Optimal symptom combinations to aid COVID-19 case identification: Analysis from a community-based, prospective, observational cohort. Issue 3 (March 2021) - Antonelli, M.; Capdevila, J.; Chaudhari, A.; Granerod, J.; Canas, L.S.; Graham, M.S.; Klaser, K.; Modat, M.; Molteni, E.; Murray, B.; Sudre, C.H.; Davies, R.; et al.
Highlights: Widely recommended symptoms identified only ∼70% COVID-19 cases. Additional symptoms increased case finding to > 90% but tests needed doubled. Optimal symptom combinations maximise case capture considering available resources. Implications for COVID-19 vaccine efficacy trials and wider public health. 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.