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

TL;DR

This paper proposes a distributed link removal strategy based on spectral optimization to control networked epidemic spread, demonstrated through COVID-19 data in Germany, effectively reducing infection rates.

Contribution

It introduces a novel spectrum-based distributed link removal method for heterogeneous networked SIR models, applicable to real-world epidemic control like COVID-19.

Findings

Significant reduction in infection percentage using the strategy

Effective control demonstrated on COVID-19 data from Germany

Applicable to heterogeneous weighted directed networks

Abstract

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.