Seasonal and Periodic Patterns in US COVID-19 Mortality using the Variable Bandpass Periodic Block Bootstrap

TL;DR

This paper introduces the Variable Bandpass Periodic Block Bootstrap (VBPBB), a novel method for accurately detecting and analyzing seasonal and periodic patterns in US COVID-19 mortality data, improving confidence interval estimation.

Contribution

The paper presents VBPBB, a new bootstrap approach that filters PC components to enhance the detection of seasonality and periodicity in COVID-19 mortality data.

Findings

Evidence of seasonal periodic components in US COVID-19 mortality

VBPBB outperforms existing bootstrap methods in accuracy and power

Identification of additional periodic components and their timing

Abstract

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.