COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmathsThis work is described in more detail on the UK Data Service Impact and Innovation Lab blog: https://blog.ukdataservice.ac.uk/visualising-high-risk-areas-for-covid-19-mortality/Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Estimates of the prevalence of self-reported long COVID and associated activity limitation, using UK Coronavirus (COVID-19) Infection Survey data. Experimental Statistics.

Experimental estimates of the prevalence and duration of long COVID symptoms, and rates of adverse events for hospitalised coronavirus (COVID-19) patients compared with those for matched control patients.

REal-time Assessment of Community Transmission (REACT-1) measured the prevalence of SARS-CoV-2 in the general population in England between May 2020 and March 2022. Each month, around 150,000 people completed a questionnaire and returned a PCR test.

Introduction: SARS-CoV-2 antibody detection serves as an important diagnostic marker for past SARS-CoV-2 infection and is essential to determine the spread of COVID-19, monitor potential COVID-19 long-term effects, and to evaluate possible protection from reinfection. A study was conducted across three hospital sites in a large central London NHS Trust in the UK, to evaluate the prevalence and duration of SARS-CoV-2 IgG antibody positivity in healthcare workers.Methods: A matrix equivalence study consisting of 228 participants was undertaken to evaluate the Abbott Panbio™ COVID-19 IgG/IgM rapid test device. Subsequently, 2001 evaluable healthcare workers (HCW), representing a diverse population, were enrolled in a HCW study between June and August 2020. A plasma sample from each HCW was evaluated using the Abbott Panbio™ COVID-19 IgG/IgM rapid test device, with confirmation of IgG-positive results by the Abbott ArchitectTM SARS-CoV-2 IgG assay. 545 participants, of whom 399 were antibody positive at enrolment, were followed up at 3 months.Results: The Panbio™ COVID-19 IgG/IgM rapid test device demonstrated a high concordance with laboratory tests. SARS-CoV-2 antibodies were detected in 506 participants (25.3%) at enrolment, with a higher prevalence in COVID-19 frontline (28.3%) than non-frontline (19.9%) staff. At follow-up, 274/399 antibody positive participants (68.7%) retained antibodies; 4/146 participants negative at enrolment (2.7%) had seroconverted. Non-white ethnicity, older age, hypertension and COVID-19 symptoms were independent predictors of higher antibody levels (OR 1.881, 2.422–3.034, 2.128, and 1.869 respectively), based on Architect™ index quartiles; participants in the first three categories also showed a greater antibody persistence at 3 months.Conclusion: The SARS-CoV-2 anti-nucleocapsid IgG positivity rate among healthcare staff was high, declining by 31.3% during the 3-month follow-up interval. Interestingly, the IgG-positive participants with certain risk factors for severe COVID-19 illness (older age, Black or Asian Ethnicity hypertension) demonstrated greater persistence over time when compared to the IgG-positive participants without these risk factors.

These files comprise the publicly available data for the COG-UK hospital-onset COVID-19 infection study. The individual CSV files provided are: - HOCI_public_dataset: Anonymized version of main study dataset, with one row per HOCI case included in the final analysis - HOCI_public_varlist: Variable descriptions for main study dataset - epi_data_combined: Weekly data on total SARS-CoV-2 +ve (cov_pos_epi) and -ve (cov_neg_epi) inpatients at each study site -community_incidence_summary: Weekly local community incidence data for each study site, per 100,000 people per week, obtained from UK government testing dashboard and weighted according to outer postcodes of inpatients at each site.

Notes on anonymisation: HOCI_public_dataset is an anonymised version of the main HOCI study database. In order to fully anonymise individuals, and because the focus of the study was on infection control actions rather than patient outcomes, all individual-level patient demographic and clinical characteristics have been removed. Site and ward names have been changed to anonymized codes, and all free text fields have been removed as some of these contained unblinded details of hospitals and wards. All date fields have been removed, with study week of SARS-CoV-2 +ve test result for each HOCI case provided.

Notes on acronyms: In ‘HOCI_public_varlist’, the following acronyms are used: AGP, aerosol-generating procedure CR, contact restrictions CT, contact tracing DIPC, Director of IPC HCAI, healthcare-associated infection HCW, healthcare worker IPC, infection prevention and control SR, sequence report SRO, sequence report output QM, quality management

The study will be expanding to other providers and care homes across England and will provide a detailed picture of prevalence, seroprevalence, transmission and potential immunity over time.By testing around 6500 staff and 5000 residents across >100 care homes in England, we will estimate the proportion who have been infected with COVID-19 in the past and have antibodies, and the proportion who are infected now. These tests will be repeated over time to learn how COVID-19 spreads in care homes and how long the antibody response lasts and whether this helps to prevent re-infection with the virus. In those who are currently infected, we will also collect information on who is experiencing symptoms to help us to understand how this affects spread of infection within care homes. We will find out about how infection spreads between care homes, the community and hospitals by linking the information we collect to national data on hospital admissions and deaths.

N.B.: The data within the VIVALDI 2 dataset is being examined and cleaned to improve its quality, this is ongoing work.

REal-time Assessment of Community Transmission (REACT-2) started in May 2020 to determine the prevalence of and trends in antibodies levels in study participants. This study involves approximately 150,000 unique people who use a finger prick test over 6 week periods, with additional information collected on contact with known cases to assess an infection point prevalence at national, regional and local levels. Within REACT 2 there is also a study on usability and efficacy of different tests.

Imperial College London is leading a major programme of home testing for COVID-19 to track the progress of the infection across England. Called REACT, the programme was commissioned by the Department of Health and Social Care, and is being carried out in partnership with Imperial College Healthcare NHS Trust and Ipsos MORI.

