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Maria Gomez

Maria Gomez


Maria Gomez

Symptom clusters in COVID-19 : A potential clinical prediction tool from the COVID Symptom Study app


  • Carole H. Sudre
  • Karla A. Lee
  • Mary Ni Lochlainn
  • Thomas Varsavsky
  • Benjamin Murray
  • Mark S. Graham
  • Cristina Menni
  • Marc Modat
  • Ruth C.E. Bowyer
  • Long H. Nguyen
  • David A. Drew
  • Amit D. Joshi
  • Wenjie Ma
  • Chuan Guo Guo
  • Chun Han Lo
  • Sajaysurya Ganesh
  • Abubakar Buwe
  • Joan Capdevila Pujol
  • Julien Lavigne du Cadet
  • Alessia Visconti
  • Maxim B. Freidin
  • Julia S. El-Sayed Moustafa
  • Mario Falchi
  • Richard Davies
  • Maria F. Gomez
  • Tove Fall
  • M. Jorge Cardoso
  • Jonathan Wolf
  • Paul W. Franks
  • Andrew T. Chan
  • Tim D. Spector
  • Claire J. Steves
  • Sébastien Ourselin

Summary, in English

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.


  • Diabetic Complications
  • EXODIAB: Excellence of Diabetes Research in Sweden
  • Genomics, Diabetes and Endocrinology
  • Genetic and Molecular Epidemiology
  • EpiHealth: Epidemiology for Health

Publishing year





Science Advances





Document type

Journal article


American Association for the Advancement of Science (AAAS)


  • Infectious Medicine



Research group

  • Diabetic Complications
  • Genomics, Diabetes and Endocrinology
  • Genetic and Molecular Epidemiology


  • ISSN: 2375-2548