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Antonelli M, Capdevila J, Chaudhari A, Granerod J, Canas LS, Graham MS, Klaser K, Modat M, Molteni E, Murray B, Sudre CH, Davies R, May A, Nguyen LH, Drew DA, Joshi A, Chan AT, Cramer JP, Spector T, Wolf J, Ourselin S, Steves CJ, Loeliger AE. Optimal symptom combinations to aid COVID-19 case identification: Analysis from a community-based, prospective, observational cohort. J Infect 2021; 82:384-390. [PMID: 33592254 PMCID: PMC7881291 DOI: 10.1016/j.jinf.2021.02.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/08/2021] [Accepted: 02/10/2021] [Indexed: 01/10/2023]
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.
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Affiliation(s)
- M Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | | | - A Chaudhari
- Coalition for Epidemic Preparedness Innovations, London, United Kingdom
| | - J Granerod
- Coalition for Epidemic Preparedness Innovations, London, United Kingdom
| | - L S Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - M S Graham
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - K Klaser
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - M Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - E Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - B Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - C H Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom; MRC Unit for Lifelong Health and Ageing at UCL/Centre for Medical Image Computing, Department of Computer Science, UCL, London, United Kingdom
| | - R Davies
- Zoe Global, London, United Kingdom
| | - A May
- Zoe Global, London, United Kingdom
| | - L H Nguyen
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - D A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - A Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - A T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - J P Cramer
- Coalition for Epidemic Preparedness Innovations, London, United Kingdom
| | - T Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - J Wolf
- Zoe Global, London, United Kingdom
| | - S Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - C J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom.
| | - A E Loeliger
- Coalition for Epidemic Preparedness Innovations, London, United Kingdom
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Antonelli M, Capdevila J, Chaudhari A, Granerod J, Canas LS, Graham MS, Klaser K, Modat M, Molteni E, Murray B, Sudre CH, Davies R, May A, Nguyen LH, Drew DA, Joshi A, Chan AT, Cramer JP, Spector T, Wolf J, Ourselin S, Steves CJ, Loeliger AE. Optimal symptom combinations to aid COVID-19 case identification: analysis from a community-based, prospective, observational cohort. medRxiv 2021:2020.11.23.20237313. [PMID: 33269364 PMCID: PMC7709185 DOI: 10.1101/2020.11.23.20237313] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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.
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Affiliation(s)
- M Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | | | - A Chaudhari
- Coalition for Epidemic Preparedness Innovations, London, UK
| | - J Granerod
- Coalition for Epidemic Preparedness Innovations, London, UK
| | - L S Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - M S Graham
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - K Klaser
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - M Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - E Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - B Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - C H Sudre
- 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
| | | | | | - L H Nguyen
- 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
- 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
- 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
- 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
- Coalition for Epidemic Preparedness Innovations, London, UK
| | - T Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - S Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - C J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - A E Loeliger
- Coalition for Epidemic Preparedness Innovations, London, UK
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