Data driven phenotyping and COVID-19 case definitions: a pattern recognition approach.

Introduction COVID-19 has pathological pulmonary as well as several extrapulmonary manifestations and thus many different symptoms may arise in patients. The aim of our study was to determine COVID-19 syndromic phenotypes in a data driven manner using survey results extracted from Carnegie Mellon University9s Delphi Group. Methods Monthly survey results (>1 million responders per month; 320.326 responders with positive COVID-19 test and disease duration 75%. These scores along with symptom duration were subsequently used by the Two Step Clustering algorithm to produce symptom clusters. Post-hoc logistic regression models adjusting for age, gender and comorbidities and confirmatory linear principal components analyses were used to further explore the data. The model created from 66.165 included responders in August, was subsequently validated in data from March to December 2020. Results Five validated COVID 19 syndromes were identified in August: 1. Afebrile (0%), Non-Coughing (0%), Oligosymptomatic (ANCOS) 2. Febrile (100%) Multisymptomatic (FMS) 3. Afebrile (0%) Coughing (100%) Oligosymptomatic (ACOS), 4. Oligosymptomatic with additional self-described symptoms (100%; OSDS) and 5. Olfaction / Gustatory Impairment Predominant (100%; OGIP). Discussion We present 5 distinct symptom phenotypes within the COVID-19 spectrum that remain stable within 9 to 12 days of first symptom onset. The typical febrile respiratory phenotype is presented as a minority among identified syndromes, a finding that may impact both epidemiological surveillance norms and transmission dynamics.