Evidence map›Paper›PMID 38937483›Full record

ArticleScientific data2024

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

Jobie Budd, Kieran Baker, Emma Karoune, Harry Coppock, Selina Patel, Richard Payne, Ana Tendero Cañadas, Alexander Titcomb, David Hurley, Sabrina Egglestone and 16 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. A summary of the ComParE COVID-19 challenges.Frontiers in digital health · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

26 authors.

Jobie BuddLondon Centre for Nanotechnology, University College London, London, UK.ORCID 0000-0003-3337-6859
Kieran BakerKing's College London, London, UK.
Emma KarouneThe Alan Turing Institute, London, UK. ekaroune@turing.ac.uk.ORCID 0000-0002-6576-6053
Harry CoppockThe Alan Turing Institute, London, UK.ORCID 0000-0003-2404-8319
Selina PatelUK Health Security Agency, London, UK.
Richard PayneUK Health Security Agency, London, UK.ORCID 0000-0002-1721-3868
Ana Tendero CañadasUK Health Security Agency, London, UK.
Alexander TitcombUK Health Security Agency, London, UK.
David HurleyUK Health Security Agency, London, UK.
Sabrina EgglestoneUK Health Security Agency, London, UK.
Lorraine ButlerUK Health Security Agency, London, UK.
Jonathon MellorUK Health Security Agency, London, UK.
George NicholsonThe Alan Turing Institute, London, UK.ORCID 0000-0001-9588-6075
Ivan KiskinUniversity of Surrey, Guildford, UK.
Vasiliki KoutraKing's College London, London, UK.
Radka JersakovaThe Alan Turing Institute, London, UK.
Rachel A McKendryLondon Centre for Nanotechnology, University College London, London, UK.
Peter DiggleUniversity of Lancaster, Lancaster, UK.
Sylvia RichardsonThe Alan Turing Institute, London, UK.
Björn W SchullerThe Alan Turing Institute, London, UK.
Steven GilmourKing's College London, London, UK.
Davide PigoliKing's College London, London, UK.ORCID 0000-0003-4591-4167
Stephen RobertsThe Alan Turing Institute, London, UK.
Josef PackhamUK Health Security Agency, London, UK.
Tracey ThornleyUK Health Security Agency, London, UK.ORCID 0000-0002-9276-052X
Chris HolmesThe Alan Turing Institute, London, UK.

Funding

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) 801604RCUK | Economic and Social Research Council (ESRC) ES/P000592/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/R00529X/1
6 · The paper itself

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.

Indexed as

COVID-19CoughExhalationHumansMachine LearningPolymerase Chain ReactionSpeechUnited Kingdom

Identifiers

PMID38937483
PMCPMC11211414

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.