  The outbreak of the SARS-CoV-2 virus, which led to an unprecedented global
pandemic, has underscored the critical importance of understanding seasonal
patterns. This knowledge is fundamental for decision-making in healthcare and
public health domains. Investigating the presence, intensity, and precise
nature of seasonal trends, as well as these temporal patterns, is essential for
forecasting future occurrences, planning interventions, and making informed
decisions based on the evolution of events over time. This study employs the
Variable Bandpass Periodic Block Bootstrap (VBPBB) to separate and analyze
different periodic components by frequency in time series data, focusing on
annually correlated (PC) principal components. Bootstrapping, a method used to
estimate statistical sampling distributions through random sampling with
replacement, is particularly useful in this context. Specifically, block
bootstrapping, a model-independent resampling method suitable for time series
data, is utilized. Its extensions are aimed at preserving the correlation
structures inherent in PC processes. The VBPBB applies a bandpass filter to
isolate the relevant PC frequency, thereby minimizing contamination from
extraneous frequencies and noise. This approach significantly narrows the
confidence intervals, enhancing the precision of estimated sampling
distributions for the investigated periodic characteristics. Furthermore, we
compared the outcomes of block bootstrapping for periodically correlated time
series with VBPBB against those from more traditional bootstrapping methods.
Our analysis shows VBPBB provides strong evidence of the existence of an annual
seasonal PC pattern in hospitalization rates not detectible by other methods,
providing timing and confidence intervals for their impact.
