Modeling Risk Perception in Networks with Community Structure

TL;DR

This paper investigates how different levels of risk perception influence disease spread in modular social networks, highlighting the importance of local infection awareness and the tradeoffs involved.

Contribution

It introduces models of disease spread on modular networks considering various risk perception levels, emphasizing the effectiveness of local knowledge in controlling epidemics.

Findings

Scale-free networks exhibit faster disease progression than random networks.

Local infection knowledge is most effective in halting disease spread.

Cost-effectiveness of perception strategies involves tradeoffs between information quantity and impact.

Abstract

We study the influence of global, local and community-level risk perception on the extinction probability of a disease in several models of social networks. In particular, we study the infection progression as a susceptible-infected-susceptible (SIS) model on several modular networks, formed by a certain number of random and scale-free communities. We find that in the scale-free networks the progression is faster than in random ones with the same average connectivity degree. For what concerns the role of perception, we find that the knowledge of the infection level in one's own neighborhood is the most effective property in stopping the spreading of a disease, but at the same time the more expensive one in terms of the quantity of required information, thus the cost/effectiveness optimum is a tradeoff between several parameters.