摘要：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.Keywords: epidemic intelligence; infectious diseases; system evaluation; HealthMap; crowd out effect(Published: 8 November 2013)Citation: Emerg Health Threats J 2013, 6: 21621 - http://dx.doi.org/10.3402/ehtj.v6i0.21621
关键词：Epidemiology; Surveillance and Reporting; Infectious Disease;epidemic intelligence; infectious diseases; system evaluation; HealthMap; crowd out effect;RA648.5-767
