1. Regularised B-splines Projected Gaussian Process Priors to Estimate Time-trends in Age-specific COVID-19 Deaths.
- Author
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Monod, Mélodie, Blenkinsop, Alexandra, Brizzi, Andrea, Yu Chen, Perello, Carlos Cardoso Correia, Jogarah, Vidoushee, Yuanrong Wang, Flaxman, Seth, Bhatt, Samir, and Ratmann, Oliver
- Subjects
GAUSSIAN function ,COVID-19 pandemic ,KERNEL functions ,EPIDEMICS - Abstract
The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate 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 Bsplines. 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 considerably 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 mortality 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 counterfactual analyses, we quantify the avoided COVID-19 deaths under lower vaccination rates and avoidable COVID-19 deaths under higher vaccination rates. The Bsplines projected GP priors that we develop are likely an appealing addition to the arsenal of Bayesian regularising priors. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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