A large-scale and PCR-referenced vocal audio dataset for COVID-19

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Abstract

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 were collected in the ‘Speak up and help beat coronavirus’ digital survey alongside demographic, symptom and self-reported respiratory condition data. Digital survey submissions were 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 70,565 of 72,999 participants and 24,105 of 25,706 positive cases. Respiratory symptoms were reported by 45.6% of participants. This dataset has additional potential uses for bioacoustics research, with 11.3% participants self-reporting asthma, and 27.2% with linked influenza PCR test results.

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SARS-CoV-2, SARS-CoV, and MERS-CoV viral load dynamics, duration of viral shedding, and infectiousness: a systematic review and meta-analysis

Muge Cevik, Matthew C. Tate, Ollie Lloyd … (2020)

Background Viral load kinetics and duration of viral shedding are important determinants for disease transmission. We aimed to characterise viral load dynamics, duration of viral RNA shedding, and viable virus shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in various body fluids, and to compare SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV) viral dynamics. Methods In this systematic review and meta-analysis, we searched databases, including MEDLINE, Embase, Europe PubMed Central, medRxiv, and bioRxiv, and the grey literature, for research articles published between Jan 1, 2003, and June 6, 2020. We included case series (with five or more participants), cohort studies, and randomised controlled trials that reported SARS-CoV-2, SARS-CoV, or MERS-CoV infection, and reported viral load kinetics, duration of viral shedding, or viable virus. Two authors independently extracted data from published studies, or contacted authors to request data, and assessed study quality and risk of bias using the Joanna Briggs Institute Critical Appraisal Checklist tools. We calculated the mean duration of viral shedding and 95% CIs for every study included and applied the random-effects model to estimate a pooled effect size. We used a weighted meta-regression with an unrestricted maximum likelihood model to assess the effect of potential moderators on the pooled effect size. This study is registered with PROSPERO, CRD42020181914. Findings 79 studies (5340 individuals) on SARS-CoV-2, eight studies (1858 individuals) on SARS-CoV, and 11 studies (799 individuals) on MERS-CoV were included. Mean duration of SARS-CoV-2 RNA shedding was 17·0 days (95% CI 15·5–18·6; 43 studies, 3229 individuals) in upper respiratory tract, 14·6 days (9·3–20·0; seven studies, 260 individuals) in lower respiratory tract, 17·2 days (14·4–20·1; 13 studies, 586 individuals) in stool, and 16·6 days (3·6–29·7; two studies, 108 individuals) in serum samples. Maximum shedding duration was 83 days in the upper respiratory tract, 59 days in the lower respiratory tract, 126 days in stools, and 60 days in serum. Pooled mean SARS-CoV-2 shedding duration was positively associated with age (slope 0·304 [95% CI 0·115–0·493]; p=0·0016). No study detected live virus beyond day 9 of illness, despite persistently high viral loads, which were inferred from cycle threshold values. SARS-CoV-2 viral load in the upper respiratory tract appeared to peak in the first week of illness, whereas that of SARS-CoV peaked at days 10–14 and that of MERS-CoV peaked at days 7–10. Interpretation Although SARS-CoV-2 RNA shedding in respiratory and stool samples can be prolonged, duration of viable virus is relatively short-lived. SARS-CoV-2 titres in the upper respiratory tract peak in the first week of illness. Early case finding and isolation, and public education on the spectrum of illness and period of infectiousness are key to the effective containment of SARS-CoV-2. Funding None.

