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Erratic Flu Vaccination Emerges from Short-Sighted Behavior in Contact Networks

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  • Daniel M Cornforth
  • Timothy C Reluga
  • Eunha Shim
  • Chris T Bauch
  • Alison P Galvani
  • Lauren Ancel Meyers

Abstract

The effectiveness of seasonal influenza vaccination programs depends on individual-level compliance. Perceptions about risks associated with infection and vaccination can strongly influence vaccination decisions and thus the ultimate course of an epidemic. Here we investigate the interplay between contact patterns, influenza-related behavior, and disease dynamics by incorporating game theory into network models. When individuals make decisions based on past epidemics, we find that individuals with many contacts vaccinate, whereas individuals with few contacts do not. However, the threshold number of contacts above which to vaccinate is highly dependent on the overall network structure of the population and has the potential to oscillate more wildly than has been observed empirically. When we increase the number of prior seasons that individuals recall when making vaccination decisions, behavior and thus disease dynamics become less variable. For some networks, we also find that higher flu transmission rates may, counterintuitively, lead to lower (vaccine-mediated) disease prevalence. Our work demonstrates that rich and complex dynamics can result from the interaction between infectious diseases, human contact patterns, and behavior. Author Summary: When influenza spreads through a human population, its dynamics are shaped by both the complex patterns of contact that arise through our daily activities and individual decisions about the prevention and treatment of flu infections. However, until recently, mathematical models of flu transmission have ignored complex interaction and behavioral patterns in order to facilitate mathematical analyses. Here, we combine two recent approaches to modeling flu–network theory and game theory–to address the interplay between contact patterns and host vaccination decisions during seasonal flu outbreaks. Intuitively, the more contacts one has, the more likely he or she is to vaccinate. However, under the assumption that people make rational decisions based on complete information about the prior seasonal epidemic, vaccination decisions are predicted to vacillate dramatically. A severe epidemic in one year inspires high vaccination rates in the following year; this causes a milder epidemic which then leads to lower vaccination rates in the following year; and the cycle begins anew. We find further that the more homogeneous the contact patterns, the more pronounced the vacillations will be, and that decision-making based on multiple past seasons (rather than just one) leads to much more consistent behavior.

Suggested Citation

  • Daniel M Cornforth & Timothy C Reluga & Eunha Shim & Chris T Bauch & Alison P Galvani & Lauren Ancel Meyers, 2011. "Erratic Flu Vaccination Emerges from Short-Sighted Behavior in Contact Networks," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-10, January.
  • Handle: RePEc:plo:pcbi00:1001062
    DOI: 10.1371/journal.pcbi.1001062
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    References listed on IDEAS

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    1. Gretchen B. Chapman & Elliot J. Coups, 1999. "Time Preferences and Preventive Health Behavior," Medical Decision Making, , vol. 19(3), pages 307-314, August.
    2. Tomas Philipson, 1996. "Private Vaccination and Public Health: An Empirical Examination for U.S. Measles," Journal of Human Resources, University of Wisconsin Press, vol. 31(3), pages 611-630.
    3. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
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    Cited by:

    1. Tang, Guo-Mei & Cai, Chao-Ran & Wu, Zhi-Xi, 2017. "Evolutionary vaccination dynamics with internal support mechanisms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 135-143.
    2. Romulus Breban, 2011. "Health Newscasts for Increasing Influenza Vaccination Coverage: An Inductive Reasoning Game Approach," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-10, December.
    3. Dong, Chao & Yin, Qiuju & Liu, Wenyang & Yan, Zhijun & Shi, Tianyu, 2015. "Can rewiring strategy control the epidemic spreading?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 169-177.
    4. Zhang, Hai-Feng & Shu, Pan-Pan & Wang, Zhen & Tang, Ming & Small, Michael, 2017. "Preferential imitation can invalidate targeted subsidy policies on seasonal-influenza diseases," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 332-342.
    5. Li, Qiu & Li, MingChu & Lv, Lin & Guo, Cheng & Lu, Kun, 2017. "A new prediction model of infectious diseases with vaccination strategies based on evolutionary game theory," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 51-60.
    6. Shams, Bita & Khansari, Mohammad, 2015. "On the impact of epidemic severity on network immunization algorithms," Theoretical Population Biology, Elsevier, vol. 106(C), pages 83-93.
    7. Shi, Benyun & Liu, Guangliang & Qiu, Hongjun & Wang, Zhen & Ren, Yizhi & Chen, Dan, 2019. "Exploring voluntary vaccination with bounded rationality through reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 171-182.

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