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      Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

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          Abstract

          We review behavioural change models (BCMs) for infectious disease transmission in humans. Following the Cochrane collaboration guidelines and the PRISMA statement, our systematic search and selection yielded 178 papers covering the period 2010–2015. We observe an increasing trend in published BCMs, frequently coupled to (re)emergence events, and propose a categorization by distinguishing how information translates into preventive actions. Behaviour is usually captured by introducing information as a dynamic parameter (76/178) or by introducing an economic objective function, either with (26/178) or without (37/178) imitation. Approaches using information thresholds (29/178) and exogenous behaviour formation (16/178) are also popular. We further classify according to disease, prevention measure, transmission model (with 81/178 population, 6/178 metapopulation and 91/178 individual-level models) and the way prevention impacts transmission. We highlight the minority (15%) of studies that use any real-life data for parametrization or validation and note that BCMs increasingly use social media data and generally incorporate multiple sources of information (16/178), multiple types of information (17/178) or both (9/178). We conclude that individual-level models are increasingly used and useful to model behaviour changes. Despite recent advancements, we remain concerned that most models are purely theoretical and lack representative data and a validation process.

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          Modelling the influence of human behaviour on the spread of infectious diseases: a review.

          Human behaviour plays an important role in the spread of infectious diseases, and understanding the influence of behaviour on the spread of diseases can be key to improving control efforts. While behavioural responses to the spread of a disease have often been reported anecdotally, there has been relatively little systematic investigation into how behavioural changes can affect disease dynamics. Mathematical models for the spread of infectious diseases are an important tool for investigating and quantifying such effects, not least because the spread of a disease among humans is not amenable to direct experimental study. Here, we review recent efforts to incorporate human behaviour into disease models, and propose that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, and according to the assumed effects of such behaviour. We highlight recent advances as well as gaps in our understanding of the interplay between infectious disease dynamics and human behaviour, and suggest what kind of data taking efforts would be helpful in filling these gaps.
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            Early Assessment of Anxiety and Behavioral Response to Novel Swine-Origin Influenza A(H1N1)

            Background Since late April, 2009, a novel influenza virus A (H1N1), generally referred to as the “swine flu,” has spread around the globe and infected hundreds of thousands of people. During the first few days after the initial outbreak in Mexico, extensive media coverage together with a high degree of uncertainty about the transmissibility and mortality rate associated with the virus caused widespread concern in the population. The spread of an infectious disease can be strongly influenced by behavioral changes (e.g., social distancing) during the early phase of an epidemic, but data on risk perception and behavioral response to a novel virus is usually collected with a substantial delay or after an epidemic has run its course. Methodology/Principal Findings Here, we report the results from an online survey that gathered data (n = 6,249) about risk perception of the Influenza A(H1N1) outbreak during the first few days of widespread media coverage (April 28 - May 5, 2009). We find that after an initially high level of concern, levels of anxiety waned along with the perception of the virus as an immediate threat. Overall, our data provide evidence that emotional status mediates behavioral response. Intriguingly, principal component analysis revealed strong clustering of anxiety about swine flu, bird flu and terrorism. All three of these threats receive a great deal of media attention and their fundamental uncertainty is likely to generate an inordinate amount of fear vis-a-vis their actual threat. Conclusions/Significance Our results suggest that respondents' behavior varies in predictable ways. Of particular interest, we find that affective variables, such as self-reported anxiety over the epidemic, mediate the likelihood that respondents will engage in protective behavior. Understanding how protective behavior such as social distancing varies and the specific factors that mediate it may help with the design of epidemic control strategies.
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              Opportunities and challenges of Web 2.0 for vaccination decisions.

              A growing number of people use the Internet to obtain health information, including information about vaccines. Websites that allow and promote interaction among users are an increasingly popular source of health information. Users of such so-called Web 2.0 applications (e.g. social media), while still in the minority, represent a growing proportion of online communicators, including vocal and active anti-vaccination groups as well as public health communicators. In this paper, the authors: define Web 2.0 and examine how it may influence vaccination decisions; discuss how anti-vaccination movements use Web 2.0 as well as the challenges Web 2.0 holds for public health communicators; describe the types of information used in these different settings; introduce the theoretical background that can be used to design effective vaccination communication in a Web 2.0 environment; make recommendations for practice and pose open questions for future research. The authors conclude that, as a result of the Internet and Web 2.0, private and public concerns surrounding vaccinations have the potential to virally spread across the globe in a quick, efficient and vivid manner. Web 2.0 may influence vaccination decisions by delivering information that alters the perceived personal risk of vaccine-preventable diseases or vaccination side-effects. It appears useful for public health officials to put effort into increasing the effectiveness of existing communication by implementing interactive, customized communication. A key step to providing successful public health communication is to identify those who are particularly vulnerable to finding and using unreliable and misleading information. Thus, it appears worthwhile that public health websites strive to be easy to find, easy to use, attractive in its presentation and readily provide the information, support and advice that the searcher is looking for. This holds especially when less knowledgeable individuals are in need of reliable information about vaccination risks and benefits. Copyright © 2012 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                J R Soc Interface
                J R Soc Interface
                RSIF
                royinterface
                Journal of the Royal Society Interface
                The Royal Society
                1742-5689
                1742-5662
                December 2016
                December 2016
                : 13
                : 125
                : 20160820
                Affiliations
                [1 ]Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp , Antwerp, Belgium
                [2 ]School of Public Health and Community Medicine, The University of New South Wales , Sydney, New South Wales, Australia
                Author notes

                Electronic supplementary material is available online at https://dx.doi.org/10.6084/m9.figshare.c.3588701.

                Author information
                http://orcid.org/0000-0001-8399-743X
                http://orcid.org/0000-0002-9210-1196
                http://orcid.org/0000-0001-5034-3595
                Article
                rsif20160820
                10.1098/rsif.2016.0820
                5221530
                28003528
                b2b1a45a-51b7-45c4-9743-2029a322cc72
                © 2016 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 10 October 2016
                : 25 November 2016
                Funding
                Funded by: Fonds Wetenschappelijk Onderzoek, http://dx.doi.org/10.13039/501100003130;
                Award ID: G043815N
                Categories
                1004
                22
                24
                44
                Life Sciences–Mathematics interface
                Review Article
                Custom metadata
                December, 2016

                Life sciences
                behaviour,model,infectious disease,vaccination,game theory,individual-based
                Life sciences
                behaviour, model, infectious disease, vaccination, game theory, individual-based

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