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Published on: April 6, 2019
Quantifying the effect of media limitations on outbreak data in a global online web-crawling epidemic intelligence
David Scales1, Alexei Zelenev, John S Brownstein
1Children's Hospital Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Center for Biomedical Informatics, Boston Children's Hospital, Harvard University, Boston, MA, USA; david.scales@aya.yale.edu.
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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