8 research outputs found

    The Impact of Imitation on Vaccination Behavior in Social Contact Networks

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    Martial L. Ndeffo Mbah is with Yale University School of Medicine, Jingzhou Liu is with Yale University School of Medicine, Chris T. Bauch is with University of Guelph, Yonas I. Tekel is with Yale University School of Medicine, Jan Medlock is with Clemson University and Oregon State University, Lauren Ancel Meyers is with UT Austin and the Santa Fe Institute, Alison P. Galvani is with Yale University School of Medicine.Previous game-theoretic studies of vaccination behavior typically have often assumed that populations are homogeneously mixed and that individuals are fully rational. In reality, there is heterogeneity in the number of contacts per individual, and individuals tend to imitate others who appear to have adopted successful strategies. Here, we use network-based mathematical models to study the effects of both imitation behavior and contact heterogeneity on vaccination coverage and disease dynamics. We integrate contact network epidemiological models with a framework for decision-making, within which individuals make their decisions either based purely on payoff maximization or by imitating the vaccination behavior of a social contact. Simulations suggest that when the cost of vaccination is high imitation behavior may decrease vaccination coverage. However, when the cost of vaccination is small relative to that of infection, imitation behavior increases vaccination coverage, but, surprisingly, also increases the magnitude of epidemics through the clustering of non-vaccinators within the network. Thus, imitation behavior may impede the eradication of infectious diseases. Calculations that ignore behavioral clustering caused by imitation may significantly underestimate the levels of vaccination coverage required to attain herd immunity.This study was funded by the National Institute of General Medical Sciences MIDAS grant U01GM087719. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Biological Sciences, School o

    Vaccination coverage under strong responsiveness to differences of payoff (α = 15) in a (A) homogenous Poisson network, (B) urban network, and (C) exponentially-scaled power law network.

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    <p>Vaccine coverage is given as a function of <i>r</i> for the two extreme cases: fully payoff maximization (Θ = 0) and fully imitation (Θ = 1). Parameters are identical to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002469#pcbi-1002469-t001" target="_blank">Table 1</a>, except α = 15.</p

    Average number of contacts between non-vaccinators as a function of the fraction of imitators (Θ) and the relative cost of vaccination (<i>r</i>) in a (A) homogenous Poisson network, (B) urban network, and (C) exponentially-scaled power law network.

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    <p>Average number of contacts between non-vaccinators as a function of the fraction of imitators (Θ) and the relative cost of vaccination (<i>r</i>) in a (A) homogenous Poisson network, (B) urban network, and (C) exponentially-scaled power law network.</p

    Summary of the parameters used in simulations.

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    <p>*For each network structure, transmission probability was chosen so as to ensure that the average final size of the epidemic is approximately equal to 90% of the total population. For Poisson network  = 0.05, urban network  = 0.06, and exponential-scaled power law network  = 0.52.</p><p>**The perceived probability a random contact is infected per epidemic was chosen to be constant, with value varying between 50–90%. For each value of , was chosen such that .</p

    Degree distributions.

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    <p>The proportion of the population with each given degree are different for a Poisson network (red histogram), urban network (blue histogram), and exponentially-scaled power law network (pink histogram).</p
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