Time series analysis of COVID-19 infection curve: A change-point perspective
Time series analysis of COVID-19 infection curve: A change-point perspective
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DOI：
10.1016/j.jeconom.2020.07.039
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发表时间：
2022-11-21
影响因子：
6.3
通讯作者：
Shao, Xiaofeng
中科院分区：
文献类型：
--
作者：
Jiang, Feiyu;Zhao, Zifeng;Shao, Xiaofeng
本文利用分段线性趋势模型对新冠肺炎累计确诊病例和死亡人数(对数尺度)的轨迹进行了建模。该模型通过变点自然地捕捉了流行病增长率的相变，并由于其半参数性质而具有很强的可解释性。在方法论方面，我们提出了新兴的自归一化(SN)技术(Shao，2010)来检验和估计非平稳时间序列的线性趋势中的单个变点。我们进一步将基于SN的变点测试与NOT算法(Baranowski等人，2019)相结合，实现了多个变点估计。使用所提出的方法，我们分析了30个主要国家的累积新冠肺炎病例和死亡的轨迹，发现了有趣的模式，对于不同国家应对大流行的有效性具有潜在的相关影响。此外，基于变点检测算法和灵活的外推函数，我们设计了一个简单的新冠肺炎两阶段预测方案，并展示了其在预测美国(C)2020爱思唯尔公司累积死亡人数中的良好性能。
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. (c) 2020 Elsevier B.V. All rights reserved.