Tracking and predicting U.S. influenza activity with a real-time surveillance network.
Tracking and predicting U.S. influenza activity with a real-time surveillance network.
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DOI：
10.1371/journal.pcbi.1008180
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发表时间：
2020-11
影响因子：
4.3
通讯作者：
Zimmer C
中科院分区：
文献类型：
--
作者：
Leuba SI;Yaesoubi R;Antillon M;Cohen T;Zimmer C
在美国，流感每年导致 9.2 至 3560 万人患病，并导致 12,000 至 56,000 人死亡。美国疾病控制与预防中心 (CDC) 通过国家监测网络追踪流感活动。这些数据要延迟 1 至 2 周后才能获得，因此流感流行病学家和传播建模者已经探索使用其他数据源来对流感活动进行更及时的估计和预测。我们评估了从全国流感诊断机商业网络收集的数据是否可以对当前负担进行有效估计并有助于预测美国的流感趋势。 Quidel 公司为我们提供了从称为流感测试系统 (ITS) 的全国流感测试机器网络实时传输的去识别化流感测试结果。我们使用此 ITS 数据集来估计和预测美国 2015-2016 和 2016-2017 流感季节的流感样疾病 (ILI) 活动。首先，我们开发了国家和地区地理尺度的线性逻辑模型，准确估计了 CDC 的两个流感指标：流感检测结果呈阳性的比例以及与流感样疾病相关的就诊比例。然后，我们利用传播模型中估计的流感样疾病相关就诊比例，对美国区域和国家范围内的流感趋势进行了改进的预测。这些发现表明，ITS 可用于改善美国流感活动的“即时预报”和短期预测。美国疾病控制与预防中心 (CDC) 通过国家监测系统追踪流感活动。然而，CDC流感监测数据的报告存在1至2周的延迟，这限制了如何利用这些信息来评估当前的疾病负担并及时预测疫情的发展轨迹。研究人员此前曾使用流感活动的间接信号，例如谷歌搜索流感相关术语或推特帖子，以更及时地估计流感负担并改进基于模型的预测。然而，这些间接信号会受到与流感活动无关的行为变化的影响，因此可能会提供不准确的估计和预测。我们使用了与流感活动直接相关的具有高地理分辨率的新实时数据源：通过全国商业流感诊断测试机网络提供的流感测试结果。我们利用这些流感检测结果来准确估计当前的流感负担，并改进美国流感负担的实时模型预测。
Each year in the United States, influenza causes illness in 9.2 to 35.6 million individuals and is responsible for 12,000 to 56,000 deaths. The U.S. Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance network. These data are only available after a delay of 1 to 2 weeks, and thus influenza epidemiologists and transmission modelers have explored the use of other data sources to produce more timely estimates and predictions of influenza activity. We evaluated whether data collected from a national commercial network of influenza diagnostic machines could produce valid estimates of the current burden and help to predict influenza trends in the United States. Quidel Corporation provided us with de-identified influenza test results transmitted in real-time from a national network of influenza test machines called the Influenza Test System (ITS). We used this ITS dataset to estimate and predict influenza-like illness (ILI) activity in the United States over the 2015-2016 and 2016-2017 influenza seasons. First, we developed linear logistic models on national and regional geographic scales that accurately estimated two CDC influenza metrics: the proportion of influenza test results that are positive and the proportion of physician visits that are ILI-related. We then used our estimated ILI-related proportion of physician visits in transmission models to produce improved predictions of influenza trends in the United States at both the regional and national scale. These findings suggest that ITS can be leveraged to improve “nowcasts” and short-term forecasts of U.S. influenza activity. The United States Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance system. However, the CDC influenza surveillance data are subject to a 1 to 2 week reporting delay, which limits how such information can be used to assess the current burden of disease and to make timely projections of the trajectory of the epidemic. Researchers have previously used indirect signals of influenza activity such as Google search queries for influenza-related terms or Twitter posts to develop more timely estimates of influenza burden and improved model-based forecasts. However, these indirect signals are subject to behavioral changes not related to influenza activity, and thus may provide inaccurate estimates and projections. We used a new real-time data source with high geographic resolution that is directly related to influenza activity: influenza test results provided through a national network of commercial influenza diagnostic test machines. We used these influenza test results to accurately estimate the current burden of influenza and improve real-time model projections of influenza burden in the United States.
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