Provisional counts of the number of total deaths and deaths not involving the coronavirus (COVID-19), between 28 December 2019 and 10 July 2020. This includes deaths disaggregated by age and sex; by region of England, and Wales, and place of death; and for underlying causes of death and deaths involving leading causes.

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.

The coronavirus (COVID-19) outbreak has affected CPC (cost-per-click) in many UK industries, although some have seen a more noticeable change than others. The difference between the average cost per click in the insurance industry in December 2019 compared to the CPC in March 2020 is clearly visible.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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

Introduction: This retrospective cohort study compares in-centre haemodialysis (ICHD) patients’ outcomes between the 1st and 2nd waves of the COVID-19 pandemic in England, Wales, and Northern Ireland. Methods: All people aged ≥18 years receiving ICHD at 31 December 2019, who were still alive and not in receipt of a kidney transplant at 1 March and who had a positive polymerase chain reaction test for SARS-CoV-2 between 1 March 2020 and 31 January 2021, were included. The COVID-19 infections were split into two “waves”: wave 1 from March to August 2020 and wave 2 from September 2020 to January 2021. Cumulative incidence of COVID-19, multivariable Cox models for risk of positivity, median, and 95% credible interval of reproduction number in dialysis units were calculated separately for wave 1 and wave 2. Survival and hazard ratios for mortality were described with age- and sex-adjusted Kaplan-Meier plots and multivariable Cox proportional models. Results: 4,408 ICHD patients had COVID-19 during the study period. Unadjusted survival at 28 days was similar in both waves (wave 1 75.6% [95% confidence interval [CI]: 73.7–77.5], wave 2 76.3% [95% CI 74.3–78.2]), but death occurred more rapidly after detected infection in wave 1. Long vintage treatment and not being on the transplant waiting list were associated with higher mortality in both waves. Conclusions: Risk of death of patients on ICHD treatment with COVID-19 remained unchanged between the first and second outbreaks. This highlights that this vulnerable patient group needs to be prioritized for interventions to prevent severe COVID-19, including vaccination, and the implementation of measures to reduce the risk of transmission alone is not sufficient.

This report provides an update on the assessment of how the pharmaceutical industry perceives the supply chain disruption caused by the COVID-19 pandemic, including the challenges associated with clinical trials, logistics, API and finished dose manufacturing.
Since the first case was diagnosed in Wuhan, China, in December 2019, COVID-19 cases have continued to rise rapidly across the globe. Read More

Overview:

Population estimates from the publication: Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic.

Population estimates were aggregated to Local Authority Districts (LADs).

Methodology:

Population estimates were extracted from Bing Tiles (Zoom Level 12) to 2019 LADs by assigning tiles to LADs by their percent areal overlap. This method assumes constant population distribution across a single Bing Tile.

2019 LAD boundaries are available from the UK Government Open Geography Portal.

These indicators are designed to accompany the SHMI publication. COVID-19 activity is included in the SHMI if the discharge date is on or after 1 September 2021. Contextual indicators on the number of provider spells which are related to COVID-19 and on the number of provider spells as a percentage of pre-pandemic activity (January 2019 – December 2019) are produced to support the interpretation of the SHMI. These indicators are being published as official statistics in development. Official statistics in development are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. Notes: 1. There is a shortfall in the number of records for East Lancashire Hospitals NHS Trust (trust code RXR) and Harrogate and District NHS Foundation Trust (trust code RCD). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 2. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 3. Royal Surrey County Hospital NHS Foundation Trust (trust code RA2) has a high percentage of records with no data for secondary diagnoses. This is having a large impact on this trust’s data and values for this trust should therefore be interpreted with caution. 4. There is a high percentage of invalid diagnosis codes for Chesterfield Royal Hospital NHS Foundation Trust (trust code RFS), East Lancashire Hospitals NHS Trust (trust code RXR), Portsmouth Hospitals University NHS Trust (trust code RHU), and University Hospitals Plymouth NHS Trust (trust code RK9). Values for these trusts should therefore be interpreted with caution. 5. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the SHMI background quality report. 6. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

ObjectivesTo identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).MethodsWe searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.Results83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.ConclusionFew models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.Systematic Review Registration:[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.

