  In this paper, we investigate the distributed link removal strategy for
networked meta-population epidemics. In particular, a deterministic networked
susceptible-infected-recovered (SIR) model is considered to describe the
epidemic evolving process. In order to curb the spread of epidemics, we present
the spectrum-based optimization problem involving the Perron-Frobenius
eigenvalue of the matrix constructed by the network topology and transition
rates. A modified distributed link removal strategy is developed such that it
can be applied to the SIR model with heterogeneous transition rates on weighted
digraphs. The proposed approach is implemented to control the COVID-19 pandemic
by using the reported infected and recovered data in each state of Germany. The
numerical experiment shows that the infected percentage can be significantly
reduced by using the distributed link removal strategy.
