title: The impact of personal experiences with infection and vaccination on behaviour-incidence dynamics of seasonal influenza.
abstract: Personal experiences with past infection events, or perceived vaccine failures and complications, are known to drive vaccine uptake. We coupled a model of individual vaccinating decisions, influenced by these drivers, with a contact network model of influenza transmission dynamics. The impact of non-influenzal influenza-like illness (niILI) on decision-making was also incorporated: it was possible for individuals to mistake niILI for true influenza. Our objectives were to (1) evaluate the impact of personal experiences on vaccine coverage; (2) understand the impact of niILI on behaviour-incidence dynamics; (3) determine which factors influence vaccine coverage stability; and (4) determine whether vaccination strategies can become correlated on the network in the absence of social influence. We found that certain aspects of personal experience can significantly impact behaviour-incidence dynamics. For instance, longer term memory for past events had a strong stabilising effect on vaccine coverage dynamics, although it could either increase or decrease average vaccine coverage depending on whether memory of past infections or past vaccine failures dominated. When vaccine immunity wanes slowly, vaccine coverage is low and stable, and infection incidence is also very low, unless the effects of niILI are ignored. Strategy correlations can occur in the absence of imitation, on account of the neighbour-neighbour transmission of infection and history-dependent decision making. Finally, niILI weakens the behaviour-incidence coupling and therefore tends to stabilise dynamics, as well as breaking up strategy correlations. Behavioural feedbacks, and the quality of self-diagnosis of niILI, may need to be considered in future programs adopting "universal" flu vaccines conferring long-term immunity. Public health interventions that focus on reminding individuals about their previous influenza infections, as well as communicating facts about vaccine efficacy and the difference between influenza and niILI, may be an effective way to increase vaccine coverage and prevent unexpected drops in coverage.
authors.affiliation: Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada. cwells01@uoguelph.ca
authors.forename: C R
authors.initials: CR
authors.lastname: Wells
authors.forename: C T
authors.initials: CT
authors.lastname: Bauch
mesh_terms: D003198:Computer Simulation; D003657:Decision Making; D004196:Disease Outbreaks / Q000517:prevention & control; D018562:Disease Transmission, Infectious / Q000517:prevention & control; D005716:Game Theory; D015438:Health Behavior; D006801:Humans; D015994:Incidence; D007252:Influenza Vaccines / Q000008:administration & dosage*; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control* / Q000523:psychology*; D012931:Social Environment
chemical_list: D007252:Influenza Vaccines