Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic
Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic
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DOI：
10.1371/journal.pone.0025407
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发表时间：
2011-08-19
期刊：
影响因子：
3.7
通讯作者：
Mohebbi, Matthew H.
中科院分区：
文献类型：
--
作者：
Cook, Samantha;Conrad, Corrie;Mohebbi, Matthew H.
背景：Google流感趋势（GFT）使用匿名，汇总的Internet搜索活动，以提供对流感活动的近乎真实的时间估计。 GFT估计显示与官方流感监测数据有很强的相关性。 2009年流感病毒A（H1N1）大流行[PH1N1]为在非季节性流感爆发期间评估GFT提供了第一个机会。 2009年9月，使用PH1N1开头的数据开发了一个更新的美国GFT模型。方法论/主要发现：我们通过比较了ILI（类似流感的疾病）活动的每周估计值，评估了每个美国GFT模型的准确性。门诊流感样疾病监测网络（ILINET）。对于每个GFT模型，我们计算了四个时间段的模型估计和ILINET之间的相关性和RMSE（根平方误差）：PRE-H1N1，夏季H1N1，Winter H1N1和H1N1总体上（Mar 2009-DEC 2009）。我们还比较了每个模型中查询数量，查询量和类型的查询数量（例如，流感症状，流感并发症）。两种模型的估计值都与ILINET PRE-H1N1高度相关，并且在整个监视期间，尽管原始模型低估了PH1N1期间ILI活性的幅度。与夏季H1N1期间的原始模型相比，更新的模型与ILINET相关（分别为r = 0.95和0.29）。更新的模型包含的搜索查询术语比原始模型更多，与流感感染直接相关的更多查询，而原始模型包含与流感并发症相关的更多查询。结论：互联网搜索行为在PH1N1期间发生了变化，尤其是在类别中“流感并发症”。 ''和“流感术语”。''与PH1N1相关的并发症，PH1N1在夏季而不是冬季开始，并且变化在寻求健康的行为中，每个行为都可能发挥了作用。尽管更新的模型在PH1N1期间，尤其是在夏季，但两种GFT模型在PH1N1之前和PH1N1期间都表现出色。
Background: Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1.Methodology/Principal Findings: We evaluated the accuracy of each U. S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U. S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009-Dec 2009). We also compared the number of queries, query volume, and types of queries (e. g., influenza symptoms, influenza complications) in each model. Both models' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications.Conclusions: Internet search behavior changed during pH1N1, particularly in the categories "influenza complications'' and "term for influenza.'' The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months.