论文标题
COVID-19感染曲线的时间序列分析：变更点的观点
Time Series Analysis of COVID-19 Infection Curve: A Change-Point Perspective
论文作者
论文摘要
在本文中，我们通过分段线性趋势模型对累积确认病例和死亡的累积案例和死亡进行了建模。该模型自然地通过变更点捕获了流行病生长速率的相变，由于其半参数性质而进一步享有极大的解释性。在方法论方面，我们将新生的自我规范化（SN）技术（SHAO，2010年）推进了测试和估计非组织时间序列的线性趋势中单个变更点的测试和估计。我们进一步将基于SN的更改点测试与非算法（Baranowski等，2019）相结合，以实现多个更改点的估计。使用拟议的方法，我们分析了30个主要国家的累积Covid-19病例和死亡的轨迹，并发现了有趣的模式，对不同国家的大流行反应的有效性具有潜在相关的影响。此外，基于更改点检测算法和灵活的外推函数，我们为Covid-19设计了一个简单的两阶段预测方案，并证明了其在预测美国累积死亡方面的有希望的表现。
In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.S.
