@article{d03d0a24576f4ece9762d08641f3bbd7,
title = "Optimal symptom combinations to aid COVID-19 case identification: Analysis from a community-based, prospective, observational cohort",
abstract = "Objectives: 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. Methods: 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. Findings: 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. Interpretation: 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.",
keywords = "Community-based cohort, COVID-19, Optimal symptom combinations, SARS-CoV-2, Vaccine trials",
author = "M. Antonelli and J. Capdevila and A. Chaudhari and J. Granerod and Canas, {L. S.} and Graham, {M. S.} and K. Klaser and M. Modat and E. Molteni and B. Murray and Sudre, {C. H.} and R. Davies and A. May and Nguyen, {L. H.} and Drew, {D. A.} and A. Joshi and Chan, {A. T.} and Cramer, {J. P.} and T. Spector and J. Wolf and S. Ourselin and Steves, {C. J.} and Loeliger, {A. E.}",
note = "Funding Information: Zoe provided in kind support for all aspects of building, running and supporting the app and service to all users worldwide. CEPI provided funding for the analysis of the data. Support for this study was provided by the NIHR-funded Biomedical Research Centre based at GSTT NHS Foundation Trust. Investigators also received support from the Wellcome Trust, the MRC/BHF, Alzheimer's Society, EU, NIHR, CDRF, and the NIHR-funded BioResource, Clinical Research Facility and BRC based at GSTT NHS Foundation Trust in partnership with KCL, the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, the Wellcome Flagship Programme (WT213038/Z/18/Z), the Chronic Disease Research Foundation, and DHSC. DAD is supported by the National Institute of Diabetes and Digestive and Kidney Diseases K01DK120742 and by the American Gastroenterological Association AGA-Takeda COVID-19 Rapid Response Research Award (AGA2021–5102). ATC was supported in this work through a Stuart and Suzanne Steele MGH Research Scholar Award. The Massachusetts Consortium on Pathogen Readiness (MassCPR) and Mark and Lisa Schwartz supported MGH investigators (LHN, DAD, ADJ, ATC). Funding Information: This work was supported by Zoe Global Limited; Department of Health; Wellcome Trust; Engineering and Physical Sciences Research Council (EPSRC); National Institute for Health Research (NIHR); Medical Research Council (MRC); Alzheimer's Society; Massachusetts Consortium for Pathogen Readiness (MassCPR); and Coalition for Epidemic Preparedness Innovations (CEPI). Publisher Copyright: {\textcopyright} 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = mar,
doi = "10.1016/j.jinf.2021.02.015",
language = "English",
volume = "82",
pages = "384--390",
journal = "Journal of Infection",
issn = "0163-4453",
publisher = "British Infection Society",
number = "3",
}