TY - JOUR
T1 - Vaccination, herd behavior, and herd immunity
AU - Cohen, Matan J.
AU - Brezis, Mayer
AU - Block, Colin
AU - Diederich, Adele
AU - Chinitz, David
PY - 2013/11
Y1 - 2013/11
N2 - 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.
AB - 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.
KW - decision analysis
KW - expected utility theory
KW - infectious disease
KW - Monte Carlo methods
KW - simulation methods
KW - state public health initiatives
KW - vaccination
UR - http://www.scopus.com/inward/record.url?scp=84885988652&partnerID=8YFLogxK
U2 - 10.1177/0272989X13487946
DO - 10.1177/0272989X13487946
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C2 - 23695369
AN - SCOPUS:84885988652
SN - 0272-989X
VL - 33
SP - 1026
EP - 1038
JO - Medical Decision Making
JF - Medical Decision Making
IS - 8
ER -