All data relating to “Self-reported long COVID after infection with the Omicron variant in the UK".

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.

Objectives: To investigate changes in beliefs and behaviours following news of the Omicron variant and changes to guidance understanding of Omicron-related guidance, and factors associated with engaging with protective behaviours.Design: Series of cross-sectional surveys (1 November to 16 December 2021, five waves of data collection).Setting: Online.Participants: People living in England, aged 16 years or over (n=1622–1902 per wave).Primary and secondary outcome measures: Levels of worry and perceived risk, and engagement with key behaviours (out-of-home activities, risky social mixing, wearing a face covering and testing uptake).Results: Degree of worry and perceived risk of COVID-19 (to oneself and people in the UK) fluctuated over time, increasing slightly around the time of the announcement about Omicron (p

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.

BackgroundMandatory COVID-19 certification, showing proof of vaccination, negative test, or recent infection to access to public venues, was introduced at different times in the four countries of the UK. We aim to study its effects on the incidence of cases and hospital admissions.MethodsWe performed Negative binomial segmented regression and ARIMA analyses for four countries (England, Northern Ireland, Scotland and Wales), and fitted Difference-in-Differences models to compare the latter three to England, as a negative control group, since it was the last country where COVID-19 certification was introduced. The main outcome was the weekly averaged incidence of COVID-19 cases and hospital admissions.ResultsCOVID-19 certification led to a decrease in the incidence of cases and hospital admissions in Northern Ireland, as well as in Wales during the second half of November. The same was seen for hospital admissions in Wales and Scotland during October. In Wales the incidence rate of cases in October already had a decreasing tendency, as well as in England, hence a particular impact of COVID-19 certification was less obvious. Method assumptions for the Difference-in-Differences analysis did not hold for Scotland. Additional NBSR and ARIMA models suggest similar results, while also accounting for correlation in the latter. The assessment of the effect in England itself leads one to believe that this intervention might not be strong enough for the Omicron variant, which was prevalent at the time of introduction of COVID-19 certification in the country.ConclusionsMandatory COVID-19 certification reduced COVID-19 transmission and hospitalizations when Delta predominated in the UK, but lost efficacy when Omicron became the most common variant.

Analysis comparing the risk of coronavirus (COVID-19) death in people infected by Omicron and Delta variants, after adjusting for socio-demographic factors, vaccination status and health conditions.

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech (speech not available in open access version) were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

Contents

• participant_metadata.csv row-wise, participant identifier indexed information on participant demographics and health status. Please see A large-scale and PCR-referenced vocal audio dataset for COVID-19 for a full description of the dataset.
• audio_metadata.csv row-wise, participant identifier indexed information on three recorded audio modalities, including audio filepaths. Please see A large-scale and PCR-referenced vocal audio dataset for COVID-19 for a full description of the dataset.
• train_test_splits.csv row-wise, participant identifier indexed information on train test splits for the following sets: 'Randomised' train and test set, Standard' train and test set, Matched' train and test sets, 'Longitudinal' test set and 'Matched Longitudinal' test set. Please see Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers for a full description of the train test splits.
• audio/ directory containing all the recordings in .wav format
  • Due to the large size of the dataset, to assist with ease of download, the audio files have been zipped into covid_data.z{ip, 01-24}. This enables the dataset to be downloaded in short periods, reducing the chances of a dropped internet connection scuppering progress. To unzip, first, ensure that all zip files are in the same directory. Then run the command 'unzip covid_data.zip' or right-click on 'covid_data.zip' and use a programme such as 'The Unarchiver' to open the file.
  • Once extracted, to check the validity of the download, please run the 'python Turing-RSS-Health-Data-Lab-Biomedical-Acoustic-Markers/data-paper/unit-tests.py. All tests should pass with no exceptions. Please clone the GitHub repo detailed below.
• README.md full dataset descriptor.
• DataDictionary_UKCOVID19VocalAudioDataset_OpenAccess.xlsx descriptor of each dataset attribute with the percentage coverage.

