作者
Liane S Canas, Carole H Sudre, Joan Capdevila Pujol, Lorenzo Polidori, Benjamin Murray, Erika Molteni, Mark S Graham, Kerstin Klaser, Michela Antonelli, Sarah Berry, Richard Davies, Long H Nguyen, David A Drew, Jonathan Wolf, Andrew T Chan, Tim Spector, Claire J Steves, Sebastien Ourselin, Marc Modat
发表日期
2021/7/29
期刊
The Lancet Digital Health
卷号
3
期号
9
页码范围
e587-e598
出版商
Elsevier
简介
Background
Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not suitable for the early detection of infection. We aimed to estimate the probability of an individual being infected with SARS-CoV-2 on the basis of early self-reported symptoms to enable timely self-isolation and urgent testing.
Methods
In this large-scale, prospective, epidemiological surveillance study, we used prospective, observational, longitudinal, self-reported data from participants in the UK on 19 symptoms over 3 days after symptoms onset and COVID-19 PCR test results extracted from the COVID-19 Symptom Study mobile phone app. We divided the study population into a training set (those who reported symptoms between April 29, 2020, and Oct 15, 2020) and a …
学术搜索中的文章
LS Canas, CH Sudre, JC Pujol, L Polidori, B Murray… - The Lancet Digital Health, 2021