Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths (preprint)/ en
Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths (preprint)/ en
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将低延迟数据源与死亡数据融合，准确预测与 COVID-19 相关的死亡（预印本）/ en
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Maskell
中科院分区：
文献类型：
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作者：
Conor Rosato;Robert E. Moore;Matthew Carter;J. Heap;J. Storópoli;S. Maskell
新型冠状病毒（COVID-19）的出现产生了快速准确地收集与其传播相关的最新信息的需求。虽然可以使用死亡来提供可靠的信息源，但从死亡中获得的数据的延迟是显著的。从阳性检测结果导出的确诊病例可能提供较低的延迟数据馈送。然而，对受检者的抽样随时间而变化，而且往往没有记录进行检测的原因。住院通常发生在感染后1-2周左右，可以认为与初始感染时间有关。这些问题的严重程度可能因时间和国家而异。我们使用机器学习算法进行自然语言处理，用多种语言进行训练，以实时识别来自社交媒体，特别是Twitter的症状个体。然后，我们使用一个扩展的SEIRD流行病学模型来融合低延迟提要的组合，包括来自Twitter的症状计数，以及死亡数据来估计模型的参数，并对每个隔间中的人数进行即时预测。该模型在概率编程语言Stan中实现，并使用定制的数值积分器。我们目前的研究结果表明，使用特定的低延迟数据馈送沿着死亡数据，比单独使用死亡数据，可以提供更一致和准确的COVID-19相关死亡预测。
The emergence of the novel coronavirus (COVID-19) has generated a need to quickly and accurately assemble up-to-date information related to its spread. While it is possible to use deaths to provide a reliable information feed, the latency of data derived from deaths is significant. Confirmed cases derived from positive test results potentially provide a lower latency data feed. However, the sampling of those tested varies with time and the reason for testing is often not recorded. Hospital admissions typically occur around 1-2 weeks after infection and can be considered out of date in relation to the time of initial infection. The extent to which these issues are problematic is likely to vary over time and between countries. We use a machine learning algorithm for natural language processing, trained in multiple languages, to identify symptomatic individuals derived from social media and, in particular Twitter, in real-time. We then use an extended SEIRD epidemiological model to fuse combinations of low-latency feeds, including the symptomatic counts from Twitter, with death data to estimate parameters of the model and nowcast the number of people in each compartment. The model is implemented in the probabilistic programming language Stan and uses a bespoke numerical integrator. We present results showing that using specific low-latency data feeds along with death data provides more consistent and accurate forecasts of COVID-19 related deaths than using death data alone.
影响因子：
8.5
作者：
Lwin, May Oo;Lu, Jiahui;Yang, Yinping
通讯作者：
Yang, Yinping