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COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data
George D Vavougios1,2,3, Vasileios T Stavrou2, Christoforos Konstantatos4
1Department of Neurology, University of Cyprus, 75 Kallipoleos Street, Lefkosia 1678, Cyprus.
Summary
This study identified five distinct COVID-19 syndromes using survey data, revealing that current definitions focusing only on respiratory symptoms may miss many cases. Understanding these COVID-19 phenotypes is crucial for accurate diagnosis.
Area of Science:
- Epidemiology
- Data Science
- Public Health
Background:
- Current COVID-19 definitions primarily focus on respiratory symptoms.
- This narrow focus may lead to underdetection of the full spectrum of COVID-19 illness.
- A data-driven approach is needed to identify distinct COVID-19 syndromic phenotypes.
Purpose of the Study:
- To determine COVID-19 syndromic phenotypes in a data-driven manner.
- To identify and validate distinct clusters of symptoms associated with COVID-19.
- To assess if current disease definitions capture the full range of COVID-19 presentations.
Main Methods:
- Utilized monthly survey data from Carnegie Mellon University’s Delphi Group (>1 million responders/month).
- Employed Logistic Regression-weighted multiple correspondence analysis (LRW-MCA) for symptom preprocessing.
- Applied Two Step Clustering algorithm to identify symptom clusters, validated with logistic regression and principal component analyses.
Main Results:
- Identified five validated COVID-19 syndromes: ANCOS, FMS, ACOS, OSDS, and OGIP.
- These syndromes represent diverse symptom profiles beyond just respiratory illness.
- Model creation in August was successfully validated across data from March-December 2020.
Conclusions:
- The COVID-19 spectrum is broader than current definitions suggest.
- Relying solely on respiratory symptoms can lead to missed diagnoses.
- Recognizing diverse COVID-19 phenotypes is essential for comprehensive public health strategies.
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