Regularised B-splines projected Gaussian Process priors to estimate time-trends in age-specific COVID-19 deaths

Mélodie Monod, Alexandra Blenkinsop, Andrea Brizzi, Yu Chen, Vidoushee Jogarah, Yuanrong Wang, Samir Bhatt and Oliver Ratmann (2021). Regularised B-splines projected Gaussian Process priors to estimate time-trends in age-specific COVID-19 deaths, http://arxiv.org/abs/2106.12360.

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 B-splines. We show that this projection defines a new 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. The B-splines projected GP priors that we develop are likely an appealing addition to the arsenal of Bayesian regularising priors.

About this repository

Installation

A yml file is provided and can be used to build a conda virtual environment containing all dependencies. Create the environment using:

$ conda env create -f BSplinesProjectedGPs.yml

Then activate the environment for use:

$ source activate BSplinesProjectedGPs

This release has been checked on Ubuntu version 18.04.2.

Warranty

Imperial makes no representation or warranty about the accuracy or completeness of the data nor that the results will not constitute in infringement of third-party rights. Imperial accepts no liability or responsibility for any use which may be made of any results, for the results, nor for any reliance which may be placed on any such work or results.

Cite

Please cite

• M Monod et al. Regularised B-splines projected Gaussian Process priors to estimate time-trends in age-specific COVID-19 deaths. (2021)