Using Google Trends for influenza surveillance in South China.

<h4>Background</h4>Google Flu Trends was developed to estimate influenza activity in many countries; however there is currently no Google Flu Trends or other Internet search data used for influenza surveillance in China.<h4>Methods and findings</h4>Influenza surveillance data...

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Autores principales:	Min Kang, Haojie Zhong, Jianfeng He, Shannon Rutherford, Fen Yang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6c0c88557031495c98f288895ad04cef
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spelling	oai:doaj.org-article:6c0c88557031495c98f288895ad04cef2021-11-18T07:59:57ZUsing Google Trends for influenza surveillance in South China.1932-620310.1371/journal.pone.0055205https://doaj.org/article/6c0c88557031495c98f288895ad04cef2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23372837/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Google Flu Trends was developed to estimate influenza activity in many countries; however there is currently no Google Flu Trends or other Internet search data used for influenza surveillance in China.<h4>Methods and findings</h4>Influenza surveillance data from 2008 through 2011 were obtained from provincial CDC influenza-like illness and virological surveillance systems of Guangdong, a province in south China. Internet search data were downloaded from the website of Google Trends. Pearson's correlation coefficients with 95% confidence intervals (95% CI) were calculated to compare surveillance data and internet search trends. The correlation between CDC ILI surveillance and CDC virus surveillance was 0.56 (95% CI: 0.43, 0.66). The strongest correlation was between the Google Trends term of Fever and ILI surveillance with a correlation coefficient of 0.73 (95% CI: 0.66, 0.79). When compared with influenza virological surveillance, the Google Trends term of Influenza A had the strongest correlation with a correlation coefficient of 0.64 (95% CI: 0.43, 0.79) in the 2009 H1N1 influenza pandemic period.<h4>Conclusions</h4>This study shows that Google Trends in Chinese can be used as a complementary source of data for influenza surveillance in south China. More research in the future should develop new models using search trends in Chinese language to estimate local disease activity and detect early signals of outbreaks.Min KangHaojie ZhongJianfeng HeShannon RutherfordFen YangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 1, p e55205 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Min Kang Haojie Zhong Jianfeng He Shannon Rutherford Fen Yang Using Google Trends for influenza surveillance in South China.
description	<h4>Background</h4>Google Flu Trends was developed to estimate influenza activity in many countries; however there is currently no Google Flu Trends or other Internet search data used for influenza surveillance in China.<h4>Methods and findings</h4>Influenza surveillance data from 2008 through 2011 were obtained from provincial CDC influenza-like illness and virological surveillance systems of Guangdong, a province in south China. Internet search data were downloaded from the website of Google Trends. Pearson's correlation coefficients with 95% confidence intervals (95% CI) were calculated to compare surveillance data and internet search trends. The correlation between CDC ILI surveillance and CDC virus surveillance was 0.56 (95% CI: 0.43, 0.66). The strongest correlation was between the Google Trends term of Fever and ILI surveillance with a correlation coefficient of 0.73 (95% CI: 0.66, 0.79). When compared with influenza virological surveillance, the Google Trends term of Influenza A had the strongest correlation with a correlation coefficient of 0.64 (95% CI: 0.43, 0.79) in the 2009 H1N1 influenza pandemic period.<h4>Conclusions</h4>This study shows that Google Trends in Chinese can be used as a complementary source of data for influenza surveillance in south China. More research in the future should develop new models using search trends in Chinese language to estimate local disease activity and detect early signals of outbreaks.
format	article
author	Min Kang Haojie Zhong Jianfeng He Shannon Rutherford Fen Yang
author_facet	Min Kang Haojie Zhong Jianfeng He Shannon Rutherford Fen Yang
author_sort	Min Kang
title	Using Google Trends for influenza surveillance in South China.
title_short	Using Google Trends for influenza surveillance in South China.
title_full	Using Google Trends for influenza surveillance in South China.
title_fullStr	Using Google Trends for influenza surveillance in South China.
title_full_unstemmed	Using Google Trends for influenza surveillance in South China.
title_sort	using google trends for influenza surveillance in south china.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/6c0c88557031495c98f288895ad04cef
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_version_	1718422670785118208

Using Google Trends for influenza surveillance in South China.

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