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Emergency department and 'Google flu trends' data as syndromic surveillance indicators for seasonal influenza.
Epidemiol Infect. 2014 Nov; 142(11):2397-405.EI

Abstract

We evaluated syndromic indicators of influenza disease activity developed using emergency department (ED) data - total ED visits attributed to influenza-like illness (ILI) ('ED ILI volume') and percentage of visits attributed to ILI ('ED ILI percent') - and Google flu trends (GFT) data (ILI cases/100 000 physician visits). Congruity and correlation among these indicators and between these indicators and weekly count of laboratory-confirmed influenza in Manitoba was assessed graphically using linear regression models. Both ED and GFT data performed well as syndromic indicators of influenza activity, and were highly correlated with each other in real time. The strongest correlations between virological data and ED ILI volume and ED ILI percent, respectively, were 0·77 and 0·71. The strongest correlation of GFT was 0·74. Seasonal influenza activity may be effectively monitored using ED and GFT data.

Authors+Show Affiliations

Department of Community Health Sciences,University of Manitoba,Winnipeg, MB,Canada.Department of Community Health Sciences,University of Manitoba,Winnipeg, MB,Canada.Department of Mathematics,University of Manitoba,Winnipeg, MB,Canada.Department of Emergency Medicine,University of Manitoba,Winnipeg, MB,Canada.Department of Community Health Sciences,University of Manitoba,Winnipeg, MB,Canada.

Pub Type(s)

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

Language

eng

PubMed ID

24480399

Citation

Thompson, L H., et al. "Emergency Department and 'Google Flu Trends' Data as Syndromic Surveillance Indicators for Seasonal Influenza." Epidemiology and Infection, vol. 142, no. 11, 2014, pp. 2397-405.
Thompson LH, Malik MT, Gumel A, et al. Emergency department and 'Google flu trends' data as syndromic surveillance indicators for seasonal influenza. Epidemiol Infect. 2014;142(11):2397-405.
Thompson, L. H., Malik, M. T., Gumel, A., Strome, T., & Mahmud, S. M. (2014). Emergency department and 'Google flu trends' data as syndromic surveillance indicators for seasonal influenza. Epidemiology and Infection, 142(11), 2397-405. https://doi.org/10.1017/S0950268813003464
Thompson LH, et al. Emergency Department and 'Google Flu Trends' Data as Syndromic Surveillance Indicators for Seasonal Influenza. Epidemiol Infect. 2014;142(11):2397-405. PubMed PMID: 24480399.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Emergency department and 'Google flu trends' data as syndromic surveillance indicators for seasonal influenza. AU - Thompson,L H, AU - Malik,M T, AU - Gumel,A, AU - Strome,T, AU - Mahmud,S M, Y1 - 2014/01/20/ PY - 2014/2/1/entrez PY - 2014/2/1/pubmed PY - 2014/12/15/medline SP - 2397 EP - 405 JF - Epidemiology and infection JO - Epidemiol. Infect. VL - 142 IS - 11 N2 - We evaluated syndromic indicators of influenza disease activity developed using emergency department (ED) data - total ED visits attributed to influenza-like illness (ILI) ('ED ILI volume') and percentage of visits attributed to ILI ('ED ILI percent') - and Google flu trends (GFT) data (ILI cases/100 000 physician visits). Congruity and correlation among these indicators and between these indicators and weekly count of laboratory-confirmed influenza in Manitoba was assessed graphically using linear regression models. Both ED and GFT data performed well as syndromic indicators of influenza activity, and were highly correlated with each other in real time. The strongest correlations between virological data and ED ILI volume and ED ILI percent, respectively, were 0·77 and 0·71. The strongest correlation of GFT was 0·74. Seasonal influenza activity may be effectively monitored using ED and GFT data. SN - 1469-4409 UR - https://www.unboundmedicine.com/medline/citation/24480399/Emergency_department_and_'Google_flu_trends'_data_as_syndromic_surveillance_indicators_for_seasonal_influenza_ L2 - https://www.cambridge.org/core/product/identifier/S0950268813003464/type/journal_article DB - PRIME DP - Unbound Medicine ER -