Adaptive nowcasting of influenza outbreaks using Google searches.
Adaptive nowcasting of influenza outbreaks using Google searches.
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DOI：
10.1098/rsos.140095
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发表时间：
2014-10
影响因子：
3.5
通讯作者：
Moat HS
中科院分区：
文献类型：
--
作者：
Preis T;Moat HS
季节性流感暴发和新流感病毒株的大流行影响着全球各地的人类。然而，传统的测量流感感染传播的系统会延迟一到两周才能得出结果。最近的研究表明，向搜索引擎谷歌查询的数据可以用来解决这个问题，提供对人群中流感样疾病水平的实时估计。然而，其他人认为，可以使用历史上的流感测量来预测当前流感水平的同样好的估计。在这里，我们建立了动态的“即时预测”模型；换句话说，在一周后官方数据发布之前，我们建立了估计当前流感水平的预测模型。我们发现，当使用谷歌流感趋势数据与历史流感水平相结合时，与仅使用流感水平历史数据的基线模型相比，样本中的“即时预测”的平均绝对误差(MAE)可以显著降低14.4%。我们进一步证明，根据滑动训练间隔的长度，样本外现播的MAE也可以显著降低16.0%到52.7%。我们的结论是，使用自适应模型，谷歌流感趋势数据确实可以用来改进实时流感监测，即使只需延迟一周就可以获得流感感染的官方报告。
Seasonal influenza outbreaks and pandemics of new strains of the influenza virus affect humans around the globe. However, traditional systems for measuring the spread of flu infections deliver results with one or two weeks delay. Recent research suggests that data on queries made to the search engine Google can be used to address this problem, providing real-time estimates of levels of influenza-like illness in a population. Others have however argued that equally good estimates of current flu levels can be forecast using historic flu measurements. Here, we build dynamic ‘nowcasting’ models; in other words, forecasting models that estimate current levels of influenza, before the release of official data one week later. We find that when using Google Flu Trends data in combination with historic flu levels, the mean absolute error (MAE) of in-sample ‘nowcasts’ can be significantly reduced by 14.4%, compared with a baseline model that uses historic data on flu levels only. We further demonstrate that the MAE of out-of-sample nowcasts can also be significantly reduced by between 16.0% and 52.7%, depending on the length of the sliding training interval. We conclude that, using adaptive models, Google Flu Trends data can indeed be used to improve real-time influenza monitoring, even when official reports of flu infections are available with only one week's delay.
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影响因子：
4.6
作者：
Preis T;Moat HS;Bishop SR;Treleaven P;Stanley HE
通讯作者：
Stanley HE
DOI：
10.1126/science.1167742
发表时间：
2009-02-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Lazer D;Pentland A;Adamic L;Aral S;Barabasi AL;Brewer D;Christakis N;Contractor N;Fowler J;Gutmann M;Jebara T;King G;Macy M;Roy D;Van Alstyne M
通讯作者：
Van Alstyne M
影响因子：
4.6
作者：
通讯作者：
--
影响因子：
3.7
作者：
Noguchi T;Stewart N;Olivola CY;Moat HS;Preis T
通讯作者：
Preis T
DOI：
10.1098/rsta.2010.0284
发表时间：
2010-12-28
影响因子：
5
作者：
Preis, Tobias;Reith, Daniel;Stanley, H. Eugene
通讯作者：
Stanley, H. Eugene