From Cyber Space Opinion Leaders and the Diffusion of Anti-vaccine Extremism to Physical Space Disease Outbreaks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

Measles is one of the leading causes of death among young children. In many developed countries with high measles, mumps, and rubella (MMR) vaccine coverage, measles outbreaks still happen each year. Previous research has demonstrated that what underlies the paradox of high vaccination coverage and measles outbreaks is the ineffectiveness of “herd immunity”, which has the false assumption that people are mixing randomly and there’s equal distribution of vaccinated population. In reality, the unvaccinated population is often clustered instead of not equally distributed. Meanwhile, the Internet has been one of the dominant information sources to gain vaccination knowledge and thus has also been the locus of the “anti-vaccine movement”. In this paper, we propose an agent-based model that explores sentiment diffusion and how this process creates anti-vaccination opinion clusters that leads to larger scale disease outbreaks. The model separates cyber space (where information diffuses) and physical space (where both information diffuses and diseases transmit). The results show that cyber space anti-vaccine opinion leaders have such an influence on anti-vaccine sentiments diffusion in the information network that even if the model starts with the majority of the population being pro-vaccine, the degree of disease outbreaks increases significantly.

Keywords

Agent-based modeling Information networks Infectious disease transmission 

References

  1. Betsch, C., Renkewitz, F., Betsch, T., Ulshöfer, C.: The influence of vaccine-critical websites on perceiving vaccination risks. J Health Psychol. 15, 446–455 (2010)CrossRefGoogle Scholar
  2. Croitoru, A., Wayant, N., Crooks, A., Radzikowski, J., Stefanidis, A.: Linking cyber and physical spaces through community detection and clustering in social media feeds. Comput. Environ. Urban Syst. 53, 47–64 (2015). doi:10.1016/j.compenvurbsys.2014.11.002 CrossRefGoogle Scholar
  3. DiResta, R., Lotan, G.: Anti-Vaxxers are using Twitter to manipulate a vaccine bill. https://www.wired.com/2015/06/antivaxxers-influencing-legislation/
  4. Fox, J.P.: Herd immunity and measles. Clin. Infect. Dis. 5, 463–466 (1983)CrossRefGoogle Scholar
  5. Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., Railsback, S.F.: The ODD protocol: a review and first update. Ecol. Model. 221, 2760–2768 (2010)CrossRefGoogle Scholar
  6. Kata, A.: Anti-vaccine activists, Web 2.0, and the postmodern paradigm – an overview of tactics and tropes used online by the anti-vaccination movement. Vaccine 30, 3778–3789 (2012)CrossRefGoogle Scholar
  7. Lieu, T.A., Ray, G.T., Klein, N.P., Chung, C., Kulldorff, M.: Geographic clusters in underimmunization and vaccine refusal. Pediatrics 135, 280–289 (2015)CrossRefGoogle Scholar
  8. May, T., Silverman, R.D.: “Clustering of exemptions” as a collective action threat to herd immunity. Vaccine 21, 1048–1051 (2003)CrossRefGoogle Scholar
  9. Salathé, M., Bonhoeffer, S.: The effect of opinion clustering on disease outbreaks. J. R. Soc. Interface 5, 1505–1508 (2008)CrossRefGoogle Scholar
  10. Shalizi, C.R., Thomas, A.C.: Homophily and contagion are generically confounded in observational social network studies. Sociol. Methods Res. 40, 211–239 (2011)MathSciNetCrossRefGoogle Scholar
  11. Tinati, R., Carr, L., Hall, W., Bentwood, J.: Scale free: Twitter’s retweet network structure (2012)Google Scholar
  12. Van Eck, P.S., Jager, W., Leeflang, P.S.H.: Opinion leaders’ role in innovation diffusion: a simulation study. J. Prod. Innov. Manag. 28, 187–203 (2011)CrossRefGoogle Scholar
  13. Wang, E., Clymer, J., Davis-Hayes, C., Buttenheim, A.: Nonmedical exemptions from school immunization requirements: a systematic review. Am. J. Public Health 104, e62–e84 (2014)CrossRefGoogle Scholar
  14. Witteman, H.O., Zikmund-Fisher, B.J.: The defining characteristics of Web 2.0 and their potential influence in the online vaccination debate. Vaccine 30, 3734–3740 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computational Social Science ProgramGeorge Mason UniversityFairfaxUSA

Personalised recommendations