Quantifying the effect of media limitations on outbreak data in a global online web-crawling epidemic intelligence system, 2008–2011
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CitationScales, David, Alexei Zelenev, and John S. Brownstein. 2013. “Quantifying the effect of media limitations on outbreak data in a global online web-crawling epidemic intelligence system, 2008–2011.” Emerging Health Threats Journal 6 (1): 10.3402/ehtj.v6i0.21621. doi:10.3402/ehtj.v6i0.21621. http://dx.doi.org/10.3402/ehtj.v6i0.21621.
AbstractBackground: 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11879073
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