The Public Health Research Database (PHRD) is a linked asset which currently includes Census 2011 data; Mortality Data; Hospital Episode Statistics (HES); GP Extraction Service (GPES) Data for Pandemic Planning and Research data. Researchers may apply for these datasets individually or any combination of the current 4 datasets.

The purpose of this dataset is to enable analysis of deaths involving COVID-19 by multiple factors such as ethnicity, religion, disability and known comorbidities as well as age, sex, socioeconomic and marital status at subnational levels. 2011 Census data for usual residents of England and Wales, who were not known to have died by 1 January 2020, linked to death registrations for deaths registered between 1 January 2020 and 8 March 2021 on NHS number. The data exclude individuals who entered the UK in the year before the Census took place (due to their high propensity to have left the UK prior to the study period), and those over 100 years of age at the time of the Census, even if their death was not linked. The dataset contains all individuals who died (any cause) during the study period, and a 5% simple random sample of those still alive at the end of the study period. For usual residents of England, the dataset also contains comorbidity flags derived from linked Hospital Episode Statistics data from April 2017 to December 2019 and GP Extraction Service Data from 2015-2019.

Abstract copyright UK Data Service and data collection copyright owner.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.

The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.

The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

LFS response to COVID-19

From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2024 Reweighting

In February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.

End User Licence and Secure Access QLFS data

Two versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job...

This systematic review aimed to provide an overview of the clinical profile and outcome of COVID-19 infection in patients with hemoglobinopathy. The rate of COVID-19 mortality and its predictors were also identified. A systematic search was conducted in accordance with PRISMA guidelines in five electronic databases (PubMed, Scopus, Web of Science, Embase, WHO COVID-19 database) for articles published between 1st December 2019 to 31st October 2020. All articles with laboratory-confirmed COVID-19 cases with underlying hemoglobinopathy were included. Methodological quality was assessed using the Joanna Briggs Institute (JBI) critical appraisal checklists. Thirty-one articles with data on 246 patients with hemoglobinopathy were included in this review. In general, clinical manifestations of COVID-19 infection among patients with hemoglobinopathy were similar to the general population. Vaso-occlusive crisis occurred in 55.6% of sickle cell disease patients with COVID-19 infection. Mortality from COVID-19 infection among patients with hemoglobinopathy was 6.9%. After adjusting for age, gender, types of hemoglobinopathy and oxygen supplementation, respiratory (adj OR = 89.63, 95% CI 2.514–3195.537, p = 0.014) and cardiovascular (adj OR = 35.20, 95% CI 1.291–959.526, p = 0.035) comorbidities were significant predictors of mortality. Patients with hemoglobinopathy had a higher mortality rate from COVID-19 infection compared to the general population. Those with coexisting cardiovascular or respiratory comorbidities require closer monitoring during the course of illness. More data are needed to allow a better understanding on the clinical impact of COVID-19 infections among patients with hemoglobinopathy.Clinical Trial Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020218200.

Context

On December 31, 2019, Chinese officials informed the first case of COVID-19 in Wuhan (China). Around the end of January, 2020, many countries (the U.S., the UK, South Korea, etc.), including Vietnam reported their first COVID-19 cases.

Since then, each country has their own specific strategy to contain the outbreak. Most of the countries have now shifted from the containment (early tracking, isolating the infection sources) to serious mitigation (tactics to reduce transmission) paradigms. Although loosing some F0 cases, Vietnam still has remained safely in the containment stage.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3439828%2Fbe8a17529fc1b48e3c44be94afe75529%2FVietnam_trend.png?generation=1588195825303050&alt=media" alt="">

Vietnam currently has only 270 COVID-19 confirmed cases in total with NO FATALITIES. And now, Vietnam is on its 13 straight days with no new local transmitted cases and 5 straight days without any imported cases (Updated on April 29, 2020). This leave us so many question to ask.

1. What has happened in Vietnam? Was the number of COVID-19 cases reported by Vietnamese officials undercounted? Did testing work well in Vietnam?

2. Did the Vietnam government suppressed information about their local COVID-19 pandemic? And if not, with such the 'real' low number of cases and no death, how did Vietnam contain the virus?

3. What did we know about the Vietnam COVID-19 patients? Is there characteristics of the patients that helps slow down the infection rate in Vietnam?

