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Cumulative query method for influenza surveillance using search engine data.
J Med Internet Res. 2014 Dec 16; 16(12):e289.JM

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.

OBJECTIVES

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+Show Affiliations

Asan Medical Center, Department of Emergency Medicine, University of Ulsan, College of Medicine, Seoul, Republic Of Korea.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

25517353

Citation

Seo, Dong-Woo, et al. "Cumulative Query Method for Influenza Surveillance Using Search Engine Data." Journal of Medical Internet Research, vol. 16, no. 12, 2014, pp. e289.
Seo DW, Jo MW, Sohn CH, et al. Cumulative query method for influenza surveillance using search engine data. J Med Internet Res. 2014;16(12):e289.
Seo, D. W., Jo, M. W., Sohn, C. H., Shin, S. Y., Lee, J., Yu, M., Kim, W. Y., Lim, K. S., & Lee, S. I. (2014). Cumulative query method for influenza surveillance using search engine data. Journal of Medical Internet Research, 16(12), e289. https://doi.org/10.2196/jmir.3680
Seo DW, et al. Cumulative Query Method for Influenza Surveillance Using Search Engine Data. J Med Internet Res. 2014 Dec 16;16(12):e289. PubMed PMID: 25517353.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Cumulative query method for influenza surveillance using search engine data. AU - Seo,Dong-Woo, AU - Jo,Min-Woo, AU - Sohn,Chang Hwan, AU - Shin,Soo-Yong, AU - Lee,JaeHo, AU - Yu,Maengsoo, AU - Kim,Won Young, AU - Lim,Kyoung Soo, AU - Lee,Sang-Il, Y1 - 2014/12/16/ PY - 2014/07/07/received PY - 2014/11/21/accepted PY - 2014/08/25/revised PY - 2014/12/18/entrez PY - 2014/12/18/pubmed PY - 2015/7/15/medline KW - Google Flu Trends KW - Internet search KW - influenza KW - influenza-like illness KW - query KW - syndromic surveillance system SP - e289 EP - e289 JF - Journal of medical Internet research JO - J. Med. Internet Res. VL - 16 IS - 12 N2 - 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. OBJECTIVES: 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. SN - 1438-8871 UR - https://www.unboundmedicine.com/medline/citation/25517353/Cumulative_query_method_for_influenza_surveillance_using_search_engine_data_ L2 - https://www.jmir.org/2014/12/e289/ DB - PRIME DP - Unbound Medicine ER -