Predicting COVID-19 incidence from seroprevalence and population-based cohort data using interpretable machine learning with differential privacy analysis
This study demonstrates that integrating interpretable machine learning with differential privacy on aggregated seroprevalence and cohort data from Germany enables accurate prediction of local COVID-19 incidence and the identification of key behavioral and immunological transmission drivers, offering a valuable complement to routine surveillance for public health decision-making.