta’s emergence. During periods of vaccination rollout, and the emergence of the Omicron variant, the IFR and IHR decreased. During 2020, we estimated a time-lag of 19 days between hospitalisation and swab positivity, and 26 days between deaths and swab positivity. By late 2021/early 2022, these time-lags had decreased to 7 days for hospitalisations and 18 days for deaths. Even though many populations have high levels of immunity to SARS-CoV-2 from vaccination and natural infection, waning of immunity and variant emergence will continue to be an upwards pressure on the IHR and IFR. As investments in community surveillance of SARS-CoV-2 infection are scaled back, alternative methods are required to accurately track the ever-changing relationship between infection, hospitalisation, and death and hence provide vital information for healthcare provision and utilisation.

BackgroundHybrid immunity (a combination of natural and vaccine-induced immunity) provides additional immune protection against the coronavirus disease 2019 (COVID-19) reinfection. Today, people are commonly infected and vaccinated; hence, hybrid immunity is the norm. However, the mitigation of the risk of Omicron variant reinfection by hybrid immunity and the durability of its protection remain uncertain. This meta-analysis aims to explore hybrid immunity to mitigate the risk of Omicron variant reinfection and its protective durability to provide a new evidence-based basis for the development and optimization of immunization strategies and improve the public’s awareness and participation in COVID-19 vaccination, especially in vulnerable and at-risk populations.MethodsEmbase, PubMed, Web of Science, Chinese National Knowledge Infrastructure, and Wanfang databases were searched for publicly available literature up to 10 June 2024. Two researchers independently completed the data extraction and risk of bias assessment and cross-checked each other. The Newcastle-Ottawa Scale assessed the risk of bias in included cohort and case–control studies, while criteria recommended by the Agency for Health Care Research and Quality (AHRQ) evaluated cross-sectional studies. The extracted data were synthesized in an Excel spreadsheet according to the predefined items to be collected. The outcome was Omicron variant reinfection, reported as an Odds Ratio (OR) with its 95% confidence interval (CI) and Protective Effectiveness (PE) with 95% CI. The data were pooled using a random- or fixed-effects model based on the I2 test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed.ResultsThirty-three articles were included. Compared with the natural immunity group, the hybrid immunity (booster vaccination) group had the highest level of mitigation in the risk of reinfection (OR = 0.43, 95% CI:0.34–0.56), followed by the complete vaccination group (OR = 0.58, 95% CI:0.45–0.74), and lastly the incomplete vaccination group (OR = 0.64, 95% CI:0.44–0.93). Compared with the complete vaccination-only group, the hybrid immunity (complete vaccination) group mitigated the risk of reinfection by 65% (OR = 0.35, 95% CI:0.27–0.46), and the hybrid immunity (booster vaccination) group mitigated the risk of reinfection by an additional 29% (OR = 0.71, 95% CI:0.61–0.84) compared with the hybrid immunity (complete vaccination) group. The effectiveness of hybrid immunity (incomplete vaccination) in mitigating the risk of reinfection was 37.88% (95% CI, 28.88–46.89%) within 270–364 days, and decreased to 33.23%% (95% CI, 23.80–42.66%) within 365–639 days; whereas, the effectiveness after complete vaccination was 54.36% (95% CI, 50.82–57.90%) within 270–364 days, and the effectiveness of booster vaccination was 73.49% (95% CI, 68.95–78.04%) within 90–119 days.ConclusionHybrid immunity was significantly more protective than natural or vaccination-induced immunity, and booster doses were associated with enhanced protection against Omicron. Although its protective effects waned over time, vaccination remains a crucial measure for controlling COVID-19.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier, CRD42024539682.

Higher Education Graduate Outcomes Statistics: UK, 2020/21 This Statistical Bulletin is the annual first release of Graduate Outcomes survey data. These experimental statistics cover UK higher education providers (HEPs) including alternative providers (APs) and further education colleges (FECs) in England, Wales and Northern Ireland. Data is collected approximately 15 months after HE course completion. The 2020/21 Graduate Outcomes cohort finished their qualifications in the second academic year affected by COVID-19. While Cohort A finished their qualifications during late summer and early autumn 2020, in a period of relatively loose restrictions, restrictions began to increase over the course of the academic year. Cohort B graduated into a period of short national lockdowns, followed by the start of the second national lockdown in January 2021. Cohort C likewise graduated in lockdown, but the progress of the vaccination programme led to a gradual easing of restrictions as spring progressed; by the time Cohort D, the largest Graduate Outcomes cohort, began to finish their qualifications in May 2021, most adults had been offered a first vaccine dose, and restrictions were gradually being phased out across the UK. The circumstances under which 2020/21 graduates were surveyed were quite different. As surveying for Cohort A opened in December 2021, Omicron variant cases were rising and new guidance was being issued requiring masks in indoor spaces and encouraging people to work from home where possible, the new restrictions were considerably more lenient than those which were introduced a year previously. By the time the Cohort B survey period opened in March 2022, all legal restrictions had been lifted in England, and remaining restrictions were phased out in other nations over the next few months. Although COVID cases rose from the start of June to a summer peak in early July, no legal restrictions were in place during the survey periods for Cohorts C and D. An insight briefing provides further detail on analysis undertaken to explore the impact of the pandemic, and the conclusions identified. This statistical bulletin has been produced by HESA in collaboration with statisticians from the Office for Students, the Department for Education, the Welsh Government, the Scottish Government and the Department for the Economy Northern Ireland. It has been released according to the arrangements approved by the UK Statistics Authority.

COVID-19 infection rates remain high in South Africa. Clinical prediction models may be helpful for rapid triage, and supporting clinical decision making, for patients with suspected COVID-19 infection. The Western Cape, South Africa, has integrated electronic health care data facilitating large-scale linked routine datasets. The aim of this study was to develop a machine learning model to predict adverse outcome in patients presenting with suspected COVID-19 suitable for use in a middle-income setting. A retrospective cohort study was conducted using linked, routine data, from patients presenting with suspected COVID-19 infection to public-sector emergency departments (EDs) in the Western Cape, South Africa between 27th August 2020 and 31st October 2021. The primary outcome was death or critical care admission at 30 days. An XGBoost machine learning model was trained and internally tested using split-sample validation. External validation was performed in 3 test cohorts: Western Cape patients presenting during the Omicron COVID-19 wave, a UK cohort during the ancestral COVID-19 wave, and a Sudanese cohort during ancestral and Eta waves. A total of 282,051 cases were included in a complete case training dataset. The prevalence of 30-day adverse outcome was 4.0%. The most important features for predicting adverse outcome were the requirement for supplemental oxygen, peripheral oxygen saturations, level of consciousness and age. Internal validation using split-sample test data revealed excellent discrimination (C-statistic 0.91, 95% CI 0.90 to 0.91) and calibration (CITL of 1.05). The model achieved C-statistics of 0.84 (95% CI 0.84 to 0.85), 0.72 (95% CI 0.71 to 0.73), and 0.62, (95% CI 0.59 to 0.65) in the Omicron, UK, and Sudanese test cohorts. Results were materially unchanged in sensitivity analyses examining missing data. An XGBoost machine learning model achieved good discrimination and calibration in prediction of adverse outcome in patients presenting with suspected COVID19 to Western Cape EDs. Performance was reduced in temporal and geographical external validation.