REACT-2 is a world largest surveillance study undertaken in England that examines the prevalence of antibodies in the community. The study focusses on finger prick self-testing at home by individuals aged 18 or over.The findings will provide the government with a better understanding of the use of antibody tests at home as well as assess the trends in antibody levels and how they vary across different population subgroups. This will inform government policies to protect health and save lives.

REACT-2 is a large-scale national surveillance study in England. It examines the prevalence of antibodies to SARS-CoV-2 in adults the community. Participants complete finger prick self-testing at home (lateral flow immunoassay) and a questionnaire.

Results from the COVID-19 and Respiratory Infections Survey.

Meta-analysis dataset for the following article: A Rapid Review and Meta-Analysis of the Asymptomatic Proportion of PCR-Confirmed SARS-CoV-2 Infections in Community Settings.These CSV format data enable the calculation of a pooled asymptomatic proportion of PCR-confirmed COVID-19 cases based on available studies (n=21) with methodologically-appropriate design to detect and identify truly asymptomatic cases. The asymptomatic proportion is given as the number of asymptomatic PCR-confirmed infections over the total number of PCR-confirmed infections.The file also includes the completed PRISMA checklist for this review.

Experimental estimates from three approaches to estimating the percentage of people testing positive for coronavirus (COVID-19) and who experience symptoms four or more weeks after infection, broken down by demographic and viral characteristics, using UK Coronavirus Infection Survey data.

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

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.

Mathematical and statistical models can be used to make predictions of how epidemics may progress in the near future and form a central part of outbreak mitigation and control. Renewal equation based models allow inference of epidemiological parameters from historical data and forecast future epidemic dynamics without requiring complex mechanistic assumptions. However, these models typically ignore interaction between age groups, partly due to challenges in parameterising a time varying interaction matrix. Social contact data collected regularly during the COVID-19 epidemic provide a means to inform interaction between age groups in real-time. We developed an age-specific forecasting framework and applied it to two age-stratified time-series: incidence of SARS-CoV-2 infection, estimated from a national infection and antibody prevalence survey; and, reported cases according to the UK national COVID-19 dashboard. Jointly fitting our model to social contact data from the CoMix study, we inferred a time-varying next generation matrix which we used to project infections and cases in the four weeks following each of 29 forecast dates between October 2020 and November 2021. We evaluated the forecasts using proper scoring rules and compared performance with three other models with alternative data and specifications alongside two naive baseline models. Overall, incorporating age interaction improved forecasts of infections and the CoMix-data-informed model was the best performing model at time horizons between two and four weeks. However, this was not true when forecasting cases. We found that age group interaction was most important for predicting cases in children and older adults. The contact-data-informed models performed best during the winter months of 2020–2021, but performed comparatively poorly in other periods. We highlight challenges regarding the incorporation of contact data in forecasting and offer proposals as to how to extend and adapt our approach, which may lead to more successful forecasts in future.

The Office for National Statistics COVID-19 Infection Survey (ONS-CIS) is the largest surveillance study of SARS-CoV-2 positivity in the community and collected data on the United Kingdom (UK) epidemic from April 2020 until March 2023 before being paused. Here, we report on the epidemiological and evolutionary dynamics of SARS-CoV-2 determined by analysing the sequenced samples collected by the ONS-CIS during this period. We observed a series of sweeps or partial sweeps, with each sweeping lineage having a distinct growth advantage compared to its predecessors. The sweeps also generated an alternating pattern in which most samples had either S-gene target failure (SGTF) or non-SGTF over time. Evolution was characterised by steadily increasing divergence and diversity within lineages but with step increases in divergence associated with each sweeping major lineage. This led to a faster overall rate of evolution when measured at the between-lineage level compared to within-lineages, and f...

Tuberculosis (TB) is caused by a bacterium called Mycobacterium tuberculosis.  TB remains a significant global health problem. The UK has one of the highest rates of TB in Europe, with almost 5000 new cases notified in 2019. Within the UK, Birmingham and the West Midlands are particular hotspots for TB, with over 300 cases of active disease and approximately 10 times that of new latent infections diagnosed each year.

Birmingham and the West Midlands have experienced particularly high rates of COVID-19 during the pandemic and there is increasing evidence that individuals of Black, Asian and minority ethnicities (BAME) experience the most significant morbidity and highest mortality rates due to COVID-19. These groups also experience the highest burdens of TB, both in the UK and overseas.

Epidemiological data suggests that current and previous tuberculosis (TB) increase the risk of COVID-19 mortality and severe disease. There is also evidence of immunopathogenic overlap between the two infections with in vitro studies finding that SARS-CoV-2 infection is increased in human macrophages cultured in the inflammatory milieu of TB-infected macrophages.

This dataset would enable a deeper analysis of demography and clinical outcomes associated with COVID-19 in patients with concurrent TB.

PIONEER geography: the West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

EHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All hospitalised patients admitted to UHB during the COVID-19 pandemic, curated to focus on Mycobacterium tuberculosis and SARS-CoV-2. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (A&E, triage, IP, ITU admissions), presenting complaint, DNAR teal, all physiology readings (AVPU scale, Covid CFS, blood pressure, respiratory rate, oxygen saturations and others), all blood results, imaging reports, all prescribed & administered treatments, all outcomes.

Available supplementary data: Matched controls; ambulance, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Initial estimates of prevalence of ongoing symptoms following coronavirus (COVID-19) infection in staff and pupils from the COVID-19 Schools Infection Survey across a sample of schools, within selected local authority areas in England. This Schools Infection Survey is jointly led by the London School of Hygiene & Tropical Medicine, Public Health England and the Office for National Statistics.