Regularised B-splines Projected Gaussian Process Priors to Estimate Time-trends in Age-specific COVID-19 Deaths

The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to es-timate the age-specific COVID-19 attributable deaths over time in the United States. The model is specified by a novel non-parametric spatial approach over time and age, a low-rank Gaussian Process (GP) projected by regularised B -splines. We show that this projection defines a GP with attractive smoothness and computational efficiency properties, derive its kernel function, and discuss the penalty terms induced by the projected GP. Simulation analyses and benchmark results show that the B-splines projected GP may perform better than standard B-splines and Bayesian P-splines, and equivalently well as a standard GP at con-siderably lower runtimes. We apply the model to weekly, age-stratified COVID-19 attributable deaths reported by the US Centers for Disease Control, which are subject to censoring and reporting biases. Using the B-splines projected GP, we can estimate longitudinal trends in COVID-19 associated deaths across the US by 1-year age bands. These estimates are instrumental to calculate age-specific mor-tality rates, describe variation in age-specific deaths across the US, and for fitting epidemic models. Here, we couple the model with age-specific vaccination rates to show that vaccination rates were significantly associated with the magnitude of resurgences in COVID-19 deaths during the summer 2021. With counterfac-tual analyses, we quantify the avoided COVID-19 deaths under lower vaccination rates and avoidable COVID-19 deaths under higher vaccination rates. The B -splines projected GP priors that we develop are likely an appealing addition to the arsenal of Bayesian regularising priors.

Automated summary

In this study, a hierarchical Bayesian model is used to estimate age-specific COVID-19 attributable deaths over time in the United States. The model utilizes a novel non-parametric spatial approach combined with regularized B-splines to project a low-rank Gaussian Process. The results demonstrate that this projection defines a Gaussian Process with smoothness and computational efficiency properties, and shows improved performance compared to standard approaches. The model is applied to weekly, age-stratified COVID-19 attributable deaths reported by the US Centers for Disease Control, and the estimates provide valuable information for calculating age-specific mortality rates, understanding variation in age-specific deaths, and fitting epidemic models. Additionally, the model is coupled with age-specific vaccination rates to analyze their impact on COVID-19 deaths and quantify the avoided and avoidable deaths based on different vaccination scenarios. The developed B-splines projected Gaussian Process priors serve as appealing additions to Bayesian regularizing priors.