• Open Access

Anomalous role of information diffusion in epidemic spreading

Xiaochen Wang, Xuzhen Zhu, Xiaofeng Tao, Jinghua Xiao, Wei Wang, and Ying-Cheng Lai
Phys. Rev. Research 3, 013157 – Published 17 February 2021

Abstract

A widely held belief in network epidemiology is that information diffusion makes individuals aware of the epidemic and thus drives them to seek protections from nonpharmaceutical or pharmaceutical resources, which can help suppress its spread. However, as the COVID-19 pandemic has revealed, excessive information diffusion can trigger irrational acquisition and hoarding behaviors, which can lead to shortages of resources even for those in urgent need, consequently, worsening disease spreading. To develop a quantitative understanding of the effect of information diffusion on epidemic spreading, subject to allocations of limited resources, has become an urgently important problem with broad implications. We construct a multiplex network framework to characterize the complex interplay among resource allocation, information diffusion, and epidemic spreading, and develop a microscopic Markov chain theory to analyze their coevolution dynamics. There are two main findings. Firstly, if infected individuals have a large recovery probability, information diffusion plays the expected “normal” role of suppressing the epidemic. However, if the recovery probability is low, information diffusion can anomalously worsen the spread, regardless of the available resources insofar as they are limited. Secondly, different types of resources can lead to distinct phase transitions underlying the epidemic outbreak when the recovery probability is low: with limited cure-focused resources, the phase transition is of the second order, but if resources are of the protection type, the transition becomes first order, and a hysteresis loop emerges. The generality of the findings is established through simulations of synthetic and empirical three-layer networks with results in agreement with the theoretical predictions.

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  • Received 27 July 2020
  • Accepted 4 February 2021

DOI:https://doi.org/10.1103/PhysRevResearch.3.013157

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

NetworksNonlinear DynamicsStatistical Physics & Thermodynamics

Authors & Affiliations

Xiaochen Wang1, Xuzhen Zhu2, Xiaofeng Tao1, Jinghua Xiao3,4, Wei Wang5,*, and Ying-Cheng Lai6

  • 1National Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 2State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 3School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 4State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 5Cybersecurity Research Institute, Sichuan University, Chengdu 610065, China
  • 6School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA

  • *wwzqbx@hotmail.com

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Vol. 3, Iss. 1 — February - April 2021

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