As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome . Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations . Clustering was validated on an independent replication dataset between 1 and 28 May 2020 . Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic - area under the curve) of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5 %). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.
MeSH: Adult, COVID-19, diagnosis, Diagnosis, Computer-Assisted, Female, Humans, Male, Middle Aged, Mobile Applications, Predictive Value of Tests, Retrospective Studies, Risk Factors, SARS-CoV-2