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Applying flow cytometry and machine learning on data from more than 500 individuals, we showed that the number of T cells with positive expression of functional markers not only could distinguish patients who recovered from COVID-19 from controls and pre-COVID donors but also identify previously unrecognized asymptomatic patients from mild, moderate, and severe recovered patients. The celluloepidemiology approach was uniquely capable to differentiate health care worker groups with different SARS-CoV-2 exposures from each other. T cell receptor (TCR) profiling strengthened our analysis by revealing that SARS-CoV-2\u2013specific TCRs were more abundant in patients than in controls. We believe that adding data on T cell reactivity will complement serology and augment the value of infection morbidity modeling for populations.<\/jats:p>","DOI":"10.1126\/sciadv.adt2926","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T17:58:50Z","timestamp":1747418330000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":4,"title":["Celluloepidemiology\u2014A paradigm for quantifying infectious disease dynamics on a population level"],"prefix":"10.1126","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6262-341X","authenticated-orcid":true,"given":"My K.","family":"Ha","sequence":"first","affiliation":[{"name":"Center for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium."},{"name":"Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious 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