Incentives for self-isolation based on incidence rather than prevalence could help to flatten the curve: A modeling study
In recent years, numerous advances have been made in understanding how epidemic dynamics are affected by changes in individual behaviors. We propose a Susceptible-Infected-Susceptible (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 incentivized 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 the 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 perform a preliminary check of the impact of a network structure on our analytical predictions. We find that the dynamics collapse 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 prioritize 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 favoring 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.
Authors
- Giulia de Meijere (ORCID: https://orcid.org/0000-0002-2112-2256)
- IRMAR, Université de Rennes, CNRS, IRMAR - UMR 6625, 35000 Rennes <i>and</i> INRAE, Agrocampus Ouest, Université de Rennes, IGEPP, Le Rheu, France. Now at Institut Denis Poisson (IDP), Université de Tours, 37000 Tours, France
- Hugo Martin
Institutions
- Université de Tours (FR)
- Centre National de la Recherche Scientifique (FR)
- Tampere University of Applied Sciences (FI)
- Institut Agro Rennes-Angers (FR)
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- Tampere University (FI)
- Institut de recherche mathématique de Rennes (FR)
- Institut Denis Poisson (FR)
Publication Details
- Journal
- Mathematical Biosciences & Engineering
- Published
- 2026-01-01
- DOI
- https://doi.org/10.3934/mbe.2026014
- Primary Topic
- COVID-19 epidemiological studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00