An efficient strategy to suppress epidemic explosion in heterogeneous metapopulation networks

TL;DR

This paper introduces a targeted resource allocation strategy in heterogeneous metapopulation networks that optimizes epidemic suppression by tuning curing rates based on node degree, significantly increasing the epidemic threshold.

Contribution

The study proposes a novel degree-dependent curing rate scheme with an optimal exponent, validated through simulations and mean field analysis, to effectively prevent epidemic outbreaks in complex networks.

Findings

Epidemic threshold peaks at an optimal exponent $$ in the curing rate.

The optimal exponent $$ is robust across different network sizes and average degrees.

Different epidemic spreading routes occur depending on whether the exponent is above or below $$.

Abstract

We propose an efficient strategy to suppress epidemic explosion in heterogeneous metapopulation networks, wherein each node represents a subpopulation with any number of individuals and is assigned a curing rate that is proportional to $k^{\alpha}$ with $k$ the node degree and $\alpha$ an adjustable parameter. We have performed stochastic simulations of the dynamical reaction-diffusion processes associated with the susceptible-infected-susceptible model in scale-free networks. We found that the epidemic threshold reaches a maximum when the exponent $\alpha$ is tuned to be $\alpha_{opt}\simeq 1.3$. This nontrivial phenomenon is robust to the change of the network size and the average degree. In addition, we have carried out a mean field analysis to further validate our scheme, which also demonstrates that epidemic explosion follows different routes for $\alpha$ larger or less than $\alpha_{opt}$. Our work suggests that in order to effectively suppress epidemic spreading on heterogeneous complex networks, subpopulations with higher degrees should be allocated more resources than just being linearly dependent on the degree $k$.