Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis.
Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis.
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使用新颖的Internet数据流进行准确的流感监测和预测：波士顿大都会的案例研究。
DOI：
10.2196/publichealth.8950
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发表时间：
2018-01-09
影响因子：
8.5
通讯作者：
Santillana M
中科院分区：
文献类型：
--
作者：
Lu FS;Hou S;Baltrusaitis K;Shah M;Leskovec J;Sosic R;Hawkins J;Brownstein J;Conidi G;Gunn J;Gray J;Zink A;Santillana M
流感爆发对世界各地的公共卫生构成重大挑战，仅在美国每年就导致数千人死亡。在城市一级跟踪流感活动的精确系统是必要的，以提供可用于临床、医院和社区疫情准备的可操作信息。虽然基于互联网的实时数据源，如谷歌搜索和推文，已成功地用于产生流感活动的估计之前，传统的卫生保健为基础的系统在国家和州一级，流感跟踪和预测在更精细的空间分辨率，如城市一级，仍然是一个悬而未决的问题。我们的研究旨在提出一种精确的，近实时的方法，能够在波士顿公共卫生委员会（BPHC）为波士顿大都市区收集和发布的流感估计之前产生流感估计。这种方法有很大的潜力，可以推广到其他有类似数据来源的城市。我们首先测试了谷歌搜索、Twitter帖子、电子健康记录和众包流感报告系统分别检测波士顿大都市大都会流感活动的能力。然后，我们采用了一种名为ARGO（一般在线信息自回归）的多变量动态回归方法，旨在跟踪国家层面的流感，并表明它有效地使用上述数据源来监测和预测当前日期前1周的城市层面的流感。最后，我们提出了一种基于集成的方法，能够将基于多个数据源的模型中的信息结合起来，以更稳健地进行临近预报，并预测波士顿大都市区的流感活动。我们的模型的性能在2012-2016年的4个流感季节以及2016 - 2017年的保持验证期内以样本外方式进行了评估。我们基于集成的方法整合了来自基于多个数据源（包括ARGO）的不同模型的信息，产生了最稳健和准确的结果。我们的样本外流感活动估计值与BPHC历史上报告的流感活动之间的Pearson相关性在临近预报流感中为0.98，在当前日期前1周预测流感中为0.94。我们表明，从互联网上的数据来源，信息结合使用一个明智的，强大的方法，可以有效地用作流感活动的早期指标，在精细的地理分辨率。
Influenza outbreaks pose major challenges to public health around the world, leading to thousands of deaths a year in the United States alone. Accurate systems that track influenza activity at the city level are necessary to provide actionable information that can be used for clinical, hospital, and community outbreak preparation. Although Internet-based real-time data sources such as Google searches and tweets have been successfully used to produce influenza activity estimates ahead of traditional health care–based systems at national and state levels, influenza tracking and forecasting at finer spatial resolutions, such as the city level, remain an open question. Our study aimed to present a precise, near real-time methodology capable of producing influenza estimates ahead of those collected and published by the Boston Public Health Commission (BPHC) for the Boston metropolitan area. This approach has great potential to be extended to other cities with access to similar data sources. We first tested the ability of Google searches, Twitter posts, electronic health records, and a crowd-sourced influenza reporting system to detect influenza activity in the Boston metropolis separately. We then adapted a multivariate dynamic regression method named ARGO (autoregression with general online information), designed for tracking influenza at the national level, and showed that it effectively uses the above data sources to monitor and forecast influenza at the city level 1 week ahead of the current date. Finally, we presented an ensemble-based approach capable of combining information from models based on multiple data sources to more robustly nowcast as well as forecast influenza activity in the Boston metropolitan area. The performances of our models were evaluated in an out-of-sample fashion over 4 influenza seasons within 2012-2016, as well as a holdout validation period from 2016 to 2017. Our ensemble-based methods incorporating information from diverse models based on multiple data sources, including ARGO, produced the most robust and accurate results. The observed Pearson correlations between our out-of-sample flu activity estimates and those historically reported by the BPHC were 0.98 in nowcasting influenza and 0.94 in forecasting influenza 1 week ahead of the current date. We show that information from Internet-based data sources, when combined using an informed, robust methodology, can be effectively used as early indicators of influenza activity at fine geographic resolutions.
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影响因子：
3.6
作者：
Araz, Ozgur M.;Bentley, Dan;Muelleman, Robert L.
通讯作者：
Muelleman, Robert L.
影响因子：
3.8
作者：
Gluskin RT;Johansson MA;Santillana M;Brownstein JS
通讯作者：
Brownstein JS
DOI：
10.1089/bsp.2011.0007
发表时间：
2011-06-01
期刊：
BIOSECURITY AND BIOTERRORISM-BIODEFENSE STRATEGY PRACTICE AND SCIENCE
影响因子：
--
作者：
Lipsitch, Marc;Finelli, Lyn;Redd, Stephen C.
通讯作者：
Redd, Stephen C.
影响因子：
4.3
作者：
Santillana M;Nguyen AT;Dredze M;Paul MJ;Nsoesie EO;Brownstein JS
通讯作者：
Brownstein JS
影响因子：
8.5
作者：
Baltrusaitis K;Santillana M;Crawley AW;Chunara R;Smolinski M;Brownstein JS
通讯作者：
Brownstein JS