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

Affiliation

Antonelli M(1), Capdevila J(2), Chaudhari A(3), Granerod J(3), Canas LS(1), Graham MS(1), Klaser K(1), Modat M(1), Molteni E(1), Murray B(1), Sudre CH(4), Davies R(2), May A(2), Nguyen LH(5), Drew DA(5), Joshi A(5), Chan AT(5), Cramer JP(3), Spector T(6), Wolf J(2), Ourselin S(1), Steves CJ(7), Loeliger AE(3).
Author information:
(1)School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
(2)Zoe Global, London, United Kingdom.
(3)Coalition for Epidemic Preparedness Innovations, London, United Kingdom.
(4)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.
(5)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.
(6)Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom.
(7)Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom. Electronic address: [Email]

Abstract

Update of medRxiv. 2020 Dec 01;: