SIS Epidemic Spreading with Correlated Heterogeneous Infection Rates

TL;DR

This paper investigates how correlated heterogeneous infection rates, which depend on node degrees, influence epidemic spreading on different network topologies, revealing that correlation sign and recovery rate critically affect spread dynamics.

Contribution

It introduces a correlated heterogeneous SIS model based on real-world data and compares it with uncorrelated scenarios, providing analytical and simulation insights into epidemic spread.

Findings

Negative correlation promotes spreading at low recovery rates.

Positive correlation facilitates spreading at higher recovery rates.

Results validated on real-world networks.

Abstract

The epidemic spreading has been widely studied when each node may get infected by an infected neighbor with the same rate. However, the infection rate between a pair of nodes is usually heterogeneous and even correlated with their nodal degrees in the contact network. We aim to understand how such correlated heterogeneous infection rates influence the spreading on different network topologies. Motivated by real-world datasets, we propose a correlated heterogeneous Susceptible-Infected-Susceptible model which assumes that the infection rate $\beta_{ij}(=\beta_{ji})$ between node $i$ and $j$ is correlated with the degree of the two end nodes: $\beta_{ij}=c(d_id_j)^\alpha$, where $\alpha$ indicates the strength of the correlation and $c$ is selected so that the average infection rate is $1$. In order to understand the effect of such correlation on epidemic spreading, we consider as well the corresponding uncorrected but still heterogeneous infection rate scenario as a reference, where the original correlated infection rates in our CSIS model are shuffled and reallocated to the links of the same network topology. We compare these two scenarios in the average fraction of infected nodes in the metastable state on Erd{\"o}s-R{\'e}nyi (ER) and scale-free (SF) networks with a similar average degree. Through the continuous-time simulations, we find that, when the recovery rate is small, the negative correlation is more likely to help the epidemic spread and the positive correlation prohibit the spreading; as the recovery rate increases to be larger than a critical value, the positive but not negative correlation tends to help the spreading. Our findings are further analytically proved in a wheel network (one central node connects with each of the nodes in a ring) and validated on real-world networks with correlated heterogeneous interaction frequencies.