Quantifying the effect of media limitations on outbreak data in a global online web-crawling epidemic intelligence system, 2008-2011.

Emerging health threats journal Pub Date : 2013-11-08 DOI:10.3402/ehtj.v6i0.21621

David Scales, Alexei Zelenev, John S Brownstein

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引用次数: 13

Abstract

Background: This is the first study quantitatively evaluating the effect that media-related limitations have on data from an automated epidemic intelligence system.

Methods: We modeled time series of HealthMap's two main data feeds, Google News and Moreover, to test for evidence of two potential limitations: first, human resources constraints, and second, high-profile outbreaks "crowding out" coverage of other infectious diseases.

Results: Google News events declined by 58.3%, 65.9%, and 14.7% on Saturday, Sunday and Monday, respectively, relative to other weekdays. Events were reduced by 27.4% during Christmas/New Years weeks and 33.6% lower during American Thanksgiving week than during an average week for Google News. Moreover data yielded similar results with the addition of Memorial Day (US) being associated with a 36.2% reduction in events. Other holiday effects were not statistically significant. We found evidence for a crowd out phenomenon for influenza/H1N1, where a 50% increase in influenza events corresponded with a 4% decline in other disease events for Google News only. Other prominent diseases in this database - avian influenza (H5N1), cholera, or foodborne illness - were not associated with a crowd out phenomenon.

Conclusions: These results provide quantitative evidence for the limited impact of editorial biases on HealthMap's web-crawling epidemic intelligence.

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量化2008-2011年全球在线网络爬行流行病情报系统中媒体限制对疫情数据的影响。

背景:这是第一个定量评估媒体相关限制对自动流行病情报系统数据影响的研究。方法:我们对HealthMap的两个主要数据源(Google News和Moreover)的时间序列进行建模，以检验两个潜在限制的证据:第一，人力资源限制;第二，高调的疫情“挤占”了其他传染病的报道。结果:谷歌新闻事件在周六、周日和周一相对于其他工作日分别下降了58.3%、65.9%和14.7%。与谷歌新闻的平均一周相比，圣诞节/新年周的事件减少了27.4%，美国感恩节周的事件减少了33.6%。此外，数据也得出了类似的结果，增加阵亡将士纪念日(美国)与事件减少36.2%有关。其他节日效应在统计上并不显著。我们发现了流感/H1N1出现挤出现象的证据，仅在谷歌新闻中，流感事件增加50%对应于其他疾病事件减少4%。该数据库中的其他突出疾病——禽流感(H5N1)、霍乱或食源性疾病——与排挤现象无关。结论:这些结果为编辑偏见对HealthMap的网络爬行流行病情报的有限影响提供了定量证据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Emerging health threats journal

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