Dataset description

Daily official UK Covid data. The data is available per country (England, Scotland, Wales and Northern Ireland) and for different regions in England. The different regions are split into two different files as part of the data is directly gathered by the NHS (National Health Service). The files that contain the word 'nhsregion' in their name, include data related to hospitals only, such as number of admissions or number of people in respirators. The files containing the word 'region' in their name, include the rest of the data, such as number of cases, number of vaccinated people or number of tests performed per day. The next paragraphs describe the columns for the different file types.

Region files

Files related to regions (word 'region' included in the file name) have the following columns: - "date": date in YYYY-MM-DD format - "area type": type of area covered in the file (region or nation) - "area name": name of area covered in the file (region or nation name) - "daily cases": new cases on a given date - "cum cases": cumulative cases - "new deaths 28days": new deaths within 28 days of a positive test - "cum deaths 28days": cumulative deaths within 28 days of a positive test - "new deaths_60days": new deaths within 60 days of a positive test - "cum deaths 60days": cumulative deaths within 60 days of a positive test - "new_first_episode": new first episodes by date - "cum_first_episode": cumulative first episodes by date - "new_reinfections": new reinfections by specimen data - "cum_reinfections": cumualtive reinfections by specimen data - "new_virus_test": new virus tests by date - "cum_virus_test": cumulative virus tests by date - "new_pcr_test": new PCR tests by date - "cum_pcr_test": cumulative PCR tests by date - "new_lfd_test": new LFD tests by date - "cum_lfd_test": cumulative LFD tests by date - "test_roll_pos_pct": percentage of unique case positivity by date rolling sum - "test_roll_people": unique people tested by date rolling sum - "new first dose": new people vaccinated with a first dose - "cum first dose": cumulative people vaccinated with a first dose - "new second dose": new people vaccinated with a first dose - "cum second dose": cumulative people vaccinated with a first dose - "new third dose": new people vaccinated with a booster or third dose - "cum third dose": cumulative people vaccinated with a booster or third dose

Country files

Files related to countries (England, Northern Ireland, Scotland and Wales) have the above columns and also: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

NHS Region files

Files related to nhsregion (word 'nhsregion' included in the file name) have the following columns: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

It's worth noting that the dataset hasn't been cleaned and it needs cleaning. Also, different files have different null columns. This isn't an error in the dataset but the way different countries and regions report the data.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting at the local authority level in the United Kingdom.

Household gross saving rate in the United Kingdom spiked during the second quarter of 2020, reaching 25.9 percent, nearly three times as high as in the first quarter of the year. This unprecedented increase is due to the coronavirus (COVID-19) outbreak and the resulting widespread lockdown and temporary business closures. a similar increase can be seen during the second generalized lockdown implemented during the first quarter of 2021.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in the United Kingdom. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting at the local authority level in the United Kingdom.

The role COVID-19 vaccination on healthcare resource utilization (HCRU) and cost remains unclear, especially during Omicron predominance and among high risk UK populations. A retrospective cohort study using UK The Health Improvement Network (THIN) primary care data included adults (≥18 years) with confirmed or suspected COVID-19 between September 2022- May 2023. Three cohorts were defined: Highest risk (eligible for two seasonal doses), High Risk (eligible for one dose), and All COVID-19 patients. Long COVID was identified as ≥ 1 symptom or diagnostic/referral code, ≥4 weeks post COVID-19 diagnosis. Inverse probability of treatment weighting assessed associations between vaccination status (yes/no and time since vaccination) and long COVID, HCRU, and costs. In both Risk Cohorts, COVID-19 vaccination was not associated with long COVID incidence. However, in the High Risk (n = 1,889) and All Patients cohorts (n = 8,507) outpatient specialist referrals were significantly lower in the 3–6-month post-vaccination group versus > 6 months (rate ratio: 0.28; 95% CI: 0.10-0.79, p 

Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39–4.13), indicating that 58–76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6–7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090–33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information.This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

Time series of Covid-19 Case Fatality Rate (CFR) for Spain, the United Kingdom, and the United States.

Objective: Cardiac injury is detected in numerous patients with coronavirus disease 2019 (COVID-19) and has been demonstrated to be closely related to poor outcomes. However, an optimal cardiac biomarker for predicting COVID-19 prognosis has not been identified.Methods: The PubMed, Web of Science, and Embase databases were searched for published articles between December 1, 2019 and September 8, 2021. Eligible studies that examined the anomalies of different cardiac biomarkers in patients with COVID-19 were included. The prevalence and odds ratios (ORs) were extracted. Summary estimates and the corresponding 95% confidence intervals (95% CIs) were obtained through meta-analyses.Results: A total of 63 studies, with 64,319 patients with COVID-19, were enrolled in this meta-analysis. The prevalence of elevated cardiac troponin I (cTnI) and myoglobin (Mb) in the general population with COVID-19 was 22.9 (19–27%) and 13.5% (10.6–16.4%), respectively. However, the presence of elevated Mb was more common than elevated cTnI in patients with severe COVID-19 [37.7 (23.3–52.1%) vs.30.7% (24.7–37.1%)]. Moreover, compared with cTnI, the elevation of Mb also demonstrated tendency of higher correlation with case-severity rate (Mb, r = 13.9 vs. cTnI, r = 3.93) and case-fatality rate (Mb, r = 15.42 vs. cTnI, r = 3.04). Notably, elevated Mb level was also associated with higher odds of severe illness [Mb, OR = 13.75 (10.2–18.54) vs. cTnI, OR = 7.06 (3.94–12.65)] and mortality [Mb, OR = 13.49 (9.3–19.58) vs. cTnI, OR = 7.75 (4.4–13.66)] than cTnI.Conclusions: Patients with COVID-19 and elevated Mb levels are at significantly higher risk of severe disease and mortality. Elevation of Mb may serve as a marker for predicting COVID-19-related adverse outcomes.Prospero Registration Number:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020175133, CRD42020175133.

The COVID-19 pandemic has had a substantial impact on mental health; because students are particularly vulnerable to loneliness, isolation, stress and unhealthy lifestyle choices, their mental health and wellbeing may potentially be more severely impacted by lockdown measures than the general population. This study assessed the mental health and wellbeing of UK undergraduate students during and after the lockdowns associated with the COVID-19 pandemic. Data were collected via online questionnaire at 3 time points – during the latter part of the first wave of the pandemic (spring/summer 2020; n=46) while stringent lockdown measures were still in place but gradually being relaxed; during the second wave of the pandemic (winter 2020-21; n=86) while local lockdowns were in place across the UK; and during the winter of 2021-22 (n=77), when infection rates were high but no lockdown measures were in place. Stress was found to most strongly predict wellbeing and mental health measures during the two pandemic waves. Other substantial predictors were diet quality and intolerance of uncertainty. Positive wellbeing was the least well accounted for of our outcome variables. Conversely, we found that depression and anxiety were higher during winter 2021-22 (no lockdowns) than winter 2020-21 (under lockdown). This may be due to the high rates of infection over that period and the effects of COVID-19 infection itself on mental health. This suggests that, as significant as the effects of lockdowns were on the wellbeing of the nation, not implementing lockdown measures could potentially have been even more detrimental for mental health.

Time-varying model parameters fitted for Covid-19 in Spain, United Kingdom, and United States.

Comprehensive analysis of simulation results for Covid-19 in Spain, United Kingdom, and United States.

Epidemiological time series of Covid-19 for Spain, the United Kingdom, and the United States.

Comprehensive analysis of simulation results for Covid-19 in Spain, United Kingdom, and United States.

Results for Covid-19 simulation with data from Brazil, Spain, United Kingdom, and United States.