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首页> 外文期刊>JMIR public health and surveillance. >Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study
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Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study

机译：在城市医院中使用社交媒体进行本地流感监测：一项回顾性观察研究

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Background: Public health officials and policy makers in the United States expend significant resources at the national, state, county, and city levels to measure the rate of influenza infection. These individuals rely on influenza infection rate information to make important decisions during the course of an influenza season driving vaccination campaigns, clinical guidelines, and medical staffing. Web and social media data sources have emerged as attractive alternatives to supplement existing practices. While traditional surveillance methods take 1-2 weeks, and significant labor, to produce an infection estimate in each locale, web and social media data are available in near real-time for a broad range of locations. Objective: The objective of this study was to analyze the efficacy of flu surveillance from combining data from the websites Google Flu Trends and HealthTweets at the local level. We considered both emergency department influenza-like illness cases and laboratory-confirmed influenza cases for a single hospital in the City of Baltimore. Methods: This was a retrospective observational study comparing estimates of influenza activity of Google Flu Trends and Twitter to actual counts of individuals with laboratory-confirmed influenza, and counts of individuals presenting to the emergency department with influenza-like illness cases. Data were collected from November 20, 2011 through March 16, 2014. Each parameter was evaluated on the municipal, regional, and national scale. We examined the utility of social media data for tracking actual influenza infection at the municipal, state, and national levels. Specifically, we compared the efficacy of Twitter and Google Flu Trends data. Results: We found that municipal-level Twitter data was more effective than regional and national data when tracking actual influenza infection rates in a Baltimore inner-city hospital. When combined, national-level Twitter and Google Flu Trends data outperformed each data source individually. In addition, influenza-like illness data at all levels of geographic granularity were best predicted by national Google Flu Trends data. Conclusions: In order to overcome sensitivity to transient events, such as the news cycle, the best-fitting Google Flu Trends model relies on a 4-week moving average, suggesting that it may also be sacrificing sensitivity to transient fluctuations in influenza infection to achieve predictive power. Implications for influenza forecasting are discussed in this report.
机译：背景：美国的公共卫生官员和政策制定者在国家，州，县和市各级花费了大量资源来衡量流感感染率。这些人依靠流感感染率信息来在流感季节驾驶疫苗接种运动，临床指南和医务人员的过程中做出重要决定。网络和社交媒体数据源已成为替代现有做法的有吸引力的替代方法。尽管传统的监视方法需要花费1-2周的时间，而且要花大量的时间才能在每个区域生成估计的感染量，但网络和社交媒体数据几乎可以实时地在广泛的位置使用。目的：本研究的目的是通过结合本地Google流感趋势和HealthTweets网站的数据来分析流感监测的效果。我们考虑了巴尔的摩市一家医院的急诊科类流感病例和实验室确诊的流感病例。方法：这是一项回顾性观察性研究，将Google Flu Trends和Twitter的流感活动估计值与实验室确诊的流感病毒的实际人数以及呈流感样疾病病例的急诊人员的人数进行了比较。收集了2011年11月20日至2014年3月16日的数据。每个参数均在市政，区域和国家范围内进行了评估。我们检查了社交媒体数据在跟踪市，州和国家各级实际流感感染情况中的实用性。具体来说，我们比较了Twitter和Google Flu Trends数据的功效。结果：我们发现，在追踪巴尔的摩市中心医院的实际流感感染率时，市政级Twitter数据比区域和国家/地区数据更有效。综合起来，国家级Twitter和Google Flu Trends数据的表现要优于每个数据源。此外，国家Google流感趋势数据可以最好地预测所有地理粒度级别的类似流感样疾病数据。结论：为了克服对新闻事件等短暂事件的敏感性，最适合的Google Flu Trends模型依靠4周的移动平均值，这表明它可能还会牺牲对流感病毒感染暂时性波动的敏感性，以实现预测力。本报告讨论了流感预测的意义。

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