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Cumulative query method for influenza surveillance using search engine data.

Dong-Woo Seo1, Min-Woo Jo, Chang Hwan Sohn

  • 1Asan Medical Center, Department of Emergency Medicine, University of Ulsan, College of Medicine, Seoul, Republic Of Korea.

Journal of Medical Internet Research
|December 18, 2014
PubMed
Summary
This summary is machine-generated.

A new cumulative query method demonstrated a stronger correlation with national influenza surveillance data in South Korea compared to individual search queries. This approach enhances syndromic surveillance by leveraging internet search trends for public health monitoring.

Keywords:
Google Flu TrendsInternet searchinfluenzainfluenza-like illnessquerysyndromic surveillance system

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Area of Science:

  • Public Health Surveillance
  • Epidemiology
  • Data Science

Background:

  • Internet search queries are valuable for syndromic surveillance systems.
  • South Korea currently lacks a syndromic surveillance system utilizing internet search query data.

Purpose of the Study:

  • To evaluate the correlation between a novel cumulative query method and national influenza surveillance data.
  • To assess the efficacy of internet search query data for influenza monitoring in South Korea.

Main Methods:

  • Utilized Daum search engine data (25% market share) and Korea Centers for Disease Control and Prevention (KCDC) influenza-like illness (ILI) data.
  • Developed and validated a cumulative query method based on Pearson's correlation coefficients (r ≥ 0.7) with ILI data.
  • Employed a two-set validation approach using distinct epidemiological years (2009/10-2011/12).

Main Results:

  • In validation set 1, 8 out of 13 cumulative query methods showed higher correlations (r = 0.916-0.943) than the best single query.
  • 11 of 13 cumulative methods achieved r ≥ 0.7, compared to only 4 of 13 single queries.
  • In validation set 2, 8 of 15 cumulative methods outperformed the best single query (r = 0.975-0.987), with all 15 cumulative methods reaching r ≥ 0.7.

Conclusions:

  • The cumulative query method demonstrated a significantly higher correlation with national influenza surveillance data.
  • This method offers a promising advancement for syndromic surveillance in South Korea.
  • Internet search query data, when aggregated, can effectively supplement traditional influenza monitoring.