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

This paper studies the distributed link removal problem for controlling epidemic spreading in a networked meta-population system. A deterministic networked susceptible-infected-recovered (SIR) model is considered to describe the epidemic evolving process. To curb the spread of epidemics, we reformulate the original topology design problem into a minimization program of the Perron-Frobenius eigenvalue of the matrix involving 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 infected and recovered data reported by the German federal states. The numerical experiment shows that the infected percentage can be significantly reduced by employing the distributed link removal strategy.