Suppressing disease spreading by using information diffusion on multiplex networks

TL;DR

This paper explores how information diffusion can be used to suppress disease spreading in multiplex networks, revealing an optimal transmission rate that effectively reduces disease outbreaks based on empirical data and modeling.

Contribution

It introduces a coevolution model of information and disease spreading on multiplex networks and identifies an optimal information transmission rate for disease suppression.

Findings

Asymmetrical interactions between information and disease spreading.

An optimal information transmission rate significantly suppresses disease spread.

The model's dynamics align with real-world spreading processes.

Abstract

Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate.