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Improving Google Flu Trends Estimates for the United States through Transformation

Google Flu Trends (GFT) uses Internet search queries in an effort to provide early warning of increases in influenza-like illness (ILI). In the United States, GFT estimates the percentage of physician visits related to ILI (%ILINet) reported by the Centers for Disease Control and Prevention (CDC). H...

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Autores principales: Martin, Leah J., Xu, Biying, Yasui, Yutaka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4281210/
https://www.ncbi.nlm.nih.gov/pubmed/25551391
http://dx.doi.org/10.1371/journal.pone.0109209
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author Martin, Leah J.
Xu, Biying
Yasui, Yutaka
author_facet Martin, Leah J.
Xu, Biying
Yasui, Yutaka
author_sort Martin, Leah J.
collection PubMed
description Google Flu Trends (GFT) uses Internet search queries in an effort to provide early warning of increases in influenza-like illness (ILI). In the United States, GFT estimates the percentage of physician visits related to ILI (%ILINet) reported by the Centers for Disease Control and Prevention (CDC). However, during the 2012–13 influenza season, GFT overestimated %ILINet by an appreciable amount and estimated the peak in incidence three weeks late. Using data from 2010–14, we investigated the relationship between GFT estimates (%GFT) and %ILINet. Based on the relationship between the relative change in %GFT and the relative change in %ILINet, we transformed %GFT estimates to better correspond with %ILINet values. In 2010–13, our transformed %GFT estimates were within ±10% of %ILINet values for 17 of the 29 weeks that %ILINet was above the seasonal baseline value determined by the CDC; in contrast, the original %GFT estimates were within ±10% of %ILINet values for only two of these 29 weeks. Relative to the %ILINet peak in 2012–13, the peak in our transformed %GFT estimates was 2% lower and one week later, whereas the peak in the original %GFT estimates was 74% higher and three weeks later. The same transformation improved %GFT estimates using the recalibrated 2013 GFT model in early 2013–14. Our transformed %GFT estimates can be calculated approximately one week before %ILINet values are reported by the CDC and the transformation equation was stable over the time period investigated (2010–13). We anticipate our results will facilitate future use of GFT.
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spelling pubmed-42812102015-01-07 Improving Google Flu Trends Estimates for the United States through Transformation Martin, Leah J. Xu, Biying Yasui, Yutaka PLoS One Research Article Google Flu Trends (GFT) uses Internet search queries in an effort to provide early warning of increases in influenza-like illness (ILI). In the United States, GFT estimates the percentage of physician visits related to ILI (%ILINet) reported by the Centers for Disease Control and Prevention (CDC). However, during the 2012–13 influenza season, GFT overestimated %ILINet by an appreciable amount and estimated the peak in incidence three weeks late. Using data from 2010–14, we investigated the relationship between GFT estimates (%GFT) and %ILINet. Based on the relationship between the relative change in %GFT and the relative change in %ILINet, we transformed %GFT estimates to better correspond with %ILINet values. In 2010–13, our transformed %GFT estimates were within ±10% of %ILINet values for 17 of the 29 weeks that %ILINet was above the seasonal baseline value determined by the CDC; in contrast, the original %GFT estimates were within ±10% of %ILINet values for only two of these 29 weeks. Relative to the %ILINet peak in 2012–13, the peak in our transformed %GFT estimates was 2% lower and one week later, whereas the peak in the original %GFT estimates was 74% higher and three weeks later. The same transformation improved %GFT estimates using the recalibrated 2013 GFT model in early 2013–14. Our transformed %GFT estimates can be calculated approximately one week before %ILINet values are reported by the CDC and the transformation equation was stable over the time period investigated (2010–13). We anticipate our results will facilitate future use of GFT. Public Library of Science 2014-12-31 /pmc/articles/PMC4281210/ /pubmed/25551391 http://dx.doi.org/10.1371/journal.pone.0109209 Text en © 2014 Martin et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Martin, Leah J.
Xu, Biying
Yasui, Yutaka
Improving Google Flu Trends Estimates for the United States through Transformation
title Improving Google Flu Trends Estimates for the United States through Transformation
title_full Improving Google Flu Trends Estimates for the United States through Transformation
title_fullStr Improving Google Flu Trends Estimates for the United States through Transformation
title_full_unstemmed Improving Google Flu Trends Estimates for the United States through Transformation
title_short Improving Google Flu Trends Estimates for the United States through Transformation
title_sort improving google flu trends estimates for the united states through transformation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4281210/
https://www.ncbi.nlm.nih.gov/pubmed/25551391
http://dx.doi.org/10.1371/journal.pone.0109209
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