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?:abstract
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BACKGROUND: Several COVID-19 vaccine efficacy trials are ongoing with others predicted to start soon. Diagnostic work-up of trial participants following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify an efficient symptom combination to capture most cases using the lowest possible number of tests. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms between March-September 2020 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 RT-PCR positive case were calculated for individual symptoms and symptom combinations. A multi-objective evolutionary algorithm was applied to generate symptom combinations with good trade-offs between sensitivity and specificity. FINDINGS: The UK dataset included 122,305 individuals (1,202 RT-PCR positive). Findings were replicated in a US dataset including 3,162 individuals (79 RT-PCR positive). Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 RT-PCR tests per positive case. The symptom combination with the highest sensitivity was fatigue, anosmia, cough, diarrhoea, headache, and sore throat, identifying 96% of cases and requiring 96 tests. INTERPRETATION: We confirm the significance of COVID-19 specific symptoms widely recommended for triggering RT-PCR. By using the data-driven optimization technique we identified additional symptoms (fatigue, sore throat, headache, diarrhoea) that enabled many more positive cases to be captured efficiently. By providing a set of solutions with optimal trade-offs between sensitivity and specificity, we produced a selection of symptom subsets that maximise the capture of cases given different laboratory capacities. The methodology may be of particular use for COVID-19 vaccine developers across a range of resource settings and have more far-reaching public health implications for detection of symptomatic SARS CoV2 infection.
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