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Vaccination, Herd Behavior, and Herd Immunity

Author

Listed:
  • Matan J. Cohen
  • Mayer Brezis
  • Colin Block
  • Adele Diederich
  • David Chinitz

Abstract

Background: During the 2009 outbreak of novel influenza AH1N1, insufficient data were available to adequately inform decision makers about benefits and risks of vaccination and disease. We hypothesized that individuals would opt to mimic their peers, having no better decision anchor. We used Game Theory, decision analysis, and transmission models to simulate the impact of subjective risks and preference estimates on vaccination behavior. Methods: We asked 95 students to provide estimates of risk and health state valuations with regard to AH1N1 infection, complications, and expectations of vaccine benefits and risks. These estimates were included in a sequential chain of models: a dynamic epidemic model, a decision tree, and a population-level model. Additionally, participants’ intentions to vaccinate or not at varying vaccination rates were documented. Results: The model showed that at low vaccination rates, vaccination dominated. When vaccination rates increased above 78%, nonvaccination was the dominant strategy. We found that vaccination intentions did not correspond to the shift in strategy dominance and segregated to 3 types of intentions: regardless of what others do 29/95 (31%) intended to vaccinate while 27/95 (28%) did not; among 39 of 95 (41%) intention was positively associated with putative vaccination rates. Conclusions: Some people conform to the majority’s choice, either shifting epidemic dynamics toward herd immunity or, conversely, limiting societal goals. Policy leaders should use models carefully, noting their limitations and theoretical assumptions. Behavior drivers were not explicitly explored in this study, and the discrepant results beg further investigation. Models including real subjective perceptions with empiric or subjective probabilities can provide insight into deviations from expected rational behavior and suggest interventions in order to provide better population outcomes.

Suggested Citation

  • Matan J. Cohen & Mayer Brezis & Colin Block & Adele Diederich & David Chinitz, 2013. "Vaccination, Herd Behavior, and Herd Immunity," Medical Decision Making, , vol. 33(8), pages 1026-1038, November.
  • Handle: RePEc:sae:medema:v:33:y:2013:i:8:p:1026-1038
    DOI: 10.1177/0272989X13487946
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    References listed on IDEAS

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    1. Geoffard, Pierre-Yves & Philipson, Tomas, 1997. "Disease Eradication: Private versus Public Vaccination," American Economic Review, American Economic Association, vol. 87(1), pages 222-230, March.
    2. Hershey, John C. & Asch, David A. & Thumasathit, Thi & Meszaros, Jacqueline & Waters, Victor V., 1994. "The Roles of Altruism, Free Riding, and Bandwagoning in Vaccination Decisions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 59(2), pages 177-187, August.
    3. Dunia López-Pintado & Duncan J. Watts, 2008. "Social Influence, Binary Decisions and Collective Dynamics," Rationality and Society, , vol. 20(4), pages 399-443, November.
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    2. Nicolò Gozzi & Daniela Perrotta & Daniela Paolotti & Nicola Perra, 2020. "Towards a data-driven characterization of behavioral changes induced by the seasonal flu," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-19, May.
    3. Kairiza, Terrence & Kembo, George & Chigusiwa, Lloyd, 2023. "Herding behavior in COVID-19 vaccine hesitancy in rural Zimbabwe: The moderating role of health information under heterogeneous household risk perceptions," Social Science & Medicine, Elsevier, vol. 323(C).
    4. Giorgio Gronchi & Elena Parilina & Alessandro Tampieri, 2021. "Plenty of Fish in the Sea: Divorce Choice and the Quality of Singles," Mathematics, MDPI, vol. 9(23), pages 1-33, November.

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