Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.
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用于评估新闻趋势与传染病爆发之间关联的时间主题建模。

DOI:
10.1038/srep40841
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发表时间:
2017-01-19
期刊:
影响因子:
4.6
通讯作者:
Ramakrishnan N
Ramakrishnan N
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Ghosh S;Chakraborty P;Nsoesie EO;Cohn E;Mekaru SR;Brownstein JS;Ramakrishnan N

文献摘要

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在回顾性评估中,互联网新闻报道已被证明可以在官方实验室确认之前捕获未知传染病传播的早期报告。一般来说,媒体的兴趣和报道在疫情爆发期间会达到高峰和减弱。在这项研究中,我们量化了在传染病爆发期间媒体的兴趣在多大程度上表明了报告发病率的趋势。我们介绍了一种方法,使用监督的时间主题模型转换成时间主题趋势的大型语料库的新闻文章。这种方法的主要优点包括:适用于广泛的疾病和捕捉疾病动态的能力,包括季节性,突然的高峰和低谷。我们使用来自美利坚合众国(U.S.)报告的多种传染病暴发的数据评估了该方法,中国和印度。我们证明,从疾病相关新闻报道中提取的时间主题趋势成功地捕捉了多个疫情的动态,如美国的百日咳(2012年),印度的登革热疫情(2013年)和中国(2014年)。我们的观察结果还表明,当新闻报道是统一的,有效的建模的时间主题的趋势,使用时间序列回归技术可以估计疾病病例数与卫生组织的官方报告之前,增加精度。
In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.