  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.
