Effect of risk perception on epidemic spreading in temporal networks

TL;DR

This paper investigates how risk perception influences epidemic spreading in temporal networks, showing that awareness can significantly reduce prevalence and shift epidemic thresholds, with effects varying between synthetic and empirical contact data.

Contribution

It extends epidemic models to temporal networks incorporating risk awareness, revealing its impact on epidemic thresholds and prevalence through analytical and numerical analysis.

Findings

Awareness does not change the epidemic threshold analytically.

Prevalence can be significantly decreased by awareness.

Finite-size effects influence the epidemic threshold in synthetic networks.

Abstract

Many progresses in the understanding of epidemic spreading models have been obtained thanks to numerous modeling efforts and analytical and numerical studies, considering host populations with very different structures and properties, including complex and temporal interaction networks. Moreover, a number of recent studies have started to go beyond the assumption of an absence of coupling between the spread of a disease and the structure of the contacts on which it unfolds. Models including awareness of the spread have been proposed, to mimic possible precautionary measures taken by individuals that decrease their risk of infection, but have mostly considered static networks. Here, we adapt such a framework to the more realistic case of temporal networks of interactions between individuals. We study the resulting model by analytical and numerical means on both simple models of temporal networks and empirical time-resolved contact data. Analytical results show that the epidemic threshold is not affected by the awareness but that the prevalence can be significantly decreased. Numerical studies highlight however the presence of very strong finite-size effects, in particular for the more realistic synthetic temporal networks, resulting in a significant shift of the effective epidemic threshold in the presence of risk awareness. For empirical contact networks, the awareness mechanism leads as well to a shift in the effective threshold and to a strong reduction of the epidemic prevalence.