Literature DB >> 22939311

The impact of personal experiences with infection and vaccination on behaviour-incidence dynamics of seasonal influenza.

C R Wells1, C T Bauch.   

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.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 22939311     DOI: 10.1016/j.epidem.2012.06.002

Source DB:  PubMed          Journal:  Epidemics        ISSN: 1878-0067            Impact factor:   4.396


  13 in total

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Authors:  Osaro Mgbere; Salma Khuwaja
Journal:  Online J Public Health Inform       Date:  2020-05-16

2.  Disease Interventions Can Interfere with One Another through Disease-Behaviour Interactions.

Authors:  Michael A Andrews; Chris T Bauch
Journal:  PLoS Comput Biol       Date:  2015-06-05       Impact factor: 4.475

3.  Bounded rationality alters the dynamics of paediatric immunization acceptance.

Authors:  Tamer Oraby; Chris T Bauch
Journal:  Sci Rep       Date:  2015-06-02       Impact factor: 4.379

Review 4.  Behavioural change models for infectious disease transmission: a systematic review (2010-2015).

Authors:  Frederik Verelst; Lander Willem; Philippe Beutels
Journal:  J R Soc Interface       Date:  2016-12       Impact factor: 4.118

5.  Complex social contagion makes networks more vulnerable to disease outbreaks.

Authors:  Ellsworth Campbell; Marcel Salathé
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

6.  Policy resistance undermines superspreader vaccination strategies for influenza.

Authors:  Chad R Wells; Eili Y Klein; Chris T Bauch
Journal:  PLoS Comput Biol       Date:  2013-03-07       Impact factor: 4.475

7.  Behavioral responses to epidemics in an online experiment: using virtual diseases to study human behavior.

Authors:  Frederick Chen; Amanda Griffith; Allin Cottrell; Yue-Ling Wong
Journal:  PLoS One       Date:  2013-01-09       Impact factor: 3.240

8.  Pandemic-related health behavior: repeat episodes of influenza-like illness related to the 2009 H1N1 influenza pandemic.

Authors:  O Mgbere; K Ngo; S Khuwaja; M Mouzoon; A Greisinger; R Arafat; J Markee
Journal:  Epidemiol Infect       Date:  2017-07-20       Impact factor: 4.434

9.  Network information analysis reveals risk perception transmission in a behaviour-influenza dynamics system.

Authors:  C-M Liao; S-H You; Y-H Cheng
Journal:  Epidemiol Infect       Date:  2014-03-20       Impact factor: 4.434

10.  Beyond Rational Decision-Making: Modelling the Influence of Cognitive Biases on the Dynamics of Vaccination Coverage.

Authors:  Marina Voinson; Sylvain Billiard; Alexandra Alvergne
Journal:  PLoS One       Date:  2015-11-23       Impact factor: 3.240

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