Code Base

The accompanying code can be found here: https://github.com/alan-turing-institute/Turing-RSS-Health-Data-Lab-Biomedical-Acoustic-Markers

Citations:

Please cite.

@article{coppock2022,

author = {Coppock, Harry and Nicholson, George and Kiskin, Ivan and Koutra, Vasiliki and Baker, Kieran and Budd, Jobie and Payne, Richard and Karoune, Emma and Hurley, David and Titcomb, Alexander and Egglestone, Sabrina and Cañadas, Ana Tendero and Butler, Lorraine and Jersakova, Radka and Mellor, Jonathon and Patel, Selina and Thornley, Tracey and Diggle, Peter and Richardson, Sylvia and Packham, Josef and Schuller, Björn W. and Pigoli, Davide and Gilmour, Steven and Roberts, Stephen and Holmes, Chris},

title = {Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers},

journal = {arXiv},

year = {2022},

doi = {10.48550/ARXIV.2212.08570},

url = {https://arxiv.org/abs/2212.08570},

}

@article{budd2022,

author={Jobie Budd and Kieran Baker and Emma Karoune and Harry Coppock and Selina Patel and Ana Tendero Cañadas and Alexander Titcomb and Richard Payne and David Hurley and Sabrina Egglestone and Lorraine Butler and George Nicholson and Ivan Kiskin and Vasiliki Koutra and Radka Jersakova and Peter Diggle and Sylvia Richardson and Bjoern Schuller and Steven Gilmour and Davide Pigoli and Stephen Roberts and Josef Packham Tracey Thornley Chris Holmes},

title={A large-scale and PCR-referenced vocal audio dataset for COVID-19},

year={2022},

journal={arXiv},

doi = {10.48550/ARXIV.2212.07738}

}

@article{Pigoli2022,

author={Davide Pigoli and Kieran Baker and Jobie Budd and Lorraine Butler and Harry Coppock and Sabrina Egglestone and Steven G.\ Gilmour and Chris Holmes and David Hurley and Radka Jersakova and Ivan Kiskin and Vasiliki Koutra and George Nicholson and Joe Packham and Selina Patel and Richard Payne and Stephen J.\ Roberts and Bj\"{o}rn W.\ Schuller and Ana Tendero-Ca$\tilde{n}$adas and Tracey Thornley and Alexander Titcomb},

title={Statistical Design and Analysis for Robust Machine Learning: A Case Study from Covid-19},

year={2022},

journal={arXiv},

doi = {10.48550/ARXIV.2212.08571}

}

The Dublin Core™ Metadata Initiative

- Title: The UK COVID-19 Vocal Audio Dataset, Open Access Edition.

- Creator: The UK Health Security Agency (UKHSA) in collaboration with The Turing-RSS Health Data Lab.

- Subject: COVID-19, Respiratory symptom, Other audio, Cough, Asthma, Influenza.

- Description: The UK COVID-19 Vocal Audio Dataset Open Access Edition is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs and exhalations were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset Open Access Edition represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

- Publisher: The UK Health Security Agency (UKHSA).

- Contributor: The UK Health Security Agency (UKHSA) and The Alan Turing Institute.

- Date: 2021-03/2022-03

- Type: Dataset

- Format: Waveform Audio File Format audio/wave, Comma-separated values text/csv

- Identifier: 10.5281/zenodo.10043978

- Source: The UK COVID-19 Vocal Audio Dataset Protected Edition, accessed via application to Accessing UKHSA protected data.

- Language: eng

- Relation: The UK COVID-19 Vocal Audio Dataset Protected Edition, accessed via application to Accessing UKHSA protected data.

- Coverage: United Kingdom, 2021-03/2022-03.

- Rights: Open Government Licence version 3 (OGL v.3), © Crown Copyright UKHSA 2023.

- accessRights: When you use this information under the Open Government Licence, you should include the following attribution: The UK COVID-19 Vocal Audio Dataset Open Access Edition, UK Health Security Agency, 2023, licensed under the Open Government Licence v3.0 and cite the papers detailed above.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. 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.