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Symptom prevalence, duration, and risk of hospital admission in individuals infected with SARS-CoV-2 during periods of omicron and delta variant dominance: a prospective observational study from the ZOE COVID Study

Cristina Menni, Ana M. Valdes, Lorenzo Polidori … (2022)

Background The SARS-CoV-2 variant of concern, omicron, appears to be less severe than delta. We aim to quantify the differences in symptom prevalence, risk of hospital admission, and symptom duration among the vaccinated population. Methods In this prospective longitudinal observational study, we collected data from participants who were self-reporting test results and symptoms in the ZOE COVID app (previously known as the COVID Symptoms Study App). Eligible participants were aged 16–99 years, based in the UK, with a body-mass index between 15 and 55 kg/m 2 , had received at least two doses of any SARS-CoV-2 vaccine, were symptomatic, and logged a positive symptomatic PCR or lateral flow result for SARS-CoV-2 during the study period. The primary outcome was the likelihood of developing a given symptom (of the 32 monitored in the app) or hospital admission within 7 days before or after the positive test in participants infected during omicron prevalence compared with those infected during delta prevalence. Findings Between June 1, 2021, and Jan 17, 2022, we identified 63 002 participants who tested positive for SARS-CoV-2 and reported symptoms in the ZOE app. These patients were matched 1:1 for age, sex, and vaccination dose, across two periods (June 1 to Nov 27, 2021, delta prevalent at >70%; n=4990, and Dec 20, 2021, to Jan 17, 2022, omicron prevalent at >70%; n=4990). Loss of smell was less common in participants infected during omicron prevalence than during delta prevalence (16·7% vs 52·7%, odds ratio [OR] 0·17; 95% CI 0·16–0·19, p<0·001). Sore throat was more common during omicron prevalence than during delta prevalence (70·5% vs 60·8%, 1·55; 1·43–1·69, p<0·001). There was a lower rate of hospital admission during omicron prevalence than during delta prevalence (1·9% vs 2·6%, OR 0·75; 95% CI 0·57–0·98, p=0·03). Interpretation The prevalence of symptoms that characterise an omicron infection differs from those of the delta SARS-CoV-2 variant, apparently with less involvement of the lower respiratory tract and reduced probability of hospital admission. Our data indicate a shorter period of illness and potentially of infectiousness which should impact work–health policies and public health advice. Funding Wellcome Trust, ZOE, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, and Medical Research Council

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False-negative results of initial RT-PCR assays for COVID-19: A systematic review

Ingrid Arevalo-Rodriguez, Diana Buitrago-Garcia, Daniel Simancas-Racines … (2020)

Background A false-negative case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is defined as a person with suspected infection and an initial negative result by reverse transcription-polymerase chain reaction (RT-PCR) test, with a positive result on a subsequent test. False-negative cases have important implications for isolation and risk of transmission of infected people and for the management of coronavirus disease 2019 (COVID-19). We aimed to review and critically appraise evidence about the rate of RT-PCR false-negatives at initial testing for COVID-19. Methods We searched MEDLINE, EMBASE, LILACS, as well as COVID-19 repositories, including the EPPI-Centre living systematic map of evidence about COVID-19 and the Coronavirus Open Access Project living evidence database. Two authors independently screened and selected studies according to the eligibility criteria and collected data from the included studies. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We calculated the proportion of false-negative test results using a multilevel mixed-effect logistic regression model. The certainty of the evidence about false-negative cases was rated using the GRADE approach for tests and strategies. All information in this article is current up to July 17, 2020. Results We included 34 studies enrolling 12,057 COVID-19 confirmed cases. All studies were affected by several risks of bias and applicability concerns. The pooled estimate of false-negative proportion was highly affected by unexplained heterogeneity (tau-squared = 1.39; 90% prediction interval from 0.02 to 0.54). The certainty of the evidence was judged as very low due to the risk of bias, indirectness, and inconsistency issues. Conclusions There is substantial and largely unexplained heterogeneity in the proportion of false-negative RT-PCR results. The collected evidence has several limitations, including risk of bias issues, high heterogeneity, and concerns about its applicability. Nonetheless, our findings reinforce the need for repeated testing in patients with suspicion of SARS-Cov-2 infection given that up to 54% of COVID-19 patients may have an initial false-negative RT-PCR (very low certainty of evidence). Systematic review registration Protocol available on the OSF website: https://tinyurl.com/vvbgqya.

