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The Impact of Imitation on Vaccination Behavior in Social Contact Networks

Overview of attention for article published in PLoS Computational Biology, April 2012
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Title
The Impact of Imitation on Vaccination Behavior in Social Contact Networks
Published in
PLoS Computational Biology, April 2012
DOI 10.1371/journal.pcbi.1002469
Pubmed ID
Authors

Martial L. Ndeffo Mbah, Jingzhou Liu, Chris T. Bauch, Yonas I. Tekel, Jan Medlock, Lauren Ancel Meyers, Alison P. Galvani

Abstract

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.

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The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 3 2%
United States 3 2%
Australia 2 1%
France 1 <1%
Cameroon 1 <1%
China 1 <1%
Brazil 1 <1%
Unknown 139 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 23%
Student > Ph. D. Student 33 22%
Student > Master 19 13%
Student > Bachelor 12 8%
Professor > Associate Professor 9 6%
Other 28 19%
Unknown 15 10%
Readers by discipline Count As %
Medicine and Dentistry 17 11%
Computer Science 17 11%
Agricultural and Biological Sciences 17 11%
Social Sciences 14 9%
Physics and Astronomy 14 9%
Other 44 29%
Unknown 28 19%