title: Vaccination, herd behavior, and herd immunity.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Center for Clinical Quality and Safety, Hadassah-Hebrew University Medical Center, Jerusalem, Israel (MJC, MB).|Division of Internal Medicine Ein Kerem campus, Hadassah-Hebrew University Medical Center, Jerusalem, Israel (MJC)|Department of Clinical Microbiology and Infectious Diseases, Hadassah-Hebrew University Medical Center, Jerusalem, Israel (MJC, CB)|Braun School of Public Health and Community Medicine, Hadassah-Hebrew University Medical Center, Jerusalem, Israel (MJC, DC)
authors.forename: Matan J
authors.initials: MJ
authors.lastname: Cohen
authors.affiliation: Center for Clinical Quality and Safety, Hadassah-Hebrew University Medical Center, Jerusalem, Israel (MJC, MB).
authors.forename: Mayer
authors.initials: M
authors.lastname: Brezis
authors.affiliation: Department of Clinical Microbiology and Infectious Diseases, Hadassah-Hebrew University Medical Center, Jerusalem, Israel (MJC, CB)
authors.forename: Colin
authors.initials: C
authors.lastname: Block
authors.affiliation: Jacobs University, Bremen, Germany (AD)
authors.forename: Adele
authors.initials: A
authors.lastname: Diederich
authors.affiliation: Braun School of Public Health and Community Medicine, Hadassah-Hebrew University Medical Center, Jerusalem, Israel (MJC, DC)
authors.forename: David
authors.initials: D
authors.lastname: Chinitz
mesh_terms: D003663:Decision Trees; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000276:immunology*; D007252:Influenza Vaccines / Q000032:analysis* / Q000276:immunology; D007251:Influenza, Human / Q000276:immunology / Q000517:prevention & control / Q000821:virology; D008962:Models, Theoretical
chemical_list: D007252:Influenza Vaccines
keywords: Monte Carlo methods; decision analysis; expected utility theory; infectious disease; simulation methods; state public health initiatives; vaccination