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Author and article information

Contributors

Emma Karoune:

ORCID: http://orcid.org/0000-0002-6576-6053

ekaroune@turing.ac.uk

Journal

Journal ID (nlm-ta): Sci Data

Journal ID (iso-abbrev): Sci Data

Title: Scientific Data

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2052-4463

Publication date (Electronic): 27 June 2024

Publication date PMC-release: 27 June 2024

Publication date Collection: 2024

Volume: 11

Electronic Location Identifier: 700

Affiliations

[1 ]GRID grid.83440.3b, ISNI 0000000121901201, London Centre for Nanotechnology, , University College London, ; London, UK

[2 ]Division of Medicine, University College London, ( https://ror.org/02jx3x895) London, UK

[3 ]King’s College London, ( https://ror.org/0220mzb33) London, UK

[4 ]The Alan Turing Institute, ( https://ror.org/035dkdb55) London, UK

[5 ]Imperial College London, ( https://ror.org/041kmwe10) London, UK

[6 ]UK Health Security Agency, ( https://ror.org/018h10037) London, UK

[7 ]Institute of Health Informatics, University College London, ( https://ror.org/02jx3x895) London, UK

[8 ]Centre for Stress and Age-Related Disease, School of Applied Sciences, University of Brighton, ( https://ror.org/04kp2b655) Brighton, UK

[9 ]University of Oxford, ( https://ror.org/052gg0110) Oxford, UK

[10 ]University of Surrey, ( https://ror.org/00ks66431) Guildford, UK

[11 ]The Surrey Institute for People-Centred AI, Centre for Vision, Speech and Signal Processing, Guildford, UK

[12 ]University of Lancaster, ( https://ror.org/04f2nsd36) Lancaster, UK

[13 ]GRID grid.6936.a, ISNI 0000000123222966, CHI, MRI, , Technical University of Munich, ; Munich, Germany

[14 ]University of Nottingham, ( https://ror.org/01ee9ar58) Nottingham, UK

Author information

Jobie Budd http://orcid.org/0000-0003-3337-6859

Emma Karoune http://orcid.org/0000-0002-6576-6053

Harry Coppock http://orcid.org/0000-0003-2404-8319

Richard Payne http://orcid.org/0000-0002-1721-3868

George Nicholson http://orcid.org/0000-0001-9588-6075

Davide Pigoli http://orcid.org/0000-0003-4591-4167

Tracey Thornley http://orcid.org/0000-0002-9276-052X

Article

Publisher ID: 3492

DOI: 10.1038/s41597-024-03492-w

PMC ID: 11211414

PubMed ID: 38937483

SO-VID: 276391d2-4620-4faa-984b-73991d3b8729

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 22 February 2024

Date accepted : 10 June 2024

Funding

Funded by: FundRef https://doi.org/10.13039/501100000266, RCUK | Engineering and Physical Sciences Research Council (EPSRC);

Award ID: EP/R00529X/1

Award Recipient : Jobie Budd Rachel A. McKendry

Funded by: FundRef https://doi.org/10.13039/501100000269, RCUK | Economic and Social Research Council (ESRC);

Award ID: ES/P000592/1

Award Recipient : Selina Patel

Funded by: FundRef https://doi.org/10.13039/100010665, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions);

Award ID: 801604

Award Recipient : Richard Payne

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Keywords: diagnostic markers,respiratory signs and symptoms

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Keywords: diagnostic markers, respiratory signs and symptoms

A large-scale and PCR-referenced vocal audio dataset for COVID-19

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UCL: Covid-19 Research

Most cited references 20

SARS-CoV-2, SARS-CoV, and MERS-CoV viral load dynamics, duration of viral shedding, and infectiousness: a systematic review and meta-analysis

Symptom prevalence, duration, and risk of hospital admission in individuals infected with SARS-CoV-2 during periods of omicron and delta variant dominance: a prospective observational study from the ZOE COVID Study

False-negative results of initial RT-PCR assays for COVID-19: A systematic review

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