title: Quantifying the effect of media limitations on outbreak data in a global online web-crawling epidemic intelligence system, 2008-2011.
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.
authors.affiliation: Children'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.
authors.forename: David
authors.initials: D
authors.lastname: Scales
authors.forename: Alexei
authors.initials: A
authors.lastname: Zelenev
authors.forename: John S
authors.initials: JS
authors.lastname: Brownstein
mesh_terms: D002771:Cholera / Q000453:epidemiology; D003141:Communicable Diseases; D016208:Databases, Factual / Q000592:standards*; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D005069:Evaluation Studies as Topic; D005517:Foodborne Diseases / Q000453:epidemiology; D006801:Humans; D053124:Influenza A Virus, H5N1 Subtype; D007251:Influenza, Human / Q000453:epidemiology; D020407:Internet; D008402:Mass Media / Q000639:trends*; D009862:Online Systems / Q000458:organization & administration*; D010561:Personnel Staffing and Scheduling; D011159:Population Surveillance / Q000379:methods
keywords: HealthMap; crowd out effect; epidemic intelligence; infectious diseases; system evaluation