{"title":"Quantifying the effect of media limitations on outbreak data in a global online web-crawling epidemic intelligence system, 2008-2011.","authors":"David Scales, Alexei Zelenev, John S Brownstein","doi":"10.3402/ehtj.v6i0.21621","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This is the first study quantitatively evaluating the effect that media-related limitations have on data from an automated epidemic intelligence system.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>These results provide quantitative evidence for the limited impact of editorial biases on HealthMap's web-crawling epidemic intelligence.</p>","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3402/ehtj.v6i0.21621","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Emerging health threats journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3402/ehtj.v6i0.21621","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