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. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

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 

Supplementary Figures, Tables and Videos for the following paper published in Cell Reports:

A delicate balance between antibody evasion and ACE2 affinity for Omicron BA.2.75

Jiandong Huo1,2,3,#,*, Aiste Dijokaite-Guraliuc4,#, Chang Liu4,5,#, Raksha Das4, Piyada Supasa4, Muneeswaran Selvaraj4, Rungtiwa Nutalai4, Daming Zhou2,5, Alexander J. Mentzer4,7, Donal Skelly7,8,9, Thomas G. Ritter7, Ali Amini7,8,10, Sagida Bibi11, Sandra Adele7, Sile Ann Johnson7, Neil G. Paterson6, Mark A. Williams6, David R. Hall6, Megan Plowright12,13, Thomas A.H. Newman12,13, Hailey Hornsby12, Thushan I de Silva12,13, Nigel Temperton14, Paul Klenerman7,8,10,15, Eleanor Barnes7,8,10,15, Susanna J. Dunachie7,8,16,17, Andrew J Pollard11,15, Teresa Lambe5,11, Philip Goulder8,18, OPTIC consortium&, ISARIC4C consortium$, Elizabeth E. Fry2*, Juthathip Mongkolsapaya4,5,*, Jingshan Ren2,*, David I. Stuart2,5,6,*,^, Gavin R Screaton4,5,*

1. State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
2. Division of Structural Biology, Nuffield Department of Medicine, University of Oxford, The Wellcome Centre for Human Genetics, Oxford, UK
3. Guangzhou Laboratory, Bio-island, Guangzhou 510320, China
4. Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
5. Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, UK
6. Diamond Light Source Ltd, Harwell Science & Innovation Campus, Didcot, UK

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.

Snapshot img

Europe Coronavirus Test Kits Market Size 2024-2028

The coronavirus test kits market in Europe size is forecast to decrease by USD 1.89 billion at a CAGR of -72.5% between 2023 and 2028.

The European coronavirus test kits market is experiencing significant growth due to the increasing demand for rapid diagnostic solutions. The emergence of SARS-CoV-2 variants, such as the Delta variant, has highlighted the importance of accurate and timely testing. Oropharyngeal swabs, nasal swabs, and sputum samples are commonly used for diagnosing COVID-19 infections. Point-of-Care (PoC) kits have gained popularity due to their convenience and quick results. However, the accuracy of diagnostic tests remains a challenge, with the Indian Council of Medical Research (ICMR) and the Health Ministry reporting false positives and negatives. The market is expected to continue its expansion as the world navigates the ongoing pandemic.

What will be the Size of the Market During the Forecast Period?

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The European coronavirus test kits market is witnessing significant growth due to the ongoing pandemic caused by SARS-CoV-2. The demand for test kits is driven by the need for early detection and rapid screening of infected individuals to prevent the spread of the virus within communities. According to the medical device database from GlobalData, RT-PCR tests remain the gold standard for diagnosing SARS-CoV-2 infection. These tests detect viral genetic material from human nasal samples, providing accurate results. However, the time-consuming nature of these tests and the requirement for specialized equipment have led to the emergence of alternative solutions, such as SARS-CoV-2 antigen tests.
Moreover, rapid antigen tests, also known as point-of-care (PoC) kits, offer user-friendly solutions for healthcare systems. These tests provide results within minutes, making them ideal for mass screening in various settings, including schools, workplaces, and airports. The Delta variant and the emerging Omicron variant of SARS-CoV-2 have added to the urgency for effective testing solutions. The European Union has been proactive in addressing this need, with initiatives such as the European Health Union and the EU Digital COVID Certificate system. The European coronavirus test kits market is expected to continue its growth trajectory, driven by the ongoing pandemic and the need for regular testing to ensure public health and safety

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Government
  Non government


Type

  Rapid test kit
  RT-PCR
  Others


Geography

  Europe

    Germany
    UK
    France

By End-user Insights

The government segment is estimated to witness significant growth during the forecast period.

