TY - JOUR
T1 - Time series analysis of COVID-19 infection curve
T2 - A change-point perspective
AU - Jiang, Feiyu
AU - Zhao, Zifeng
AU - Shao, Xiaofeng
N1 - Funding Information:
Jiang is supported by the National Key Research and Development Program of China (No.2018YFC1603105), China Scholarship Council (No. 201906210093), National Natural Science Foundation of China (No. 71973077 and No. 11771239) and acknowledges that the work was carried out during the visit at Department of Statistics, University of Illinois at Urbana-Champaign.Zhao is supported in part by NSF-DMS2014053.Shao is supported in part by NSF-DMS1807032.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2023/1
Y1 - 2023/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.jeconom.2020.07.039
DO - 10.1016/j.jeconom.2020.07.039
M3 - Article
C2 - 32836681
AN - SCOPUS:85089085318
SN - 0304-4076
VL - 232
SP - 1
EP - 17
JO - Journal of Econometrics
JF - Journal of Econometrics
IS - 1
ER -