Quantify the role of superspreaders -opinion leaders- on COVID-19 information propagation in the Chinese Sina-microblog

PLoS One. 2020 Jun 8;15(6):e0234023. doi: 10.1371/journal.pone.0234023. eCollection 2020.

Abstract

Backgroud: Effective communication of accurate information through social media constitutes an important component of public health interventions in modern time, when traditional public health approaches such as contact tracing, quarantine and isolation are among the few options for the containing the disease spread in the population. The success of control of COVID-19 outbreak started from Wuhan, the capital city of Hubei Province of China relies heavily on the resilience of residents to follow public health interventions which induce substantial interruption of social-economic activities, and evidence shows that opinion leaders have been playing significant roles in the propagation of epidemic information and public health policy and implementations.

Methods: We design a mathematical model to quantify the roles of information superspreaders in single specific information which outbreaks rapidly and usually has a short duration period, and to examine the information propagation dynamics in the Chinese Sina-microblog. Our opinion-leader susceptible-forwarding-immune (OL-SFI) model is formulated to track the temporal evolution of forwarding quantities generated by opinion leaders and normal users.

Results: Data fitting from the real data of COVID-19 obtained from Chinese Sina-microblog can identify the different contact rates and forwarding probabilities (and hence calculate the basic information forwarding reproduction number of superspreaders), and can be used to evaluate the roles of opinion leaders in different stages of the information propagation and the outbreak unfolding.

Conclusions: The parameterized model can be used to nearcast the information propagation trend, and the model-based sensitivity analysis can help to explore important factors for the roles of opinion leaders.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Betacoronavirus / physiology
  • Blogging*
  • COVID-19
  • China / epidemiology
  • Coronavirus Infections / epidemiology*
  • Disease Outbreaks
  • Health Communication*
  • Humans
  • Models, Theoretical*
  • Pandemics
  • Pneumonia, Viral / epidemiology*
  • Public Health*
  • SARS-CoV-2
  • Social Media*

Grants and funding

The authors acknowledge the National Natural Science Foundation of China (Grant numbers: 61801440), the Natural Science and Engineering Research Council of Canada, the Canada Research Chair Program (JWu), the Fundamental Research Funds for the Central Universities and the High-quality and Cutting-edge Disciplines Construction Project for Universities in Beijing (Internet Information, Communication University of China); grateful to all study participants for their contribution to this research and also thank the tireless efforts of our data collectors: Jiale Wu, Meiqi Ji and Xueying Shao and Hongyu Pang. These fundings are used to pay for the data collection and experiments in this paper.