Celluloepidemiology: generating and modeling SARS-COV-2 specific T-cell responses on a population level for more accurate interventions in public health

Mathematical simulation models have become indispensable tools for forecasting and studying the effectiveness of intervention strategies such as lockdowns and screening during the SARS-CoV-2 pandemic. Estimation of key modeling quantities uses the serological footprint of an infection on the host. However, although depending on the type of assay, SARS-CoV-2 antibody titers were frequently not found in young and/or asymptomatic individuals and were shown to wane after a relatively short period, especially in asymptomatic individuals. In contrast, T-cells have been found in different situations - also without antibodies being present - ranging from convalescent asymptomatic to mild SARS-CoV-2 patients and their household members, thereby indicating that T-cells offer more sensitivity to detect past exposure to SARS-CoV-2 than the detection of antibodies can. In this project, we will gather on a population level T-cell and antibody SARS-CoV-2 specific data from different well-described cohorts including 300 individuals (and 200 household members) who have had proven covid19 infection > 3 months earlier, 100 general practitioners, 100 hospital workers, 500 randomly selected individuals and 75 pre-covid-era PBMC/sera. This data will be used in comparative simulation models and will lead to a reassessment of several key epidemiological estimates such as herd immunity and the reproduction number R that will significantly inform covid-19 related public health interventions.