Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems

Lone Simonsen, Julia Gog, Don Olson, Cecile Viboud

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports. We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling.
Original languageEnglish
JournalJournal of Infectious Diseases
Volume214
Issue numbersuppl.4
Pages (from-to)S380–S385
ISSN0022-1899
DOIs
Publication statusPublished - 2016
Externally publishedYes

Keywords

  • Big data
  • Death certifcates
  • Electronic patient records
  • Infectious diseases surveillance
  • Influenza
  • Internet search queries
  • Medical claims
  • Real-time monitoring
  • Syndromic data

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