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

Feiyu Jiang, Zifeng Zhao, Xiaofeng Shao

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalJournal of Econometrics
DOIs
StateAccepted/In press - 2020

Keywords

  • Coronavirus
  • COVID-19
  • severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
  • Novel coronavirus
  • 2019-nCoV
  • Pandemic

ASJC Scopus subject areas

  • Economics and Econometrics

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