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In this paper, we investigate the distributed link removal strategy fornetworked meta-population epidemics. In particular, a deterministic networkedsusceptible-infected-recovered (SIR) model is considered to describe theepidemic evolving process. In order to curb the spread of epidemics, we presentthe spectrum-based optimization problem involving the Perron-Frobeniuseigenvalue of the matrix constructed by the network topology and transitionrates. A modified distributed link removal strategy is developed such that itcan be applied to the SIR model with heterogeneous transition rates on weighteddigraphs. The proposed approach is implemented to control the COVID-19 pandemicby using the reported infected and recovered data in each state of Germany. Thenumerical experiment shows that the infected percentage can be significantlyreduced by using the distributed link removal strategy.

Distributed Link Removal Strategy for Networked Meta-Population Epidemics and its Application to the Control of the COVID-19 Pandemic