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Time Series Analysis of COVID-19 Infection Curve: A Change-Point Perspective

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  • Feiyu Jiang
  • Zifeng Zhao
  • Xiaofeng Shao

Abstract

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.

Suggested Citation

  • Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2020. "Time Series Analysis of COVID-19 Infection Curve: A Change-Point Perspective," Papers 2007.04553, arXiv.org.
    • Handle: RePEc:arx:papers:2007.04553
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    Cited by:

    1. Ziyuan Xia & Jeffery Chen & Anchen Sun, 2021. "Mining the Relationship Between COVID-19 Sentiment and Market Performance," Papers 2101.02587, arXiv.org, revised Mar 2023.
    2. Rolando de la Cruz & Cristian Meza & Nicolás Narria & Claudio Fuentes, 2022. "A Bayesian Change Point Analysis of the USD/CLP Series in Chile from 2018 to 2020: Understanding the Impact of Social Protests and the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(18), pages 1-15, September.
    3. Lujia Bai & Weichi Wu, 2021. "Detecting long-range dependence for time-varying linear models," Papers 2110.08089, arXiv.org, revised Mar 2023.
    4. Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2021. "Local mortality estimates during the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1189-1217, October.
    5. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    6. Zehra Taşkın, 2021. "Forecasting the future of library and information science and its sub-fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1527-1551, February.
    7. Antoni Wiliński & Łukasz Kupracz & Aneta Senejko & Grzegorz Chrząstek, 2022. "COVID-19: average time from infection to death in Poland, USA, India and Germany," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4729-4746, December.
    8. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    9. Geon Lee & Se-eun Yoon & Kijung Shin, 2022. "Simple epidemic models with segmentation can be better than complex ones," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-18, January.
    10. Yiannakoulias, Nikolaos & Slavik, Catherine E. & Sturrock, Shelby L. & Darlington, J. Connor, 2020. "Open government data, uncertainty and coronavirus: An infodemiological case study," Social Science & Medicine, Elsevier, vol. 265(C).
    11. Zifeng Zhao & Feiyu Jiang & Xiaofeng Shao, 2022. "Segmenting time series via self‐normalisation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1699-1725, November.

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