  Since the emergence of the SARS-CoV-2 virus, research into the existence,
extent, and pattern of seasonality has been of the highest importance for
public health preparation. This study uses a novel bandpass bootstrap approach
called the Variable Bandpass Periodic Block Bootstrap (VBPBB) to investigate
the periodically correlated (PC) components including seasonality within US
COVID-19 mortality. Bootstrapping to produce confidence intervals (CI) for
periodic characteristics such as the seasonal mean requires preservation of the
PC component's correlation structure during resampling. While existing
bootstrap methods can preserve the PC component correlation structure,
filtration of that PC component's frequency from interference is critical to
bootstrap the PC component's characteristics accurately and efficiently. The
VBPBB filters the PC time series to reduce interference from other components
such as noise. This greatly reduces bootstrapped CI size and outperforms the
statistical power and accuracy of other methods when estimating the periodic
mean sampling distribution. VBPBB analysis of US COVID-19 mortality PC
components are provided and compared against alternative bootstrapping methods.
These results reveal crucial evidence supporting the presence of a seasonal PC
pattern and existence of additional PC components, their timing, and CIs for
their effect which will aid prediction and preparation for future COVID-19
responses.
