Optimal symptom combinations to aid COVID-19 case identification: analysis from a community-based, prospective, observational cohort.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
08 Feb 2021
Historique:
pubmed: 4 12 2020
medline: 4 12 2020
entrez: 3 12 2020
Statut: epublish

Résumé

Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.

Identifiants

pubmed: 33269364
doi: 10.1101/2020.11.23.20237313
pmc: PMC7709185
pii:
doi:

Types de publication

Preprint

Langues

eng

Commentaires et corrections

Type : UpdateIn

Déclaration de conflit d'intérêts

Conflicts of interests Potential conflicts of interest. JW, RD, JCP, and AM are employees of Zoe Global Ltd. ATC reports grants from Massachusetts Consortium on Pathogen Readiness during the conduct of the study, personal fees from Pfizer Inc., and grants and personal fees from Bayer Pharma; CEPI (authors AC, JG, JPC, AEL) funds clinical trials of COVID-19 vaccines. All other authors declare no competing interests.

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Auteurs

M Antonelli (M)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

J Capdevila (J)

Zoe Global, London, UK.

A Chaudhari (A)

Coalition for Epidemic Preparedness Innovations, London, UK.

J Granerod (J)

Coalition for Epidemic Preparedness Innovations, London, UK.

L S Canas (LS)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

M S Graham (MS)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

K Klaser (K)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

M Modat (M)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

E Molteni (E)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

B Murray (B)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

C H Sudre (CH)

MRC Unit for Lifelong Health and Ageing at UCL/Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

R Davies (R)

Zoe Global, London, UK.

A May (A)

Zoe Global, London, UK.

L H Nguyen (LH)

Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

D A Drew (DA)

Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

A Joshi (A)

Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

A T Chan (AT)

Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

J P Cramer (JP)

Coalition for Epidemic Preparedness Innovations, London, UK.

T Spector (T)

Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.

J Wolf (J)

Zoe Global, London, UK.

S Ourselin (S)

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

C J Steves (CJ)

Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.

A E Loeliger (AE)

Coalition for Epidemic Preparedness Innovations, London, UK.

Classifications MeSH