Persistence of Anti-vaccine Sentiment in Social Networks Through Strategic Interactions

Authors

  • A S M Ahsan-Ul Haque Biocomplexity Institute, University of Virginia Department of Computer Science, University of Virginia
  • Mugdha Thakur Biocomplexity Institute, University of Virginia
  • Matthew Bielskas Biocomplexity Institute, University of Virginia Department of Computer Science, University of Virginia
  • Achla Marathe Biocomplexity Institute, University of Virginia Department of Public Health Sciences, University of Virginia
  • Anil Vullikanti Biocomplexity Institute, University of Virginia Department of Computer Science, University of Virginia

DOI:

https://doi.org/10.1609/aaai.v35i6.16613

Keywords:

AI Responses to the COVID-19 Pandemic (Covid19), Games, Healthcare, Medicine & Wellness, Behavioral Game Theory

Abstract

Vaccination is the primary intervention for controlling the spread of infectious diseases. A certain level of vaccination rate (referred to as "herd immunity'') is needed for this intervention to be effective. However, there are concerns that herd immunity might not be achieved due to an increasing level of hesitancy and opposition to vaccines. One of the primary reasons for this is the cost of non-conformance with one's peers. We use the framework of network coordination games to study the persistence of anti-vaccine sentiment in a population. We extend it to incorporate the opposing forces of the pressure of conforming to peers, herd-immunity and vaccination benefits. We study the structure of the equilibria in such games, and the characteristics of unvaccinated nodes. We also study Stackelberg strategies to reduce the number of nodes with anti-vaccine sentiment. Finally, we evaluate our results on different kinds of real world social networks.

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Published

2021-05-18

How to Cite

Haque, A. S. M. A.-U., Thakur, M., Bielskas, M., Marathe, A., & Vullikanti, A. (2021). Persistence of Anti-vaccine Sentiment in Social Networks Through Strategic Interactions. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 4812-4820. https://doi.org/10.1609/aaai.v35i6.16613

Issue

Section

AAAI Technical Track Focus Area on AI Responses to the COVID-19 Pandemic