Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission

Yang Ye, Qingpeng Zhang, Zhongyuan Ruan, Zhidong Cao, Qi Xuan, and Daniel Dajun Zeng
Phys. Rev. E 102, 042314 – Published 30 October 2020
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Abstract

Motivated by the importance of individual differences in risk perception and behavior change in people's responses to infectious disease outbreaks (particularly the ongoing COVID-19 pandemic), we propose a heterogeneous disease-behavior-information transmission model, in which people's risk of getting infected is influenced by information diffusion, behavior change, and disease transmission. We use both a mean-field approximation and Monte Carlo simulations to analyze the dynamics of the model. Information diffusion influences behavior change by allowing people to be aware of the disease and adopt self-protection and subsequently affects disease transmission by changing the actual infection rate. Results show that (a) awareness plays a central role in epidemic prevention, (b) a reasonable fraction of overreacting nodes are needed in epidemic prevention (c) the basic reproduction number R0 has different effects on epidemic outbreak for cases with and without asymptomatic infection, and (d) social influence on behavior change can remarkably decrease the epidemic outbreak size. This research indicates that the media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people become aware of the disease and adopt self-protection to protect themselves and the whole population.

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  • Received 31 May 2020
  • Revised 7 October 2020
  • Accepted 12 October 2020

DOI:https://doi.org/10.1103/PhysRevE.102.042314

©2020 American Physical Society

Physics Subject Headings (PhySH)

Nonlinear DynamicsNetworks

Authors & Affiliations

Yang Ye1, Qingpeng Zhang1,*, Zhongyuan Ruan2, Zhidong Cao3,4,5, Qi Xuan2, and Daniel Dajun Zeng3,4,5

  • 1School of Data Science, City University of Hong Kong, Hong Kong SAR, China
  • 2Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, Zhejiang, China
  • 3State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  • 4School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
  • 5Shenzhen Artificial Intelligence and Data Science Institute, Shenzhen, Guangdong, China

  • *qingpeng.zhang@cityu.edu.hk

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Issue

Vol. 102, Iss. 4 — October 2020

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