Learn about our response to COVID-19, including freely available research and expanded remote access support.

Universal Nonlinear Infection Kernel from Heterogeneous Exposure on Higher-Order Networks

Guillaume St-Onge, Hanlin Sun, Antoine Allard, Laurent Hébert-Dufresne, and Ginestra Bianconi
Phys. Rev. Lett. 127, 158301 – Published 6 October 2021

Abstract

The collocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network. Standard epidemic models fail to capture the potential complexity of this scenario by (1) neglecting the higher-order structure of contacts that typically occur through environments like workplaces, restaurants, and households, and (2) assuming a linear relationship between the exposure to infected contacts and the risk of infection. Here, we leverage a hypergraph model to embrace the heterogeneity of environments and the heterogeneity of individual participation in these environments. We find that combining heterogeneous exposure with the concept of minimal infective dose induces a universal nonlinear relationship between infected contacts and infection risk. Under nonlinear infection kernels, conventional epidemic wisdom breaks down with the emergence of discontinuous transitions, superexponential spread, and hysteresis.

  • Figure
  • Figure
  • Received 2 February 2021
  • Revised 26 July 2021
  • Accepted 25 August 2021

DOI:https://doi.org/10.1103/PhysRevLett.127.158301

© 2021 American Physical Society

Physics Subject Headings (PhySH)

Nonlinear DynamicsInterdisciplinary PhysicsNetworks

Authors & Affiliations

Guillaume St-Onge1,2,*, Hanlin Sun3, Antoine Allard1,2,4, Laurent Hébert-Dufresne1,4,5, and Ginestra Bianconi3,6

  • 1Département de physique, de génie physique et d’optique, Université Laval, Québec (Québec) G1V 0A6, Canada
  • 2Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec (Québec) G1V 0A6, Canada
  • 3School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
  • 4Vermont Complex Systems Center, University of Vermont, Burlington, Vermont 05405, USA
  • 5Department of Computer Science, University of Vermont, Burlington, Vermont 05405, USA
  • 6The Alan Turing Institute, 96 Euston Road, London NW1 2DB, United Kingdom

  • *Corresponding author. guillaume.st-onge.4@ulaval.ca

Article Text

Click to Expand

Supplemental Material

Click to Expand

References

Click to Expand
Issue

Vol. 127, Iss. 15 — 8 October 2021

Reuse & Permissions
Access Options
CHORUS

Article part of CHORUS

APS and the Physical Review Editorial Office Continue to Support Researchers

COVID-19 has impacted many institutions and organizations around the world, disrupting the progress of research. Through this difficult time APS and the Physical Review editorial office are fully equipped and actively working to support researchers by continuing to carry out all editorial and peer-review functions and publish research in the journals as well as minimizing disruption to journal access.

We appreciate your continued effort and commitment to helping advance science, and allowing us to publish the best physics journals in the world. And we hope you, and your loved ones, are staying safe and healthy.

Ways to Access APS Journal Articles Off-Campus

Many researchers now find themselves working away from their institutions and, thus, may have trouble accessing the Physical Review journals. To address this, we have been improving access via several different mechanisms. See Off-Campus Access to Physical Review for further instructions.

Sign up to receive regular email alerts from Physical Review Letters