tetano
Editor, Senior Moderator
Zhonghua Yu Fang Yi Xue Za Zhi. 2015 Jun;49(6):581-584.
[h=1][Google Flu Trends--the initial application of big data in public health].[/h] [Article in Chinese]
Zou X[SUP]1[/SUP], Zhu W[SUP]1[/SUP], Yang L[SUP]1[/SUP], Shu Y[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Google Flu Trends (GFT) was the first application of big data in the public health field. GFT was open online in 2009 and attracted worldwide attention immediately. However, GFT failed catching the 2009 pandemic H1N1 and kept overestimating the intensity of influenza-like illness in the 2012-2014 season in the United States. GFT model has been updated for three times since 2009, making its prediction bias controlled. Here, we summarized the mechanism GFT worked, the strategy GFT used to update, and its influence on public health.
PMID: 26310351 [PubMed - as supplied by publisher]
[h=1][Google Flu Trends--the initial application of big data in public health].[/h] [Article in Chinese]
Zou X[SUP]1[/SUP], Zhu W[SUP]1[/SUP], Yang L[SUP]1[/SUP], Shu Y[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Google Flu Trends (GFT) was the first application of big data in the public health field. GFT was open online in 2009 and attracted worldwide attention immediately. However, GFT failed catching the 2009 pandemic H1N1 and kept overestimating the intensity of influenza-like illness in the 2012-2014 season in the United States. GFT model has been updated for three times since 2009, making its prediction bias controlled. Here, we summarized the mechanism GFT worked, the strategy GFT used to update, and its influence on public health.
PMID: 26310351 [PubMed - as supplied by publisher]