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.0023610
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Mohebbi MH
中科院分区：
文献类型：
--
作者：
Cook S;Conrad C;Fowlkes AL;Mohebbi MH
谷歌流感趋势 (GFT) 使用匿名、聚合的互联网搜索活动来提供近乎实时的流感活动估计。 GFT 估计值与官方流感监测数据有很强的相关性。 2009 年甲型流感病毒 (H1N1) 大流行 [pH1N1] 首次提供了在非季节性流感爆发期间评估 GFT 的机会。 2009 年 9 月，使用 pH1N1 初期的数据开发了更新的美国 GFT 模型。我们通过将 ILI（流感样疾病）活动的每周估计值与美国门诊流感样疾病监测网络 (ILINet) 进行比较，评估了每个美国 GFT 模型的准确性。对于每个 GFT 模型，我们计算了四个时间段的模型估计值和 ILINEt 之间的相关性和 RMSE（均方根误差）：H1N1 流感前、夏季 H1N1、冬季 H1N1 和 H1N1 整体（2009 年 3 月至 2009 年 12 月）。我们还比较了每个模型中的查询数量、查询量和查询类型（例如流感症状、流感并发症）。尽管原始模型低估了 pH1N1 期间 ILI 活动的强度，但这两个模型的估计均与 H1N1 流感前和整个监测期间的 ILINEt 高度相关。在夏季 H1N1 期间，更新后的模型与 ILINEt 的相关性高于原始模型（r = 0.95 和 0.29）。更新后的模型比原始模型包含更多的搜索查询词，其中更多的查询与流感感染直接相关，而原始模型包含更多与流感并发症相关的查询。 pH1N1 期间，互联网搜索行为发生了变化，特别是在“流感并发症”和“流感术语”类别中。与 pH1N1 相关的并发症、pH1N1 开始于夏季而不是冬季的事实以及寻求健康行为的变化都可能起到了一定作用。两个 GFT 模型在 pH1N1 之前和期间都表现良好，尽管更新后的模型在 pH1N1 期间（尤其是在夏季）表现更好。
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. 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. 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.
影响因子：
158.5
作者：
Dawood, Fatimah S.;Jain, Seema;Uyeki, Timothy M.
通讯作者：
Uyeki, Timothy M.
影响因子：
11.8
作者：
Brammer, Lynnette;Blanton, Lenee;Finelli, Lyn
通讯作者：
Finelli, Lyn
影响因子：
11.8
作者：
Reed, Carrie;Angulo, Frederick J.;Finelli, Lyn
通讯作者：
Finelli, Lyn