An efficient strategy to suppress epidemic explosion in heterogeneous metapopulation networks

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 $k$.