title: Cumulative query method for influenza surveillance using search engine data.
abstract: BACKGROUND
  Internet search queries have become an important data source in syndromic surveillance system. However, there is currently no syndromic surveillance system using Internet search query data in South Korea.
  
  
  OBJECTIVE
  The objective of this study was to examine correlations between our cumulative query method and national influenza surveillance data.
  
  
  METHODS
  Our study was based on the local search engine, Daum (approximately 25% market share), and influenza-like illness (ILI) data from the Korea Centers for Disease Control and Prevention. A quota sampling survey was conducted with 200 participants to obtain popular queries. We divided the study period into two sets: Set 1 (the 2009/10 epidemiological year for development set 1 and 2010/11 for validation set 1) and Set 2 (2010/11 for development Set 2 and 2011/12 for validation Set 2). Pearson's correlation coefficients were calculated between the Daum data and the ILI data for the development set. We selected the combined queries for which the correlation coefficients were .7 or higher and listed them in descending order. Then, we created a cumulative query method n representing the number of cumulative combined queries in descending order of the correlation coefficient.
  
  
  RESULTS
  In validation set 1, 13 cumulative query methods were applied, and 8 had higher correlation coefficients (min=.916, max=.943) than that of the highest single combined query. Further, 11 of 13 cumulative query methods had an r value of ≥.7, but 4 of 13 combined queries had an r value of ≥.7. In validation set 2, 8 of 15 cumulative query methods showed higher correlation coefficients (min=.975, max=.987) than that of the highest single combined query. All 15 cumulative query methods had an r value of ≥.7, but 6 of 15 combined queries had an r value of ≥.7.
  
  
  CONCLUSIONS
  Cumulative query method showed relatively higher correlation with national influenza surveillance data than combined queries in the development and validation set.
authors.affiliation: Asan Medical Center, Department of Emergency Medicine, University of Ulsan, College of Medicine, Seoul, Republic Of Korea.
authors.forename: Dong-Woo
authors.identifier: http://orcid.org/0000-0001-8104-0247
authors.initials: DW
authors.lastname: Seo
authors.forename: Min-Woo
authors.identifier: http://orcid.org/0000-0002-4574-1318
authors.initials: MW
authors.lastname: Jo
authors.forename: Chang Hwan
authors.identifier: http://orcid.org/0000-0001-9747-0196
authors.initials: CH
authors.lastname: Sohn
authors.forename: Soo-Yong
authors.identifier: http://orcid.org/0000-0002-2410-6120
authors.initials: SY
authors.lastname: Shin
authors.forename: JaeHo
authors.identifier: http://orcid.org/0000-0003-2619-1231
authors.initials: J
authors.lastname: Lee
authors.forename: Maengsoo
authors.identifier: http://orcid.org/0000-0002-9566-7099
authors.initials: M
authors.lastname: Yu
authors.forename: Won Young
authors.identifier: http://orcid.org/0000-0002-6904-5966
authors.initials: WY
authors.lastname: Kim
authors.forename: Kyoung Soo
authors.identifier: http://orcid.org/0000-0001-8716-008X
authors.initials: KS
authors.lastname: Lim
authors.forename: Sang-Il
authors.identifier: http://orcid.org/0000-0002-1068-7542
authors.initials: SI
authors.lastname: Lee
mesh_terms: D000328:Adult; D002487:Centers for Disease Control and Prevention, U.S.; D003625:Data Collection / Q000379:methods; D005260:Female; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D020407:Internet; D008297:Male; D008875:Middle Aged; D011159:Population Surveillance / Q000379:methods*; D057386:Search Engine; D014481:United States / Q000453:epidemiology
keywords: Google Flu Trends; Internet search; influenza; influenza-like illness; query; syndromic surveillance system