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An agent-based model about the effects of fake news on a norovirus outbreak

J Brainard1, P R Hunter1, I R Hall2

  • 1Norwich Medical School, Norwich, United Kingdom.

Revue D'Epidemiologie Et De Sante Publique
|February 11, 2020
PubMed
Abstract

Insights

Combating health misinformation during a norovirus outbreak requires reducing bad advice to 30% or ensuring 30% resistance to it. These strategies can mitigate the negative impacts of false health information.

Area of Science:

  • Epidemiology
  • Computational modeling
  • Health communication

Background:

  • Health misinformation, particularly online, is a persistent concern.
  • The spread of inaccurate health advice can exacerbate public health crises.

Purpose of the Study:

  • To model the impact of health misinformation on a norovirus outbreak.
  • To evaluate strategies for countering the effects of both accurate and inaccurate health advice.

Main Methods:

  • Agent-based modeling was employed to simulate disease and information spread (online and offline).
  • The models incorporated stochastic elements, with repeated runs to establish baseline scenarios and identify factors worsening outbreaks.
  • Strategies to counteract "fake" health news were tested within the model.

Main Results:

  • Reducing health misinformation to 30% of total information was an effective threshold.
  • Achieving at least 30% population resistance to believing and sharing bad health advice also proved effective.
  • These thresholds were identified as crucial for counteracting negative impacts during a norovirus outbreak.

Conclusions:

  • The study identified specific thresholds for health information quality and population resistance to mitigate outbreak severity.
  • Further research is needed to assess the feasibility of implementing these targets in real-world communication networks.
Keywords:
Agent-based-modelsBulles de filtresDésinformationFake newsFilter bubblesModèles à base d’agentsNorovirusOutbreakÉpidémie

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