  The COVID-19 pandemic has taken the world by storm with its high infection
rate. Investigating its geographical disparities has paramount interest in
order to gauge its relationships with political decisions, economic indicators,
or mental health. This paper focuses on clustering the daily death rates
reported in several regions of Europe and the United States over eight months.
Several methods have been developed to cluster such functional data. However,
these methods are not translation-invariant and thus cannot handle different
times of arrivals of the disease, nor can they consider external covariates and
so are unable to adjust for the population risk factors of each region. We
propose a novel three-step clustering method to circumvent these issues. As a
first step, feature extraction is performed by translation-invariant wavelet
decomposition which permits to deal with the different onsets. As a second
step, single-index regression is used to neutralize disparities caused by
population risk factors. As a third step, a nonparametric mixture is fitted on
the regression residuals to achieve the region clustering. Supplementary
materials for this article, including a standardized description of the
materials available for reproducing the work, are available online.
