Time series analysis of COVID-19 infection curve: A change-point perspective

Feiyu Jiang; Zifeng Zhao; Xiaofeng Shao

doi:10.1016/j.jeconom.2020.07.039

Time series analysis of COVID-19 infection curve: A change-point perspective

J Econom. 2023 Jan;232(1):1-17. doi: 10.1016/j.jeconom.2020.07.039. Epub 2020 Jul 30.

Authors

Feiyu Jiang¹, Zifeng Zhao², Xiaofeng Shao³

Affiliations

¹ Center for Statistical Science and Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
² Department of Information Technology, Analytics, and Operations, Mendoza College of Business, University of Notre Dame, Notre Dame, IN 46556, USA.
³ Department of Statistics, University of Illinois at Urbana Champaign, Champaign, IL 61820, USA.

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.