Abstract
To complement serology as a tool in public health interventions, we introduced the "celluloepidemiology" paradigm where we leveraged pathogen-specific T cell responses at a population level to advance our epidemiological understanding of infectious diseases, using SARS-CoV-2 as a model. 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-specific 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.