Data-driven surveillance: effective collection, integration, and interpretation of data to support decision making

Fernanda C. Dórea, Crawford W. Revie

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Abstract

The biggest change brought about by the "era of big data" to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex "variety" dimension of data
in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.
Original languageEnglish
Article number633977
Number of pages8
JournalFrontiers in Veterinary Science
Volume8
DOIs
Publication statusPublished - 12 Mar 2021

Keywords

  • epidemiology
  • machine learning
  • big data
  • data analyses
  • linked data

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