Validating the Use of Google Trends to Enhance Pertussis Surveillance in California

Introduction and Methods: Pertussis has recently re-emerged in the United States. Timely surveillance is vital to estimate the burden of this disease accurately and to guide public health response. However, the surveillance of pertussis is limited by delays in reporting, consolidation and dissemination of data to relevant stakeholders. We fit and assessed a real-time predictive Google model for pertussis in California using weekly incidence data from 2009-2014. Results and Discussion: The linear model was moderately accurate (r = 0.88). Our findings cautiously offer a complementary, real-time signal to enhance pertussis surveillance in California and help to further define the limitations and potential of Google-based epidemic prediction in the rapidly evolving field of digital disease detection.

• Publication year

  2015

• Venue

  PLOS Currents

• Publication date

  2015-10-19

• Fields of study

  Medicine

• Identifiers
  DOI 10.1371/currents.outbreaks.7119696b3e7523faa4543faac87c56c2PMID 26543674PMCID 4626035
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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