In Europe, various diagnostic techniques are utilized to identify COVID-19 cases, with WHO recommending that countries with limited testing capacity or inexperienced national laboratories send their initial positive and negative samples to five referral laboratories in Europe for confirmatory testing. These laboratories include the German coronavirus diagnostic working group at Charite and Robert Koch Institute in Berlin, Erasmus Medical Center in Rotterdam, the Institute Pasteur in Paris, and the Respiratory Virus Unit at Public Health England. Additionally, several other laboratories in Belgium, Luxembourg, the Netherlands, and Spain offer diagnostic testing support. In the UK, Public Health England (PHE) regional laboratories provide testing facilities alongside WHO referral laboratories. As the world awaits vaccinations and booster doses, public awareness remains crucial. During the flu season, mask mandates and social distancing measures continue to be essential preventative measures.

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Market Dynamics

Our Europe Coronavirus Test Kits Market researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in the adoption of the European coronavirus Test Kits Market?

Rising adoption of rapid coronavirus test kits is the key driver of the market.

In Europe, the coronavirus pandemic has put immense pressure on healthcare systems, particularly in terms of diagnostic capabilities. To mitigate this challenge, European governments have prioritized expanding their testing capacity through various means. In 2022, there was a significant push to distribute coronavirus test kits across

Here we report binding index of 305 human HLA class I molecules from 18,771 unique haplotypes of 28,104 individuals to the 821 peptides experimentally observed from spike protein receptor-binding domain (RBD) of 5 main SARS-CoV-2 strains hydrolysed by human proteasomes with constitutive and immuno catalytic phenotypes. Our data read that 4 point mutations in the C-terminal RBD region 496-505 of Omicron B1.1.529 strain results in a dramatic increase of proteasome-mediated release of two public HLA class I epitopes covering 82% and 27% of world population haplotypes. Global population analysis of HLA class I haplotypes specific to these peptides demonstrated decreased mortality of human populations bearing these haplotypes to COVID-19 after but not before December, 2021, when Omicron spread over the world and became dominant SARS-CoV-2 strain. Analysis of population frequency of HLA class I alleles revealed that HLA-B*07:02, -B*08:01, -B*15:01, -C*01:02, -C*06:02 and -C*07:02 potentially provides increased resistance of human population to Omicron. Concluding, we found direct experimental observation, which might be one of the key factors that forced the SARS-CoV-2 virus to cross back the red line of pandemic status.

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.

BackgroundUneven vaccination and less resilient health care systems mean hospitals in LMICs are at risk of being overwhelmed during periods of increased COVID-19 infection. Risk-scores proposed for rapid triage of need for admission from the emergency department (ED) have been developed in higher-income settings during initial waves of the pandemic.MethodsRoutinely collected data for public hospitals in the Western Cape, South Africa from the 27th August 2020 to 11th March 2022 were used to derive a cohort of 446,084 ED patients with suspected COVID-19. The primary outcome was death or ICU admission at 30 days. The cohort was divided into derivation and Omicron variant validation sets. We developed the LMIC-PRIEST score based on the coefficients from multivariable analysis in the derivation cohort and existing triage practices. We externally validated accuracy in the Omicron period and a UK cohort.ResultsWe analysed 305,564 derivation, 140,520 Omicron and 12,610 UK validation cases. Over 100 events per predictor parameter were modelled. Multivariable analyses identified eight predictor variables retained across models. We used these findings and clinical judgement to develop a score based on South African Triage Early Warning Scores and also included age, sex, oxygen saturation, inspired oxygen, diabetes and heart disease. The LMIC-PRIEST score achieved C-statistics: 0.82 (95% CI: 0.82 to 0.83) development cohort; 0.79 (95% CI: 0.78 to 0.80) Omicron cohort; and 0.79 (95% CI: 0.79 to 0.80) UK cohort. Differences in prevalence of outcomes led to imperfect calibration in external validation. However, use of the score at thresholds of three or less would allow identification of very low-risk patients (NPV ≥0.99) who could be rapidly discharged using information collected at initial assessment.ConclusionThe LMIC-PRIEST score shows good discrimination and high sensitivity at lower thresholds and can be used to rapidly identify low-risk patients in LMIC ED settings.

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.