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 covid-19 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.