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公众对意大利基孔肯雅热疫情的反应——基于广泛新型数据流的结构方程建模分析的见解。

Public reaction to Chikungunya outbreaks in Italy-Insights from an extensive novel data streams-based structural equation modeling analysis.

机构信息

Department of Medicine 'B', Sheba Medical Center, Tel-Hashomer, Israel.

Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.

出版信息

PLoS One. 2018 May 24;13(5):e0197337. doi: 10.1371/journal.pone.0197337. eCollection 2018.

DOI:10.1371/journal.pone.0197337
PMID:29795578
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5968406/
Abstract

The recent outbreak of Chikungunya virus in Italy represents a serious public health concern, which is attracting media coverage and generating public interest in terms of Internet searches and social media interactions. Here, we sought to assess the Chikungunya-related digital behavior and the interplay between epidemiological figures and novel data streams traffic. Reaction to the recent outbreak was analyzed in terms of Google Trends, Google News and Twitter traffic, Wikipedia visits and edits, and PubMed articles, exploiting structural modelling equations. A total of 233,678 page-views and 150 edits on the Italian Wikipedia page, 3,702 tweets, 149 scholarly articles, and 3,073 news articles were retrieved. The relationship between overall Chikungunya cases, as well as autochthonous cases, and tweets production was found to be fully mediated by Chikungunya-related web searches. However, in the allochthonous/imported cases model, tweet production was not found to be significantly mediated by epidemiological figures, with web searches still significantly mediating tweet production. Inconsistent relationships were detected in mediation models involving Wikipedia usage as a mediator variable. Similarly, the effect between news consumption and tweets production was suppressed by the Wikipedia usage. A further inconsistent mediation was found in the case of the effect between Wikipedia usage and tweets production, with web searches as a mediator variable. When adjusting for the Internet penetration index, similar findings could be obtained, with the important exception that in the adjusted model the relationship between GN and Twitter was found to be partially mediated by Wikipedia usage. Furthermore, the link between Wikipedia usage and PubMed/MEDLINE was fully mediated by GN, differently from what was found in the unadjusted model. In conclusion-a significant public reaction to the current Chikungunya outbreak was documented. Health authorities should be aware of this, recognizing the role of new technologies for collecting public concerns and replying to them, disseminating awareness and avoid misleading information.

摘要

意大利近期暴发的基孔肯雅热病毒引起了严重的公共卫生关注,这引发了媒体的报道,并在互联网搜索和社交媒体互动方面引起了公众的兴趣。在这里,我们试图评估与基孔肯雅热相关的数字行为,以及流行病学数据和新型数据流流量之间的相互作用。使用结构建模方程,分析了谷歌趋势、谷歌新闻和推特流量、维基百科访问和编辑以及 PubMed 文章对最近暴发的反应。共检索到 233678 次页面浏览量和意大利语维基百科页面上的 150 次编辑、3702 条推文、149 篇学术文章和 3073 篇新闻文章。发现总体基孔肯雅热病例以及本地病例与推文生成之间的关系完全由基孔肯雅热相关网络搜索介导。然而,在输入病例/输入病例模型中,推文生成并未发现由流行病学数据显著介导,网络搜索仍然显著介导推文生成。在涉及维基百科使用作为中介变量的中介模型中检测到不一致的关系。同样,新闻消费和推文生成之间的关系在维基百科使用的抑制下被削弱。在维基百科使用作为中介变量的情况下,还发现了另一个不一致的中介作用。当调整互联网渗透率指数时,可以得到类似的发现,但有一个重要的例外,即在调整后的模型中,GN 和推特之间的关系被发现部分由维基百科使用介导。此外,维基百科使用和 PubMed/MEDLINE 之间的联系完全由 GN 介导,与未调整模型中的发现不同。总之,记录到了公众对当前基孔肯雅热暴发的强烈反应。卫生当局应该意识到这一点,认识到新技术在收集公众关注和回应公众、传播意识和避免误导性信息方面的作用。

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