OBJECTIVES: We assessed the performance of syndromic indicators based on Google Flu Trends (GFT) and emergency department (ED) data for the early detection and monitoring of the 2009 H1N1 pandemic waves in Manitoba. METHODS: Time-series curves for the weekly counts of laboratory-confirmed H1N1 cases in Manitoba during the 2009 pandemic were plotted against the three syndromic indicators: 1) GFT data, based on flu-related Internet search queries, 2) weekly count of all ED visits triaged as influenza-like illness (ED ILI volume), and 3) percentage of all ED visits that were triaged as an ILI (ED ILI percent). A linear regression model was fitted separately for each indicator and correlations with weekly virologic data were calculated for different lag periods for each pandemic wave. RESULTS: All three indicators peaked 1-2 weeks earlier than the epidemic curve of laboratory-confirmed cases. For GFT data, the best-fitting model had about a 2-week lag period in relation to the epidemic curve. Similarly, the best-fitting models for both ED indicators were observed for a time lag of 1-2 weeks. All three indicators performed better as predictors of the virologic time trends during the second wave as compared to the first. There was strong congruence between the time series of the GFT and both the ED ILI volume and the ED ILI percent indicators. CONCLUSION: During an influenza season characterized by high levels of disease activity, GFT and ED indicators provided a good indication of weekly counts of laboratory-confirmed influenza cases in Manitoba 1-2 weeks in advance.
OBJECTIVES: We assessed the performance of syndromic indicators based on Google Flu Trends (GFT) and emergency department (ED) data for the early detection and monitoring of the 2009 H1N1 pandemic waves in Manitoba. METHODS: Time-series curves for the weekly counts of laboratory-confirmed H1N1 cases in Manitoba during the 2009 pandemic were plotted against the three syndromic indicators: 1) GFT data, based on flu-related Internet search queries, 2) weekly count of all ED visits triaged as influenza-like illness (ED ILI volume), and 3) percentage of all ED visits that were triaged as an ILI (ED ILI percent). A linear regression model was fitted separately for each indicator and correlations with weekly virologic data were calculated for different lag periods for each pandemic wave. RESULTS: All three indicators peaked 1-2 weeks earlier than the epidemic curve of laboratory-confirmed cases. For GFT data, the best-fitting model had about a 2-week lag period in relation to the epidemic curve. Similarly, the best-fitting models for both ED indicators were observed for a time lag of 1-2 weeks. All three indicators performed better as predictors of the virologic time trends during the second wave as compared to the first. There was strong congruence between the time series of the GFT and both the ED ILI volume and the ED ILI percent indicators. CONCLUSION: During an influenza season characterized by high levels of disease activity, GFT and ED indicators provided a good indication of weekly counts of laboratory-confirmed influenza cases in Manitoba 1-2 weeks in advance.
Authors: Salaheddin M Mahmud; Marissa Becker; Yoav Keynan; Lawrence Elliott; Laura H Thompson; Keith Fowke; Lisa Avery; Paul Van Caeseele; Sande Harlos; James Blanchard; Magdy Dawood Journal: CMAJ Date: 2010-09-07 Impact factor: 8.262
Authors: E Kissling; M Valenciano; Jm Falcao; A Larrauri; K Widgren; D Pitigoi; B Oroszi; B Nunes; C Savulescu; A Mazick; E Lupulescu; B Ciancio; A Moren Journal: Euro Surveill Date: 2009-11-05
Authors: Jeremy Ginsberg; Matthew H Mohebbi; Rajan S Patel; Lynnette Brammer; Mark S Smolinski; Larry Brilliant Journal: Nature Date: 2009-02-19 Impact factor: 49.962
Authors: Anna Chu; Rachel Savage; Michael Whelan; Laura C Rosella; Natasha S Crowcroft; Don Willison; Anne-Luise Winter; Richard Davies; Ian Gemmill; Pia K Mucchal; Ian Johnson Journal: Can J Public Health Date: 2013-05-14
Authors: Sungjin Cho; Chang Hwan Sohn; Min Woo Jo; Soo-Yong Shin; Jae Ho Lee; Seoung Mok Ryoo; Won Young Kim; Dong-Woo Seo Journal: PLoS One Date: 2013-12-05 Impact factor: 3.240