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      The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic

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          Abstract

          In recent years, many studies have drawn attention to the important role of collective awareness and human behaviour during epidemic outbreaks. A number of modelling efforts have investigated the interaction between the disease transmission dynamics and human behaviour change mediated by news coverage and by information spreading in the population. Yet, given the scarcity of data on public awareness during an epidemic, few studies have relied on empirical data. Here, we use fine-grained, geo-referenced data from three online sources—Wikipedia, the GDELT Project and the Internet Archive—to quantify population-scale information seeking about the 2016 Zika virus epidemic in the U.S., explicitly linking such behavioural signal to epidemiological data. Geo-localized Wikipedia pageview data reveal that visiting patterns of Zika-related pages in Wikipedia were highly synchronized across the United States and largely explained by exposure to national television broadcast. Contrary to the assumption of some theoretical epidemic models, news volume and Wikipedia visiting patterns were not significantly correlated with the magnitude or the extent of the epidemic. Attention to Zika, in terms of Zika-related Wikipedia pageviews, was high at the beginning of the outbreak, when public health agencies raised an international alert and triggered media coverage, but subsequently exhibited an activity profile that suggests nonlinear dependencies and memory effects in the relation between information seeking, media pressure, and disease dynamics. This calls for a new and more general modelling framework to describe the interaction between media exposure, public awareness and disease dynamics during epidemic outbreaks.

          Author summary

          Despite its importance for public health policy-makers, understanding the impact of media coverage on collective attention during disease outbreaks remains an elusive research task, due to the lack of available data, especially at high spatial granularity. In this paper, we study the dynamics of collective attention received by the 2016 Zika epidemic in the USA and its interplay with the media coverage of the outbreak, at level of US states and cities. We measure the attention to Zika through geo-localized Wikipedia page view data, and we compare it with mentions of Zika in US news outlets and TV shows. We also compare the collective attention received by the outbreak with the incidence of Zika reported by the US Centers for Disease Control and Prevention in each state. We find that the attention dynamics was highly synchronized across states, irrespective of the local risk of transmission of the virus. By building a linear regression model, we show that the dynamics of collective attention is highly predictable, even at state level, only based on the national media coverage received by the outbreak.

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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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            The spread of awareness and its impact on epidemic outbreaks.

            When a disease breaks out in a human population, changes in behavior in response to the outbreak can alter the progression of the infectious agent. In particular, people aware of a disease in their proximity can take measures to reduce their susceptibility. Even if no centralized information is provided about the presence of a disease, such awareness can arise through first-hand observation and word of mouth. To understand the effects this can have on the spread of a disease, we formulate and analyze a mathematical model for the spread of awareness in a host population, and then link this to an epidemiological model by having more informed hosts reduce their susceptibility. We find that, in a well-mixed population, this can result in a lower size of the outbreak, but does not affect the epidemic threshold. If, however, the behavioral response is treated as a local effect arising in the proximity of an outbreak, it can completely stop a disease from spreading, although only if the infection rate is below a threshold. We show that the impact of locally spreading awareness is amplified if the social network of potential infection events and the network over which individuals communicate overlap, especially so if the networks have a high level of clustering. These findings suggest that care needs to be taken both in the interpretation of disease parameters, as well as in the prediction of the fate of future outbreaks.
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              New Introduction to Multiple Time Series Analysis

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Software
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                March 2020
                12 March 2020
                : 16
                : 3
                : e1007633
                Affiliations
                [001] ISI Foundation, Turin, Italy
                The Pennsylvania State University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-7246-2341
                http://orcid.org/0000-0002-3336-0374
                http://orcid.org/0000-0003-1356-3470
                Article
                PCOMPBIOL-D-19-00953
                10.1371/journal.pcbi.1007633
                7067377
                32163409
                3da19e16-075b-4759-8263-505bcb71753e
                © 2020 Tizzoni et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 June 2019
                : 6 January 2020
                Page count
                Figures: 3, Tables: 1, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100007364, Fondazione CRT;
                Award ID: Lagrange Project
                MT, AP, DP and CC acknowledge the support by the Lagrange Project of the ISI Foundation funded by the CRT Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and life sciences
                Organisms
                Viruses
                RNA viruses
                Flaviviruses
                Zika Virus
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Viral Pathogens
                Flaviviruses
                Zika Virus
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Viral Pathogens
                Flaviviruses
                Zika Virus
                Biology and Life Sciences
                Organisms
                Viruses
                Viral Pathogens
                Flaviviruses
                Zika Virus
                Social Sciences
                Sociology
                Communications
                Mass Media
                Encyclopedias
                Online Encyclopedias
                Biology and Life Sciences
                Psychology
                Collective Human Behavior
                Social Sciences
                Psychology
                Collective Human Behavior
                Medicine and Health Sciences
                Epidemiology
                People and places
                Geographical locations
                North America
                United States
                Medicine and Health Sciences
                Epidemiology
                Infectious Disease Epidemiology
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Epidemiology
                Medicine and Health Sciences
                Epidemiology
                Disease Dynamics
                Research and Analysis Methods
                Research Facilities
                Information Centers
                Archives
                Custom metadata
                Data are available from the Zenodo repository ( https://doi.org/10.5281/zenodo.3603916).

                Quantitative & Systems biology
                Quantitative & Systems biology

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