Before revision:  Using mathematical models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10  %DIF < more than state-of-the-art edge-betweenness methods.
After revision:  Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10  %DIF < more than state-of-the-art edge-betweenness methods.