Incentives for self-isolation based on incidence rather than prevalence could help to flatten the curve: a modelling study

TL;DR

This study models how incentivizing self-isolation based on incidence rather than prevalence can influence epidemic dynamics, showing potential to flatten the curve during outbreaks through targeted behavioral incentives.

Contribution

It introduces a novel SIS-based model incorporating imitation dynamics and incentives based on incidence, providing analytical insights and simulation validation on empirical networks.

Findings

Favors incidence-based incentives can flatten the epidemic curve transiently.

Equilibrium prevalence depends mainly on cost and isolation duration.

Network structure has limited impact on the core dynamics.

Abstract

In recent years, numerous advances have been made in understanding how epidemic dynamics is affected by changes in individual behaviours. We propose an SIS-based compartmental model to tackle the simultaneous and coupled evolution of an outbreak and of the adoption by individuals of the isolation measure. The compliance with self-isolation is described with the help of the imitation dynamics framework. Individuals are incentivised to isolate based on the prevalence and the incidence rate of the outbreak, and are tempted to defy isolation recommendations depending on the duration of isolation and on the cost of putting social interactions on hold. We are able to derive analytical results on the equilibria of the model under the homogeneous mean-field approximation. Simulating the compartmental model on empirical networks, we also do a preliminary check of the impact of a network structure on our analytical predictions. We find that the dynamics collapses to surprisingly simple regimes where either the imitation dynamics no longer plays a role or the equilibrium prevalence depends on only two parameters of the model, namely the cost and the relative time spent in isolation. Whether individuals prioritise disease prevalence or incidence as an indicator of the state of the outbreak appears to play no role on the equilibria of the dynamics. However, it turns out that favouring incidence may help to flatten the curve in the transient phase of the dynamics. We also find a fair agreement between our analytical predictions and simulations run on an empirical multiplex network.