One remarkable thing about Vietnam health care system is the fact that privacy laws are not as stringent as in the US, Canada or the EU. Therefore, COVID-19 patient data in Vietnam is publicly available. For some cases, detail gets seriously down to their names, their personal contacts, daily activities and even their habits.

To help answer some of the above questions, I decided to collect the Vietnam data and study it independently using all the information available on the internet. I hope this dataset will provide some insights into the COVID-19 pandemic at the specific country level.

Collection methodology

• Data was acquired by web scrapping with manually curated from the Vietnam Ministry of Health's website (https://ncov.moh.gov.vn/) and other mainstream media in Vietnam (which were cited specifically in each data row).
• The world data was obtained from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). https://github.com/CSSEGISandData/COVID-19

Disclaimer:

• This is my personal work with no link to any organization. Although this analysis is data-driven, my comments reflect my personal perspectives.
• My results are based on the data collected from the Vietnam Health Ministry website and the mainstream media in Vietnam. The data might be biased and reflects what is publicly available on the internet. However, it can served as a good reference for someone who are curious about the COVID-19 pandemic in Vietnam. Some information conflicts (about the data) would be explained in detail in the exploration notebook that goes together with this dataset.

A full report and visualization for this dataset can be found in my Medium site.

Dataset was updated until May 10, 2020.

The food and drink market in the United Kingdom saw its e-commerce user penetration rate significantly increase after December 2019, as the COVID-19 pandemic led more consumers to purchase online. That month, the share of the UK population buying food and drinks online was approximately ** percent. By December 2020, that figure had increased *****percentage points. As of June 2025, around****percent of the UK population purchased food and drinks online. Online food delivery market in the UK With an estimated ** billion U.S. dollars in revenues, the United Kingdom ranked third among the leading online food delivery markets worldwide in 2024. China held the top spot, with a market size of nearly *** billion dollars, followed by the United States with an estimated *** billion U.S. dollars. In terms of retailers, Tesco was the uncontested market leader, generating more than * billion U.S. dollars in e-commerce net sales in the UK in 2023. Fresh commerce Order fulfillment for fresh food and beverages may be the most challenging aspect of online food delivery for both retailers and consumers alike. It is of utmost importance for fresh fruits and vegetables to arrive at customers' doorstep without being damaged or without going bad on the way. In 2022, approximately ** percent of UK consumers reported purchasing fresh food and beverages online, making the United Kingdom the only European market with such a high user adoption of fresh e-commerce. Additionally, around ** percent of UK shoppers reported ordering fresh food online at least once a week, indicating that this practice is not only widely adopted in the country, but it is also done on a very frequent basis.

This is an indicator designed to accompany the Summary Hospital-level Mortality Indicator (SHMI). As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. This indicator shows the number of provider spells which are coded as COVID-19, and therefore excluded from the SHMI, as a percentage of all provider spells in the SHMI (prior to the exclusion). This indicator is being published as an experimental statistic. Experimental statistics are official statistics which are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. Notes: 1. Please note that there has been a fall in the number of spells for most trusts between this publication and the previous SHMI publication, ranging from 0 per cent to 5 per cent. This is due to COVID-19 impacting on activity from March 2020 onwards and appears to be an accurate reflection of hospital activity rather than a case of missing data. 2. Day cases and regular day attenders are excluded from the SHMI. However, some day cases for University College London Hospitals NHS Foundation Trust (trust code RRV) have been incorrectly classified as ordinary admissions meaning that they have been included in the SHMI. Maidstone and Tunbridge Wells NHS Trust (trust code RWF) has submitted a number of records with a patient classification of ‘day case’ or ‘regular day attender’ and an intended management value of ‘patient to stay in hospital for at least one night’. This mismatch has resulted in the patient classification being updated to ‘ordinary admission’ by the HES data cleaning rules. This may have resulted in the number of ordinary admissions being overstated. The trust has been contacted to clarify what the correct patient classification is for these records. Values for these trusts should therefore be interpreted with caution. 3. There is a shortfall in the number of records for Mid Cheshire Hospitals NHS Foundation Trust (trust code RBT), meaning that values for this trust are based on incomplete data and should therefore be interpreted with caution. 4. On 1 February 2021 Royal Brompton and Harefield NHS Foundation Trust (trust code RT3) merged with Guy's and St Thomas' NHS Foundation Trust (trust code RJ1). This new organisation structure is reflected from this publication onwards. 5. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.