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Observational Study
. 2021 Mar;82(3):384-390.
doi: 10.1016/j.jinf.2021.02.015. Epub 2021 Feb 13.

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

Affiliations
Observational Study

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

M Antonelli et al. J Infect. 2021 Mar.

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: COVID-19; Community-based cohort; Optimal symptom combinations; SARS-CoV-2; Vaccine trials.

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Conflict of interest statement

Declaration of Competing Interest 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.

Figures

Fig 1
Fig. 1
Symptom frequency for COVID-19 negative (left) and COVID-19 positive (right) cases.
Fig 2
Fig. 2
Combination of symptoms with highest sensitivity, sensitivity ∼ 90%, and specificity ∼50%.
Fig 3
Fig. 3
Pareto of optimal subset generated by the multi-objective evolutionary algorithm for three- and seven-day analyses Each point represents a subset of symptoms characterised by a different trade-off between sensitivity and specificity.
Fig 4
Fig. 4
Percentage of a symptom's appearance in symptom combinations with sensitivity ≥90%.

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