  The COVID-19 pandemic has created unprecedented challenges for governments
and healthcare systems worldwide, highlighting the critical importance of
understanding the factors that contribute to virus transmission. This study
aimed to identify the most influential age groups in COVID-19 infection rates
at the US county level using the Modified Morris Method and deep learning for
time series. Our approach involved training the state-of-the-art time-series
model Temporal Fusion Transformer on different age groups as a static feature
and the population vaccination status as the dynamic feature. We analyzed the
impact of those age groups on COVID-19 infection rates by perturbing individual
input features and ranked them based on their Morris sensitivity scores, which
quantify their contribution to COVID-19 transmission rates. The findings are
verified using ground truth data from the CDC and US Census, which provide the
true infection rates for each age group. The results suggest that young adults
were the most influential age group in COVID-19 transmission at the county
level between March 1, 2020, and November 27, 2021. Using these results can
inform public health policies and interventions, such as targeted vaccination
strategies, to better control the spread of the virus. Our approach
demonstrates the utility of feature sensitivity analysis in identifying
critical factors contributing to COVID-19 transmission and can be applied in
other public health domains.
